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Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.
Aims:
The delineation of outer margins of early Barrett's cancer can be challenging even for experienced endoscopists. Artificial intelligence (AI) could assist endoscopists faced with this task. As of date, there is very limited experience in this domain. In this study, we demonstrate the measure of overlap (Dice coefficient = D) between highly experienced Barrett endoscopists and an AI system in the delineation of cancer margins (segmentation task).
Methods:
An AI system with a deep convolutional neural network (CNN) was trained and tested on high-definition endoscopic images of early Barrett's cancer (n = 33) and normal Barrett's mucosa (n = 41). The reference standard for the segmentation task were the manual delineations of tumor margins by three highly experienced Barrett endoscopists. Training of the AI system included patch generation, patch augmentation and adjustment of the CNN weights. Then, the segmentation results from patch classification and thresholding of the class probabilities. Segmentation results were evaluated using the Dice coefficient (D).
Results:
The Dice coefficient (D) which can range between 0 (no overlap) and 1 (complete overlap) was computed only for images correctly classified by the AI-system as cancerous. At a threshold of t = 0.5, a mean value of D = 0.72 was computed.
Conclusions:
AI with CNN performed reasonably well in the segmentation of the tumor region in Barrett's cancer, at least when compared with expert Barrett's endoscopists. AI holds a lot of promise as a tool for better visualization of tumor margins but may need further improvement and enhancement especially in real-time settings.
Based on previous work by our group with manual annotation of visible Barrett oesophagus (BE) cancer images, a real-time deep learning artificial intelligence (AI) system was developed. While an expert endoscopist conducts the endoscopic assessment of BE, our AI system captures random images from the real-time camera livestream and provides a global prediction (classification), as well as a dense prediction (segmentation) differentiating accurately between normal BE and early oesophageal adenocarcinoma (EAC). The AI system showed an accuracy of 89.9% on 14 cases with neoplastic BE.
The growing number of publications on the application of artificial intelligence (AI) in medicine underlines the enormous importance and potential of this emerging field of research.
In gastrointestinal endoscopy, AI has been applied to all segments of the gastrointestinal tract most importantly in the detection and characterization of colorectal polyps. However, AI research has been published also in the stomach and esophagus for both neoplastic and non-neoplastic disorders.
The various technical as well as medical aspects of AI, however, remain confusing especially for non-expert physicians.
This physician-engineer co-authored review explains the basic technical aspects of AI and provides a comprehensive overview of recent publications on AI in gastrointestinal endoscopy. Finally, a basic insight is offered into understanding publications on AI in gastrointestinal endoscopy.
Background: For surgical fixation of bone fractures of the human hand, so-called Kirschner-wires (K-wires) are drilled through bone fragments. Due to the minimally invasive drilling procedures without a view of risk structures like vessels and nerves, a thorough training of young surgeons is necessary. For the development of a virtual reality (VR) based training system, a three-dimensional (3D) printed phantom hand is required. To ensure an intuitive operation, this phantom hand has to be realistic in both, its position relative to the driller as well as in its haptic features. The softest 3D printing material available on the market, however, is too hard to imitate human soft tissue. Therefore, a support-material (SUP) filled metamaterial is used to soften the raw material. Realistic haptic features are important to palpate protrusions of the bone to determine the drilling starting point and angle. An optical real-time tracking is used to transfer position and rotation to the training system.
Methods: A metamaterial already developed in previous work is further improved by use of a new unit cell. Thus, the amount of SUP within the volume can be increased and the tissue is softened further. In addition, the human anatomy is transferred to the entire hand model. A subcutaneous fat layer and penetration of air through pores into the volume simulate shiftability of skin layers. For optical tracking, a rotationally symmetrical marker attached to the phantom hand with corresponding reference marker is developed. In order to ensure trouble-free position transmission, various types of marker point applications are tested.
Results: Several cuboid and forearm sample prints lead to a final 30 centimeter long hand model. The whole haptic phantom could be printed faultless within about 17 hours. The metamaterial consisting of the new unit cell results in an increased SUP share of 4.32%. Validated by an expert surgeon study, this allows in combination with a displacement of the uppermost skin layer a good palpability of the bones. Tracking of the hand marker in dodecahedron design works trouble-free in conjunction with a reference marker attached to the worktop of the training system.
Conclusions: In this work, an optically tracked and haptically correct phantom hand was developed using dual-material 3D printing, which can be easily integrated into a surgical training system.
A key requirement in both native knee joints and total knee arthroplasty is a stable capsular ligament complex. However, knee stability is highly individual and ranges from clinically loose to tight. So far, hardly any in vivo data on the intrinsic mechanical of the knee are available. This study investigated if stiffness of the native ligament complex may be determined in vivo using a standard knee balancer. Measurements were obtained with a commercially available knee balancer, which was initially calibrated in vitro. 5 patients underwent reconstruction of the force-displacement curves of the ligament complex. Stiffness of the medial and lateral compartments were calculated to measure the stability of the capsular ligament complex. All force-displacement curves consisted of a non-linear section at the beginning and of a linear section from about 80 N onwards. The medial compartment showed values of 28.4 ± 1.2 N/mm for minimum stiffness and of 39.9 ± 1.1 N/mm for maximum stiffness; the respective values for the lateral compartment were 19.9 ± 0.9 N/mm and 46.6 ± 0.8 N/mm. A commercially available knee balancer may be calibrated for measuring stiffness of knee ligament complex in vivo, which may contribute to a better understanding of the intrinsic mechanical behaviour of knee joints.
Musculoskeletal simulations of lumbar spine loading rely on a geometrical representation of the anatomy. However, this data has an inherent inaccuracy. This study evaluates the influence of defined geometrical parameters on lumbar spine loading utilising five parametrised musculoskeletal lumbar spine models for four different postures. The influence of the dimensions of vertebral body, disc, posterior parts of the vertebrae as well as the curvature of the lumbar spine was studied. Additionally, simulations with combinations of selected parameters were conducted. Changes in L4/L5 resultant joint force were used as outcome variable. Variations of the vertebral body height, disc height, transverse process width and the curvature of the lumbar spine were the most influential.
These parameters can be easily acquired from X-rays and should be used to morph a musculoskeletal lumbar spine model for subject-specific approaches with respect to bone geometry. Furthermore, the model was very sensitive to uncommon configurations and therefore, it is advised that stiffness properties of discs and ligaments should be individualised.
One of the main goals in reconstructing rotator cuff tears is the restoration of glenohumeral joint stability, which is subsequently of utmost importance in order to prevent degenerative damage such as superior labral anterior posterior (SLAP) lesion, arthrosis, and malfunction. The goal of the current study was to facilitate musculoskeletal models in order to estimate glenohumeral instability introduced by muscle weakness due to cuff lesions. Inverse dynamics simulations were used to compute joint reaction forces for several static abduction tasks with different muscle weakness. Results were compared with the existing literature in order to ensure the model validity. Further arm positions taken from activities of daily living, requiring the rotator cuff muscles were modeled and their contribution to joint kinetics computed. Weakness of the superior rotator cuff muscles (supraspinatus; infraspinatus) leads to a deviation of the joint reaction force to the cranial dorsal rim of the glenoid. Massive rotator cuff defects showed higher potential for glenohumeral instability in contrast to single muscle ruptures. The teres minor muscle seems to substitute lost joint torque during several simulated muscle tears to maintain joint stability. Joint instability increases with cuff tear size. Weakness of the upper part of the rotator cuff leads to a joint reaction force closer to the upper glenoid rim. This indicates the comorbidity of cuff tears with SLAP lesions. The teres minor is crucial for maintaining joint stability in case of massive cuff defects and should be uprated in clinical decision-making.
Background
There is a wide range of mechanical properties of spinal ligaments documented in literature. Due to the fact that ligaments contribute in stabilizing the spine by limiting excessive intersegmental motion, those properties are of particular interest for the implementation in musculoskeletal models. The aim of this study was to investigate the effect of varying ligament stiffness on the kinematic behaviour of the lumbar spine.
Methods
A musculoskeletal model with a detailed lumbar spine was modified according to fluoroscopic recordings and corresponding data files of three different subjects. For flexion, inverse dynamics analysis with a variation of the ligament stiffness matrix were conducted. The influence of several degrees of ligament stiffness on the lumbar spine model were investigated by tracking ligament forces, disc forces and resulting moments generated by the ligaments. Additionally, the kinematics of the motion segments were evaluated.
Results
An increase of ligament stiffness resulted in an increase of ligament and disc forces, whereas the relative change of disc force increased at a higher rate at the L4/L5 level (19 %) than at the L3/L4 (10 %) level in a fully flexed posture. The same behaviour applied to measured moments with 67 % and 45 %. As a consequence, the motion deflected to the lower levels of the lumbar spine and the lower discs had to resist an increase in loading.
Conclusions
Higher values of ligament stiffness over all lumbar levels could lead to a shift of the loading and the motion between segments to the lower lumbar levels. This could lead to an increased risk for the lower lumbar parts.
Background
Iliac crest bone harvesting is a frequently performed surgical procedure widely used to treat bone defects. The objective of this study is to assess the biomechanical quantities related to risk for pelvic fracture after harvesting an autologous bone graft at the anterior iliac crest.
Methods
Finite element models with a simulated harvest site (sized 15 × 20 mm, 15 × 35 mm, 30 × 20 mm and 30 × 35 mm) in the iliac wing are created. The relevant loading case is when the ipsilateral leg is lifted off the ground. Musculoskeletal analysis is utilized to compute the muscle and joint forces involved in this motion. These forces are used as boundary conditions for the finite element analyses. Bone tissue stress is analyzed.
Results
Critical stress peaks are located between the anterior superior iliac spine (ASIS) and the anterior edge of the harvest site. Irrespective of the graft size, the iliac wing does not show any significant stress peaks with the harvest site being 20 to 25 mm posterior to the ASIS. The harvest area itself inhibits the distribution of the forces applied on the ASIS to extend to the posterior iliac wing. This leads to a lack of stress posterior to the harvest site. A balanced stress distribution with no stress peaks appears when the bone graft is taken below the iliac crest.
Conclusion
A harvest site located at least 20 to 25 mm posterior to the ASIS should be preferred to minimize the risk of iliac fatigue fracture.
We aimed to investigate the relationship between postoperative leg length/offset (LL/OS) reconstruction and gait performance after total hip arthroplasty (THA). In the course of a prospective randomized controlled trial, 60 patients with unilateral hip arthrosis received cementless THA through a minimally-invasive anterolateral surgical approach. One year post-operatively, LL and global OS restoration were analyzed and compared to the contralateral hip on AP pelvic radiographs. The combined postoperative limb length/OS reconstruction of the operated hip was categorized as restored (within 5 mm) or non-restored (more than 5 mm reduction or more than 5 mm increment). The acetabular component inclination, anteversion and femoral component anteversion were evaluated using CT scans of the pelvis and the femur. 3D gait analysis of the lower extremity and patient related outcome measures (HHS, HOOS, EQ-5D) were obtained pre-operatively, six months and twelve months post-operatively by an observer blinded to radiographic results. Component position of cup and stem was comparable between the restored and non-restored group. Combined LL and OS restoration within 5 mm resulted in higher Froude number (p < 0.001), normalized walking speed (p < 0.001) and hip range-of-motion (ROM) (p = 0.004) during gait twelve months postoperatively, whereas gait symmetry was comparable regardless of LL and OS reconstruction at both examinations. Clinical scores did not show any relevant association between the accuracy of LL or OS reconstruction and gait six/twelve months after THA. In summary, postoperative LL/OS discrepancies larger than 5 mm relate to unphysiological gait kinematics within the first year after THA. DRKS00000739, German Clinical Trials Register.
A prospective randomized controlled trial is presented that is used to compare gait performance between the computer assisted Femur First (CAS FF) operation method and conventional THR (CON). 60 patients underwent a 3D gait analysis of the lower extremity at pre-operative, 6 months post-operative and twelve months post-operative. Detailed verification experiments were facilitated to ensure the quality of data as well as to avoid over-interpreting of the data. The results confirm a similar data-quality as reported in the literature. Walking speed, range of motion and symmetry thereof improved over the follow-up period, without significant differences between the groups. While all parameters do significantly increase over the follow-up period for both groups, there were no significant differences between them at any given time-point. Patients undergoing CAS FF showed a trend to improved hip flexion angle indicating a possible long-term benefit.
Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) has been developed and established as an emerging technique in the generation of quantitative images of metal distributions in thin tissue sections of brain samples (such as human, rat and mouse brain), with applications in research related to neurodegenerative disorders. A new analytical protocol is described which includes sample preparation by cryo-cutting of thin tissue sections and matrix-matched laboratory standards, mass spectrometric measurements, data acquisition, and quantitative analysis. Specific examples of the bioimaging of metal distributions in normal rodent brains are provided. Differences to the normal were assessed in a Parkinson’s disease and a stroke brain model. Furthermore, changes during normal aging were studied. Powerful analytical techniques are also required for the determination and characterization of metal-containing proteins within a large pool of proteins, e.g., after denaturing or non-denaturing electrophoretic separation of proteins in one-dimensional and two-dimensional gels. LA-ICP-MS can be employed to detect metalloproteins in protein bands or spots separated after gel electrophoresis. MALDI-MS can then be used to identify specific metal-containing proteins in these bands or spots. The combination of these techniques is described in the second section.
Polarized light imaging (PLI) enables the evaluation of fiber orientations in histological sections of human postmortem brains, with ultra-high spatial resolution. PLI is based on the birefringent properties of the myelin sheath of nerve fibers. As a result, the polarization state of light propagating through a rotating polarimeter is changed in such a way that the detected signal at each measurement unit of a charged-coupled device (CCD) camera describes a sinusoidal signal. Vectors of the fiber orientation defined by inclination and direction angles can then directly be derived from the optical signals employing PLI analysis. However, noise, light scatter and filter inhomogeneities interfere with the original sinusoidal PLI signals. We here introduce a novel method using independent component analysis (ICA) to decompose the PLI images into statistically independent component maps. After decomposition, gray and white matter structures can clearly be distinguished from noise and other artifacts. The signal enhancement after artifact rejection is quantitatively evaluated in 134 histological whole brain sections. Thus, the primary sinusoidal signals from polarized light imaging can be effectively restored after noise and artifact rejection utilizing ICA. Our method therefore contributes to the analysis of nerve fiber orientation in the human brain within a micrometer scale.
Polarised light imaging (PLI) utilises the birefringence of the myelin sheaths in order to visualise the orientation of nerve fibres in microtome sections of adult human post-mortem brains at ultra-high spatial resolution. The preparation of post-mortem brains for PLI involves fixation, freezing and cutting into 100-μm-thick sections. Hence, geometrical distortions of histological sections are inevitable and have to be removed for 3D reconstruction and subsequent fibre tracking. We here present a processing pipeline for 3D reconstruction of these sections using PLI derived multimodal images of post-mortem brains. Blockface images of the brains were obtained during cutting; they serve as reference data for alignment and elimination of distortion artefacts. In addition to the spatial image transformation, fibre orientation vectors were reoriented using the transformation fields, which consider both affine and subsequent non-linear registration. The application of this registration and reorientation approach results in a smooth fibre vector field, which reflects brain morphology. PLI combined with 3D reconstruction and fibre tracking is a powerful tool for human brain mapping. It can also serve as an independent method for evaluating in vivo fibre tractography.
Signal transmission between different brain regions requires connecting fiber tracts, the structural basis of the human connectome. In contrast to animal brains, where a multitude of tract tracing methods can be used, magnetic resonance (MR)-based diffusion imaging is presently the only promising approach to study fiber tracts between specific human brain regions. However, this procedure has various inherent restrictions caused by its relatively low spatial resolution. Here, we introduce 3D-polarized light imaging (3D-PLI) to map the three-dimensional course of fiber tracts in the human brain with a resolution at a submillimeter scale based on a voxel size of 100 μm isotropic or less. 3D-PLI demonstrates nerve fibers by utilizing their intrinsic birefringence of myelin sheaths surrounding axons. This optical method enables the demonstration of 3D fiber orientations in serial microtome sections of entire human brains. Examples for the feasibility of this novel approach are given here. 3D-PLI enables the study of brain regions of intense fiber crossing in unprecedented detail, and provides an independent evaluation of fiber tracts derived from diffusion imaging data.
Mass spectrometric imaging (MSI) is a young innovative analytical technique and combines different fields of advanced mass spectrometry and biomedical research with the aim to provide maps of elements and molecules, complexes or fragments. Especially essential metals such as zinc, copper, iron and manganese play a functional role in signaling, metabolism and homeostasis of the cell. Due to the high degree of spatial organization of metals in biological systems their distribution analysis is of key interest in life sciences. We have developed analytical techniques termed BrainMet using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) imaging to measure the distribution of trace metals in biological tissues for biomedical research and feasibility studies—including bioaccumulation and bioavailability studies, ecological risk assessment and toxicity studies in humans and other organisms. The analytical BrainMet techniques provide quantitative images of metal distributions in brain tissue slices which can be combined with other imaging modalities such as photomicrography of native or processed tissue (histochemistry, immunostaining) and autoradiography or with in vivo techniques such as positron emission tomography or magnetic resonance tomography.
Prospective and instrumental developments will be discussed concerning the development of the metalloprotein microscopy using a laser microdissection (LMD) apparatus for specific sample introduction into an inductively coupled plasma mass spectrometer (LMD-ICP-MS) or an application of the near field effect in LA-ICP-MS (NF-LA-ICP-MS). These nano-scale mass spectrometric techniques provide improved spatial resolution down to the single cell level.
Metals are involved in many processes of life. They are needed for enzymatic reactions, are involved in healthy processes but also yield diseases if the metal homeostasis is disordered. Therefore, the interest to assess the spatial distribution of metals is rising in biomedical science. Imaging metal (and non-metal) isotopes by laser ablation mass spectrometry with inductively coupled plasma (LA-ICP-MS) requires a special software solution to process raw data obtained by scanning a sample line-by-line. As no software ready to use was available we developed an interactive software tool for Image Generation and Analysis (IMAGENA). Unless optimised for LA-ICP-MS, IMAGENA can handle other raw data as well. The general purpose was to reconstruct images from a continuous list of raw data points, to visualise these images, and to convert them into a commonly readable image file format that can be further analysed by standard image analysis software. The generation of the image starts with loading a text file that holds a data column of every measured isotope. Specifying general spatial domain settings like the data offset and the image dimensions is done by the user getting a direct feedback by means of a preview image. IMAGENA provides tools for calibration and to correct for a signal drift in the y-direction. Images are visualised in greyscale as well a pseudo-colours with possibilities for contrast enhancement. Image analysis is performed in terms of smoothed line plots in row and column direction.
Biological applications like vesicle membrane analysis involve the precise segmentation of 3D structures in noisy volumetric data, obtained by techniques like magnetic resonance imaging (MRI) or laser scanning microscopy (LSM). Dealing with such data is a challenging task and requires robust and accurate segmentation methods. In this article, we propose a novel energy model for 3D segmentation fusing various cues like regional intensity subdivision, edge alignment and orientation information. The uniqueness of the approach consists in the definition of a new anisotropic regularizer, which accounts for the unbalanced slicing of the measured volume data, and the generalization of an efficient numerical scheme for solving the arising minimization problem, based on linearization and fixed-point iteration. We show how the proposed energy model can be optimized globally by making use of recent continuous convex relaxation techniques. The accuracy and robustness of the presented approach are demonstrated by evaluating it on multiple real data sets and comparing it to alternative segmentation methods based on level sets. Although the proposed model is designed with focus on the particular application at hand, it is general enough to be applied to a variety of different segmentation tasks.
Medical image processing provides core innovation for medical imaging. This paper is focused on recent developments from science to applications analyzing the past fifteen years of history of the proceedings of the German annual meeting on medical image processing (BVM). Furthermore, some members of the program committee present their personal points of views: (i) multi-modality for imaging and diagnosis, (ii) analysis of diffusion-weighted imaging, (iii) model-based image analysis, (iv) registration of section images, (v) from images to information in digital endoscopy, and (vi) virtual reality and robotics. Medical imaging and medical image computing is seen as field of rapid development with clear trends to integrated applications in diagnostics, treatment planning and treatment.
The development of adenocarcinoma in Barrett’s esophagus is difficult to detect by endoscopic surveillance of patients with signs of dysplasia. Computer assisted diagnosis of endoscopic images (CAD) could therefore be most helpful in the demarcation and classification of neoplastic lesions. In this study we tested the feasibility of a CAD method based on Speeded up Robust Feature Detection (SURF). A given database containing 100 images from 39 patients served as benchmark for feature based classification models. Half of the images had previously been diagnosed by five clinical experts as being ”cancerous”, the other half as ”non-cancerous”. Cancerous image regions had been visibly delineated (masked) by the clinicians. SURF features acquired from full images as well as from masked areas were utilized for the supervised training and testing of an SVM classifier. The predictive accuracy of the developed CAD system is illustrated by sensitivity and specificity values. The results based on full image matching where 0.78 (sensitivity) and 0.82 (specificity) were achieved, while the masked region approach generated results of 0.90 and 0.95, respectively.
The Impact of Semi-Automated Segmentation and 3D Analysis on Testing New Osteosynthesis Material
(2017)
A new protocol for testing osteosynthesis material postoperatively combining semi-automated segmentation and 3D analysis of surface meshes is proposed. By various steps of transformation and measuring, objective data can be collected. In this study the specifications of a locking plate used for mediocarpal arthrodesis of the wrist were examined. The results show, that union of the lunate, triquetrum, hamate and capitate was achieved and that the plate is comparable to coexisting arthrodesis systems. Additionally, it was shown, that the complications detected correlate to the clinical outcome. In synopsis, this protocol is considered beneficial and should be taken into account in further studies.
Structural MRI brain analysis and segmentation is a crucial part in the daily routine in neurosurgery for intervention planning. Exemplarily, the free software FSL-FAST (FMRIB’s Segmentation Library – FMRIB’s Automated Segmentation Tool) in version 4 is used for segmentation of brain tissue types. To speed up the segmentation procedure by parallel execution, we transferred FSL-FAST to a General Purpose Graphics Processing Unit (GPGPU) using Open Computing Language (OpenCL) [1]. The necessary steps for parallelization resulted in substantially different and less useful results. Therefore, the underlying methods were revised and adapted yielding computational overhead. Nevertheless, we achieved a speed-up factor of 3.59 from CPU to GPGPU execution, as well providing similar useful or even better results.
We propose an automatic approach for early detection of adenocarcinoma in the esophagus. High-definition endoscopic images (50 cancer, 50 Barrett) are partitioned into a dataset containing approximately equal amounts of patches showing cancerous and non-cancerous regions. A deep convolutional neural network is adapted to the data using a transfer learning approach. The final classification of an image is determined by at least one patch, for which the probability being a cancer patch exceeds a given threshold. The model was evaluated with leave one patient out cross-validation. With sensitivity and specificity of 0.94 and 0.88, respectively, our findings improve recently published results on the same image data base considerably. Furthermore, the visualization of the class probabilities of each individual patch indicates, that our approach might be extensible to the segmentation domain.
Backround
Scaphoidectomy and midcarpal fusion can be performed using traditional fixation methods like K-wires, staples, screws or different dorsal (non)locking arthrodesis systems. The aim of this study is to test the Aptus four corner locking plate and to compare the clinical findings to the data revealed by CT scans and semi-automated segmentation.
Methods:
This is a retrospective review of eleven patients suffering from scapholunate advanced collapse (SLAC) or scaphoid non-union advanced collapse (SNAC) wrist, who received a four corner fusion between August 2011 and July 2014. The clinical evaluation consisted of measuring the range of motion (ROM), strength and pain on a visual analogue scale (VAS). Additionally, the Disabilities of the Arm, Shoulder and Hand (QuickDASH) and the Mayo Wrist Score were assessed. A computerized tomography (CT) of the wrist was obtained six weeks postoperatively. After semi-automated segmentation of the CT scans, the models were post processed and surveyed.
Results
During the six-month follow-up mean range of motion (ROM) of the operated wrist was 60°, consisting of 30° extension and 30° flexion. While pain levels decreased significantly, 54% of grip strength and 89% of pinch strength were preserved compared to the contralateral healthy wrist. Union could be detected in all CT scans of the wrist. While X-ray pictures obtained postoperatively revealed no pathology, two user related technical complications were found through the 3D analysis, which correlated to the clinical outcome.
Conclusion
Due to semi-automated segmentation and 3D analysis it has been proved that the plate design can keep up to the manufacturers’ promises. Over all, this case series confirmed that the plate can compete with the coexisting techniques concerning clinical outcome, union and complication rate.
In order to fix a fracture using minimally invasive surgery approaches, surgeons are drilling complex and tiny bones with a 2 dimensional X-ray as single imaging modality in the operating room. Our novel haptic force-feedback and visual assisted training system will potentially help hand surgeons to learn the drilling procedure in a realistic visual environment. Within the simulation, the collision detection as well as the interaction between virtual drill, bone voxels and surfaces are important. In this work, the chai3d collision detection and force calculation algorithms are combined with a physics engine to simulate the bone drilling process. The chosen Bullet-Physics-Engine provides a stable simulation of rigid bodies, if the collision model of the drill and the tool holder is generated as a compound shape. Three haptic points are added to the K-wire tip for removing single voxels from the bone. For the drilling process three modes are proposed to emulate the different phases of drilling in restricting the movement of a haptic device.
This work presents a systematic review concerning recent studies and technologies of machine learning for Barrett's esophagus (BE) diagnosis and treatment. The use of artificial intelligence is a brand new and promising way to evaluate such disease. We compile some works published at some well-established databases, such as Science Direct, IEEEXplore, PubMed, Plos One, Multidisciplinary Digital Publishing Institute (MDPI), Association for Computing Machinery (ACM), Springer, and Hindawi Publishing Corporation. Each selected work has been analyzed to present its objective, methodology, and results. The BE progression to dysplasia or adenocarcinoma shows a complex pattern to be detected during endoscopic surveillance. Therefore, it is valuable to assist its diagnosis and automatic identification using computer analysis. The evaluation of the BE dysplasia can be performed through manual or automated segmentation through machine learning techniques. Finally, in this survey, we reviewed recent studies focused on the automatic detection of the neoplastic region for classification purposes using machine learning methods.
The number of patients with Barret’s esophagus (BE) has increased in the last decades. Considering the dangerousness of the disease and its evolution to adenocarcinoma, an early diagnosis of BE may provide a high probability of cancer remission. However, limitations regarding traditional methods of detection and management of BE demand alternative solutions. As such, computer-aided tools have been recently used to assist in this problem, but the challenge still persists. To manage the problem, we introduce the infinity Restricted Boltzmann Machines (iRBMs) to the task of automatic identification of Barrett’s esophagus from endoscopic images of the lower esophagus. Moreover, since iRBM requires a proper selection of its meta-parameters, we also present a discriminative iRBM fine-tuning using six meta-heuristic optimization techniques. We showed that iRBMs are suitable for the context since it provides competitive results, as well as the meta-heuristic techniques showed to be appropriate for such task.
Background: Currently, it is common practice to use three-dimensional (3D) printers not only for rapid prototyping in the industry, but also in the medical area to create medical applications for training inexperienced surgeons. In a clinical training simulator for minimally invasive bone drilling to fix hand fractures with Kirschner-wires (K-wires), a 3D-printed hand phantom must not only be geometrically but also haptically correct. Due to a limited view during an operation, surgeons need to perfectly localize underlying risk structures only by feeling of specific bony protrusions of the human hand.
Methods: The goal of this experiment is to imitate human soft tissue with its haptic and elasticity for a realistic hand phantom fabrication, using only a dual-material 3D printer and support-material-filled metamaterial between skin and bone. We present our workflow to generate lattice structures between hard bone and soft skin with iterative cube edge (CE) or cube face (CF) unit cells. Cuboid and finger shaped sample prints with and without inner hard bone in different lattice thickness are constructed and 3D printed.
Results: The most elastic available rubber-like material is too firm to imitate soft tissue. By reducing the amount of rubber in the inner volume through support material (SUP), objects become significantly softer. Without metamaterial, after disintegration, the SUP can be shifted through the volume and thus the body loses its original shape. Although the CE design increases the elasticity, it cannot restore the fabric form. In contrast to CE, the CF design increases not only the elasticity but also guarantees a local limitation of the SUP. Therefore, the body retains its shape and internal bones remain in its intended place. Various unit cell sizes, lattice thickening and skin thickness regulate the rubber material and SUP ratio. Test prints with higher SUP and lower rubber material percentage appear softer and vice versa. This was confirmed by an expert surgeon evaluation. Subjects adjudged pure rubber-like material as too firm and samples only filled with SUP or lattice structure in CE design as not suitable for imitating tissue. 3D-printed finger samples in CF design were rated as realistic compared to the haptic of human tissue with a good palpable bone structure.
Conclusions: We developed a new dual-material 3D print technique to imitate soft tissue of the human hand with its haptic properties. Blowy SUP is trapped within a lattice structure to soften rubber-like 3D print material, which makes it possible to reproduce a realistic replica of human hand soft tissue.
One common method to fix fractures of the human hand after an accident is an osteosynthesis with Kirschner wires (K-wires) to stabilize the bone fragments. The insertion of K-wires is a delicate minimally invasive surgery, because surgeons operate almost without a sight. Since realistic training methods are time consuming, costly and insufficient, a virtual-reality (VR) based training system for the placement of K-wires was developed. As part of this, the current work deals with the real-time bone drilling simulation using a haptic force-feedback device.
To simulate the drilling, we introduce a virtual fixture based force-feedback drilling approach. By decomposition of the drilling task into individual phases, each phase can be handled individually to perfectly control the drilling procedure. We report about the related finite state machine (FSM), describe the haptic feedback of each state and explain, how to avoid jerking of the haptic force-feedback during state transition.
The usage of the virtual fixture approach results in a good haptic performance and a stable drilling behavior. This was confirmed by 26 expert surgeons, who evaluated the virtual drilling on the simulator and rated it as very realistic. To make the system even more convincing, we determined real drilling feed rates through experimental pig bone drilling and transferred them to our system. Due to a constant simulation thread we can guarantee a precise drilling motion.
Virtual fixtures based force-feedback calculation is able to simulate force-feedback assisted bone drilling with high quality and, thus, will have a great potential in developing medical applications.
International internships
(2018)
Students are increasingly considering expanding their cooperative experiences into the global environment. This paper investigates the experiences of students undertaking their internship capstone in other countries under the supervision of a partner institute and a participating industry sponsor. These experiences of German students interning in New Zealand (NZ), and NZ students interning in Germany offer an insight into the organisation required by the partner institutions and the comparative cultural and technical differences that these students have encountered. The international agreement between Ostbayerische Technische Hochschule (OTH) Regensburg, Germany, and the Eastern Institute of Technology (EIT), NZ, has already built a mutual framework of visiting lecturers teaching in each other's institutes, and a programme of hosting exchange students in each other's institutes. A natural progression of this partnership has now moved into the active recruitment and brokering of international internship opportunities for their respective final year Business and Information Technology (IT) students. The benefits are evident personally to the students, which are discussed in this paper, but there are also clear benefits for EIT and OTH in terms of marketing opportunities for prospective students and enriching the learning environments. This paper explores the challenges faced by the Schools of Business and Computing at EIT as it pioneers the first two years of establishing internship options for final year German exchange students within NZ, and matching final year EIT degree students to industry sponsors within Bavaria, a federal state of Germany. A qualitative approach was taken for this study with data acquired from internship students involved, the key organising lecturers from each institute, and industry sponsors from each country. A number of student, staff and industry representative interviews were conducted along with a cultural appraisal of relevant literature in the global education field. A further overview of the macro benefits for the partner institutes in terms of marketing, and programme enrichment is also part of the conclusion. The paper may be useful as a template for other institutes on developing international internship opportunities through global partners.
The paper at hand describes how a regional university of applied sciences tried to internationalize its learning and teaching environment. It describes the challenges encountered, the managerial approach taken, illustrates the implemented initiatives, and how effective they turned out. The results might be relevant to faculty staff in managerial positions at regional universities all over the world that face the challenge to internationalize their teaching and learning environment for the benefit of their domestic students.
Computer-aided diagnosis using deep learning in the evaluation of early oesophageal adenocarcinoma
(2019)
Computer-aided diagnosis using deep learning (CAD-DL) may be an instrument to improve endoscopic assessment of Barrett’s oesophagus
(BE) and early oesophageal adenocarcinoma (EAC). Based on still images from two databases, the diagnosis of EAC by CAD-DL reached sensitivities/specificities of 97%/88% (Augsburg data) and 92%/100% (Medical Image Computing and Computer-Assisted Intervention [MICCAI]
data) for white light (WL) images and 94%/80% for narrow band images (NBI) (Augsburg data), respectively. Tumour margins delineated by
experts into images were detected satisfactorily with a Dice coefficient (D) of 0.72. This could be a first step towards CAD-DL for BE assessment. If developed further, it could become a useful
adjunctive tool for patient management.