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Experimental investigations are conducted to quantify the influence of specimen thickness and orientation on the mechanical properties of selective laser melted stainless steel 316 L. The results indicate that the mechanical strength and ductility increase with increasing specimen thickness until a saturation value is reached from a specimen thickness of about 2 mm. Specimen orientation dependency is pronounced for thin specimens (<1.5 mm), whereas only small deviations in strength are observed for thicker specimens with orientations of 30°, 45° and 90° to build direction. The mechanical properties of the specimen orientation of 0° to build direction shows great deviation to the other orientations and the smallest overall strength. A reliable design of selective laser melted components should account for specimen thickness and orientation, e. g. by a correction factor. Furthermore, it is recommended to avoid loads vertical (90°) and parallel (0°) to build direction to guarantee higher ductility and strength.
The performance of a photoacoustic trace gas sensor for the detection of acetone in N2 and synthetic air is reported. The sensor system utilises an amplitude modulated UV LED. The light source has an emission maximum at 278 nm and a maximum CW output power of 300 mW according to the datasheet. Three different collimating and focusing approaches have been investigated to guide the highly divergent LED light into the acoustic resonator of the photoacoustic measurement cell. A 3D printed aluminium cell was designed to optimize light coupling by simultaneously minimizing the photoacoustic background signal generation. Hence, the diameter of the resonator was set to a comparable large diameter of 10 mm and the inner walls of the resonator were mirror polished. The additive manufacturing procedure allowed for integration of a spirally formed gas channel, enabling gas heating prior to detection. The sensor performance was investigated by measuring acetone in N2 and synthetic air at different concentrations. The UV LED current was set to 86 % of the maximum value according to the datasheet of the light source in order to increase the lifetime and thermal stability. An Allan-Werle deviation analysis validates a stable sensor performance. The limit of detection (LoD) was determined at a 3σ noise level with a 10 s lock-in amplifier time constant by sampling data points over 20 s with a data acquisition rate of 5 Hz. LoDs of 80.8 ppbV and 19.6 ppbV were obtained for acetone in N2 and synthetic air, respectively.
Nach einer Fraktur ist Mobilisierung Behandlungsziel und Therapiesäule. Das Festlegen von Outcomes basiert jedoch auf vielen Unsicherheiten, da Assessments nicht für alle Patient/-innen geeignet sind. Sie können agesabhängig beeinflusst und subjektiv geprägt sein. Sensorbasiertes Bewegungsmonitoring bietet eine Ergänzung zur Operationalisierung der Gehfähigkeit. Für Längsschnittuntersuchungen, die auch im häuslichen Umfeld durchgeführt werden, eignet sich die tägliche Schrittzahl als Variable. Sie kann durch einen handelsüblichen Fitnesstracker beobachtet
werden.
We present a sensitive UV LED photoacoustic setup for the detection of gaseous acetone and discuss its applicability towards breath analysis. We investigated the performance of the sensor for low acetone concentrations down to 0.1 parts per million (ppmV). The influences of temperature, flow, pressure, optical power and LED duty cycle on the measured signal have been examined. To gain a better understanding of the different effects on the photoacoustic signal, correlation analysis was applied and feature importance was determined using a large measured dataset. Furthermore, the cross-sensitivities towards O2, CO2 and H2O have been studied extensively. Finally, the sensor’s performance to detect acetone between 0.1–1 ppmV within gas mixtures simulating breath exhale conditions has been investigated, too. With a limit of detection (LoD) of 12.5 parts per billion (ppbV) (3σ) measured under typical breath exhale gas mixture conditions, the sensor demonstrated a high potential for the application of acetone detection in human breath analysis.
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.
Purpose
Age-related macular degeneration (AMD) is a common threat to vision. While classification of disease stages is critical to understanding disease risk and progression, several systems based on color fundus photographs are known. Most of these require in-depth and time-consuming analysis of fundus images. Herein, we present an automated computer-based classification algorithm.
Design Algorithm development for AMD classification based on a large collection of color fundus images. Validation is performed on a cross-sectional, population-based study.
Participants.
We included 120 656 manually graded color fundus images from 3654 Age-Related Eye Disease Study (AREDS) participants. AREDS participants were >55 years of age, and non-AMD sight-threatening diseases were excluded at recruitment. In addition, performance of our algorithm was evaluated in 5555 fundus images from the population-based Kooperative Gesundheitsforschung in der Region Augsburg (KORA; Cooperative Health Research in the Region of Augsburg) study.
Methods.
We defined 13 classes (9 AREDS steps, 3 late AMD stages, and 1 for ungradable images) and trained several convolution deep learning architectures. An ensemble of network architectures improved prediction accuracy. An independent dataset was used to evaluate the performance of our algorithm in a population-based study.
Main Outcome Measures.
κ Statistics and accuracy to evaluate the concordance between predicted and expert human grader classification.
Results.
A network ensemble of 6 different neural net architectures predicted the 13 classes in the AREDS test set with a quadratic weighted κ of 92% (95% confidence interval, 89%–92%) and an overall accuracy of 63.3%. In the independent KORA dataset, images wrongly classified as AMD were mainly the result of a macular reflex observed in young individuals. By restricting the KORA analysis to individuals >55 years of age and prior exclusion of other retinopathies, the weighted and unweighted κ increased to 50% and 63%, respectively. Importantly, the algorithm detected 84.2% of all fundus images with definite signs of early or late AMD. Overall, 94.3% of healthy fundus images were classified correctly.
Conclusions
Our deep learning algoritm revealed a weighted κ outperforming human graders in the AREDS study and is suitable to classify AMD fundus images in other datasets using individuals >55 years of age.
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.
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.