The 10 most recently published documents
The strategic adoption of Artificial Intelligence (AI) is a critical determinant of competitive advantage, yet organizations face high failure rates. This paper aims to identify and categorize the Critical Success Factors (CSFs) that influence successful AI adoption by synthesizing academic and practitioner knowledge from a top-management perspective. We conducted a multivocal literature review of 57 academic and 20 practitioner sources, screened per PRISMA. From vote-count synthesis we identified 16 CSFs grouped via the Technology-Organization-Environment (TOE) framework, with organizational factors dominating such as leadership support, AI literacy, and cultural readiness. Practitioner evidence corroborates most academic CSFs and adds implementation-centric aspects (e.g., AI scalability, use-case–value alignment, partnerships). Implications for top management/Chief Information Officers include prioritizing data governance, change capability, and portfolio-level value realization. From a theoretical perspective, the paper supports continued relevance of the TOE framework for AI while highlighting the amplified importance of its organizational dimension.
Aims
Systematic visualization of anatomical landmarks is critical to prevent "blind spots" during esophagogastroduodenoscopy (EGD), which are a primary cause of missed upper gastrointestinal cancers. Standardized documentation serves as a secondary but essential tool for quality assurance. We aimed to develop a deep learning approach for the automated classification of 28 anatomical categories, including a 22-segment mapping of the stomach based on the Japanese Systemic Stomach Screening (SSS) protocol. The primary goal was to provide a technical foundation for real-time monitoring of mucosal coverage to ensure examination completeness. Primary outcomes were macro F1-score and overall accuracy for this 28-class classification task.
Methods
We used a multi-center dataset of 9.012 endoscopic images from 1.553 patients, merging the public GastroHUN database (5.202 images; 385 patients) [1] and an internal dataset (3.810 images; 1.168 patients) from a European tertiary referral centre (Augsburg, Germany). All images were obtained during routine EGD using Olympus endoscopes and anonymized prior to analysis. Each image was labelled at image level into one of 28 anatomical classes: oropharynx, esophagus, esophagogastric junction, duodenal bulb, second part of the duodenum, papilla and 22 gastric segments based on the SSS classification using a consensus-based multi-annotator process (n=4). A trained and supervised specialist annotated additional data from Augsburg. To handle this complexity, we employed a Vision Transformer (ViT-L/14) foundation model pre-trained via DINO-v2. Instead of full-parameter fine-tuning, which risks overfitting on medical datasets, we implemented Low-Rank Adaptation (LoRA) [2]. By targeting only the Q- and V-projection layers in the final six transformer blocks, we reduced the trainable parameters to 312.000 (0.36% of the total model). Evaluation was performed using a strict patient-wise 5-fold cross-validation.
Results
The model achieved a mean macro F1-score of 0.88±0.01 and an overall accuracy of 0.88±0.01. Beyond robustly identifying major regions like the esophagus and duodenum, the model showed high precision in the more challenging differentiation of fine-grained SSS segments. The use of LoRA enabled stable training and strong generalization across the combined international and local datasets ([Abb. 1]).
Conclusions
Integrating transformer-based foundation models with LoRA-based fine-tuning allows for highly accurate UGIT anatomical mapping. An F1-score of 0.88 across 28 classes suggests that this approach is robust enough for automated documentation and real-time quality monitoring. Future work will focus on expanding the multi-center data volume and implementing expert consensus-labeling across all subsets to fürther refine model reliability, alongside developing a real-time visualization tool that provides an intuitive overview of the explored segments. To our knowledge, this is among the first studies to leverage a public endoscopy dataset together with institutional data for upper GI anatomical site classification.
Die zuverlässige endoskopische Einschätzung der Infiltrationstiefe bei Neoplasien des Barrett-Ösophagus (BO) ist entscheidend für die Therapieentscheidung zwischen organerhaltender endoskopischer Resektion und chirurgischer Resektion. Ziel dieser Studie ist die Entwicklung eines KIVision- Modells, das endoskopisch kurativ resezierbare Läsionen (LGD; HGD; T1a m1 –T1b sm1, L0, V0 und G1-2; EREAC) von nicht kurativ endoskopisch resezierbaren Läsionen (≥ T1b sm2 und/oder L1, V1, G3; NEREAC) anhand endoskopischer Bildgebung unterscheidet. In einer Pilotstudie mit 116 Patient:innen wurde bei der Unterscheidung von T1a vs. T1b mit 230 Bildern ein F1-Score, eine Sensitivität und Spezifität von 74%, 77% und 64% erreicht. [1]
Material und Methodik Es handelt sich um eine ambidirektionale multizentrische Studie mit insgesamt n=578 Patient:innen und n=651 Läsionen im Zeitraum Januar 2014 bis Juni 2025. Eingeschlossen wurden Patient:innen mit gesicherter BO-assoziierter Neoplasie anhand des histologischen Goldstandards durch a) endoskopische Submukosadissektion (ESD) oder b) chirurgische Resektion. Insgesamt wurden 526 Läsionen im Arm EREAC und 125 Läsionen der Gruppe im Arm NEREAC akquiriert. Es werden Bild- und Videodaten aus Olympus und Fuji-Endoskopen verwendet. Aus der Videodokumentation wurden Frames extrahiert und anhand eines vordefinierten Schemas annotiert (u.a. Lasionssichtbarkeit, virtuelle (NBI, TXI) und farbbasierte Chromoendoskopie, Zoommodus). Es erfolgte eine mehrstufige Annotation durch einen medizinischen Doktoranden (J.B.), mit 1:1 Supervision durch einen endoskopisch tätigen Facharzt im Bereich Gastroenterologie (D.R.), sowie durch einen Barrett-Experten (A.E). Aufgrund einer Klassenimbalance zugunsten der EREAC-Gruppe erfolgt zunachst eine Zwischenanalyse aus 185 Patient:innen mit 215 Läsionen mit a 500 Frames. Mittels ConvNeXt Tiny Architektur wurde eine 5-fache Cross-Validierung mit Split auf Patientenebene durchgeführt. Primare Leistungsmetriken sind F1-Score, Sensitivität und Spezifität.
Ergebnisse Im aktuellen Test erreichte das KI-Modell für die Klassifikation (EREAC vs. NEREAC) einen mittleren F1-Score von 78.3% (resektabel: 79.8%, nicht 2 resektabel: 76.8%). Die mittlere Sensitivität und Spezifität lag bei je 78.3%. Die klassenspezifische Sensitivität und Spezifität zur Erkennung von NEREAC lag bei 72.0% und 84.6%.
Zusammenfassung Ein KI-Assistenzmodell kann die endoskopische Einschatzung der Resektabilität von Barrett-Neoplasien unterstutzen, indem es die Infiltrationstiefe aus endoskopischer Bildgebung pradiziert. Klinisch besonders relevant ist die hohe Spezifität in der Erkennung nicht-resektabler Läsionen.
Nächste Schritte sind das weitere Training mit höherer Datenmenge, die adressierte Aufarbeitung der Klassenimbalance, externe Validierung sowie die Echtzeit-Implementation.
This data article describes the Surgical Procedure Phase, Keypoint, and Instrument Recognition (PhaKIR) dataset, a collection of eight complete laparoscopic cholecystectomy videos acquired during routine minimally invasive procedures at three German medical centers. The recordings were captured with different monocular endoscopic camera systems at 25 frames per second and a resolution of 1920 × 1080 pixels, with durations ranging from 28 to 58 minutes. Segments showing regions outside the abdominal cavity were removed for anonymization, and the corresponding cut indices are provided.
The dataset offers unified annotations for three interrelated tasks on the same complete surgical sequences: surgical phase recognition, annotated for every frame at 25 fps (485,875 frames), instrument keypoint estimation (19,435 frames), and instrument instance segmentation (19,435 frames), each annotated at one frame per second (every 25th frame). Surgical phases follow the seven-phase Cholec80 scheme, extended by an undefined label for transitional frames. The instrument annotations distinguish 19 instrument classes as well as individual instances of the same class, with keypoint annotations comprising two to four class-dependent points labeled with COCO-style visibility states. Segmentation and keypoint labels were created manually using the Computer Vision Annotation Tool (CVAT), whereas phase labels were derived from documented phase-transition timestamps, and all annotations underwent a multi-stage review by a medically trained team. The data are provided in open formats (MP4, CSV, JSON, PNG) together with a frame-extraction script under the CC BY-NC-SA license and are available through controlled access upon request via the Zenodo platform.
Because the dataset combines procedural context, instrument pose, and pixel-accurate instance segmentations within full-length recordings from multiple institutions, it can be reused for single-task or multi-task model development, for temporally aware approaches that exploit motion continuity, and for cross-institutional generalization protocols such as leave-one-hospital-out evaluation. The dataset served as the training resource for the PhaKIR Challenge at the Endoscopic Vision (EndoVis) Challenge at MICCAI 2024.
Modellversuch „Leistungsstarke Auszubildende nachhaltig fördern“ (LAnf) – Ziele und Konzeption
(2003)
Background
Digital therapeutics (DTx) are patient-facing apps designed to support individuals in their daily lives. Therefore, they have the potential to revolutionize healthcare by empowering and engaging patients as active stakeholders in their own care. Despite the increasing adoption of DTx in national healthcare systems, research on their design remains limited.
Objective
The present research article introduces “DiGATax”, a taxonomy designed to categorize and analyze DTx, including perspectives on content, intervention delivery logic and technology, as well as the patient’s interface, consolidating and expanding upon prior taxonomic work.
Methods
The taxonomy was created through a multi-method approach, combining a systematic review of existing digital health taxonomies with a qualitative analysis of
n
= 44 evidence-based, permanently listed applications from the German DiGA directory. Additionally, a taxonomy-based cluster analysis was performed to identify archetypes in the current DTx landscape.
Results
The proposed taxonomy comprises 19 dimensions and 108 characteristics, enabling a systematic classification and discussion of existing applications. Cluster analysis identified three archetypes: personal goal-centered, gameful designed, and conversational-driven, with the latter dividing into two subtypes. Additional exploratory analyses examined the user interface as a potential factor associated with user acceptance and explored possible links between DTx design, health-related and engagement outcomes.
Conclusions
By offering new insights into DTx design, this study contributes towards more organized research and reporting, ultimately paving the way for the development of effective solutions. It also marks a further step towards Meta-DTx, which aim to align patient care for multimorbid patients under one umbrella.
Shaping the spatial intensity profile of the process laser beam is a promising approach for improving part and surface quality in laser powder bed fusion. However, practical implementation is limited by logistical or economic constraints, since the process beam shape needs to be adapted depending on the material and print process conditions, often involving specialized laser sources or complex optical setups. Here, a single refractive focal beam shaping optic is employed to realize laser beam intensity variations of Gaussian, flat-top (tophat) and annular (ring) beams by controlled defocusing. Additionally, a conduction-mode thermal model using measured beam profiles and temperature-dependent properties is implemented. The model correlates well with published single-track melt pool dimensions for Gaussian and ring beams and predicts lack-of-fusion defects. The observations indicate that tophat and shallow ring beam profiles lead to a smaller spread of part density values in SS 316L across low to moderate energy densities, compared to other beam profiles explored in the study. Topographical characterization of the top surfaces indicates that tophat beams exhibit superior surface quality (lower roughness).
