Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Background: We developed an artificial intelligence (AI)-based endoscopic ultrasonography (EUS) system for diagnosing the invasion depth of early gastric cancer (EGC), and we evaluated the performance of this system.
Methods: A total of 8280 EUS images from 559 EGC cases were collected from 11 institutions. Within this dataset, 3451 images (285 cases) from one institution were used as a development dataset. The AI model consisted of segmentation and classification steps, followed by the CycleGAN method to bridge differences in EUS images captured by different equipment. AI model performance was evaluated using an internal validation dataset collected from the same institution as the development dataset (1726 images, 135 cases). External validation was conducted using images collected from the other 10 institutions (3103 images, 139 cases).
Results: The area under the curve (AUC) of the AI model in the internal validation dataset was 0.870 (95% CI: 0.796-0.944). Regarding diagnostic performance, the accuracy/sensitivity/specificity values of the AI model, experts (n = 6), and nonexperts (n = 8) were 82.2/63.4/90.4%, 81.9/66.3/88.7%, and 68.3/60.9/71.5%, respectively. The AUC of the AI model in the external validation dataset was 0.815 (95% CI: 0.743-0.886). The accuracy/sensitivity/specificity values of the AI model (74.1/73.1/75.0%) and the real-time diagnoses of experts (75.5/79.1/72.2%) in the external validation dataset were comparable.
Conclusions: Our AI model demonstrated a diagnostic performance equivalent to that of experts.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11217111 | PMC |
| http://dx.doi.org/10.1007/s00535-024-02102-1 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Development of an Instrument to Measure Resilience to Misinformation on Social Media: Measurement Properties and Validation.
J Med Internet Res
September 2025
School of Nursing, University of Minho, Braga, Portugal.
Background: The spread of misinformation on social media poses significant risks to public health and individual decision-making. Despite growing recognition of these threats, instruments that assess resilience to misinformation on social media, particularly among families who are central to making decisions on behalf of children, remain scarce.
Objective: This study aimed to develop and evaluate the psychometric properties of a novel instrument that measures resilience to misinformation in the context of social media among parents of school-age children.
Development and validation of a measure of sensory sensitivity - the Sensory Sensitivity Profile: a Rasch analysis.
Scand J Rheumatol
September 2025
The Parker Institute, Copenhagen University Hospital, Bispebjerg and Frederiksberg, Frederiksberg, Denmark.
Objective: Pain hypersensitivity and hypersensitivity to other sensory modalities (visual, auditory, olfactory, and tactile) are considered defining features in nociplastic pain states. A self-report measure of sensory sensitivity may help to characterize sensory profiles across pain populations. This study aimed to evaluate the psychometric properties of a newly developed Danish nine-item Sensory Sensitivity Profile (SSP) questionnaire in patients with fibromyalgia.
View Article and Find Full Text PDFPerformance of vision language models for optic disc swelling identification on fundus photographs.
Front Digit Health
August 2025
Department of Ophthalmology, Stanford University, Palo Alto, CA, United States.
Introduction: Vision language models (VLMs) combine image analysis capabilities with large language models (LLMs). Because of their multimodal capabilities, VLMs offer a clinical advantage over image classification models for the diagnosis of optic disc swelling by allowing a consideration of clinical context. In this study, we compare the performance of non-specialty-trained VLMs with different prompts in the classification of optic disc swelling on fundus photographs.
View Article and Find Full Text PDFMulti-Omics and Clinical Validation Identify Key Glycolysis- and Immune-Related Genes in Sepsis.
Int J Gen Med
September 2025
Department of Geriatrics, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, People's Republic of China.
Background: Sepsis is characterized by profound immune and metabolic perturbations, with glycolysis serving as a pivotal modulator of immune responses. However, the molecular mechanisms linking glycolytic reprogramming to immune dysfunction remain poorly defined.
Methods: Transcriptomic profiles of sepsis were obtained from the Gene Expression Omnibus.
The morphological patterns of lung adenocarcinoma (LUAD) are recognized for their prognostic significance, with ongoing debate regarding the optimal grading strategy. This study aimed to develop a clinical-grade, fully quantitative, and automated tool for pattern classification/quantification (PATQUANT), to evaluate existing grading strategies, and determine the optimal grading system. PATQUANT was trained on a high-quality dataset, manually annotated by expert pathologists.
View Article and Find Full Text PDF