Objective: Our study aimed to evaluate and validate PanSegNet, a deep learning (DL) algorithm for pediatric pancreas segmentation on MRI in children with acute pancreatitis (AP), chronic pancreatitis (CP), and healthy controls. Methods: With IRB approval, we retrospectively collected 84 MRI scans (1.5T/3T Siemens Aera/Verio) from children aged 2-19 years at Gazi University (2015-2024). The dataset includes healthy children as well as patients diagnosed with AP or CP based on clinical criteria. Pediatric and general radiologists manually segmented the pancreas, then confirmed by a senior pediatric radiologist. PanSegNet-generated segmentations were assessed using Dice Similarity Coefficient (DSC) and 95th percentile Hausdorff distance (HD95). Cohen's kappa measured observer agreement. Results: Pancreas MRI T2W scans were obtained from 42 children with AP/CP (mean age: 11.73 +/- 3.9 years) and 42 healthy children (mean age: 11.19 +/- 4.88 years). PanSegNet achieved DSC scores of 88% (controls), 81% (AP), and 80% (CP), with HD95 values of 3.98 mm (controls), 9.85 mm (AP), and 15.67 mm (CP). Inter-observer kappa was 0.86 (controls), 0.82 (pancreatitis), and intra-observer agreement reached 0.88 and 0.81. Strong agreement was observed between automated and manual volumes (R^2 = 0.85 in controls, 0.77 in diseased), demonstrating clinical reliability. Conclusion: PanSegNet represents the first validated deep learning solution for pancreatic MRI segmentation, achieving expert-level performance across healthy and diseased states. This tool, algorithm, along with our annotated dataset, are freely available on GitHub and OSF, advancing accessible, radiation-free pediatric pancreatic imaging and fostering collaborative research in this underserved domain.

This study evaluated self-reported response certainty across several large language models (GPT, Claude, Llama, Phi, Mistral, Gemini, Gemma, and Qwen) using 300 gastroenterology board-style questions. The highest-performing models (GPT-o1 preview, GPT-4o, and Claude-3.5-Sonnet) achieved Brier scores of 0.15-0.2 and AUROC of 0.6. Although newer models demonstrated improved performance, all exhibited a consistent tendency towards overconfidence. Uncertainty estimation presents a significant challenge to the safe use of LLMs in healthcare. Keywords: Large Language Models; Confidence Elicitation; Artificial Intelligence; Gastroenterology; Uncertainty Quantification

Background and Aims: This study evaluates the medical reasoning performance of large language models (LLMs) and vision language models (VLMs) in gastroenterology. Methods: We used 300 gastroenterology board exam-style multiple-choice questions, 138 of which contain images to systematically assess the impact of model configurations and parameters and prompt engineering strategies utilizing GPT-3.5. Next, we assessed the performance of proprietary and open-source LLMs (versions), including GPT (3.5, 4, 4o, 4omini), Claude (3, 3.5), Gemini (1.0), Mistral, Llama (2, 3, 3.1), Mixtral, and Phi (3), across different interfaces (web and API), computing environments (cloud and local), and model precisions (with and without quantization). Finally, we assessed accuracy using a semiautomated pipeline. Results: Among the proprietary models, GPT-4o (73.7%) and Claude3.5-Sonnet (74.0%) achieved the highest accuracy, outperforming the top open-source models: Llama3.1-405b (64%), Llama3.1-70b (58.3%), and Mixtral-8x7b (54.3%). Among the quantized open-source models, the 6-bit quantized Phi3-14b (48.7%) performed best. The scores of the quantized models were comparable to those of the full-precision models Llama2-7b, Llama2--13b, and Gemma2-9b. Notably, VLM performance on image-containing questions did not improve when the images were provided and worsened when LLM-generated captions were provided. In contrast, a 10% increase in accuracy was observed when images were accompanied by human-crafted image descriptions. Conclusion: In conclusion, while LLMs exhibit robust zero-shot performance in medical reasoning, the integration of visual data remains a challenge for VLMs. Effective deployment involves carefully determining optimal model configurations, encouraging users to consider either the high performance of proprietary models or the flexible adaptability of open-source models.

In this paper, with the goal of addressing the high early-detection miss rate of colorectal cancer (CRC) polyps during a colonoscopy procedure, we propose the design and fabrication of a unique inflatable vision-based tactile sensing balloon (VTSB). The proposed soft VTSB can readily be integrated with the existing colonoscopes and provide a radiation-free, safe, and high-resolution textural mapping and morphology characterization of CRC polyps. The performance of the proposed VTSB has been thoroughly characterized and evaluated on four different types of additively manufactured CRC polyp phantoms with three different stiffness levels. Additionally, we integrated the VTSB with a colonoscope and successfully performed a simulated colonoscopic procedure inside a tube with a few CRC polyp phantoms attached to its internal surface.

The integration of artificial intelligence (AI) into gastroenterology and hepatology (GI) will inevitably transform the practice of GI in the coming decade. While the application of AI in health care is not new, advancements are occurring rapidly, and the future landscape of AI is beginning to come into focus. From endoscopic assistance via computer vision technology to the predictive capabilities of the vast information contained in the electronic health records, AI promises to optimize and expedite clinical and procedural practice and research in GI.

THURSDAY, Jan. 5, 2023 (HealthDay News) -- For patients with Lynch syndrome (LS), artificial intelligence (AI)-assisted colonoscopy is promising for detecting adenomas, especially flat adenomas, according to a study published online Dec. 26 in the United European Gastroenterology Journal. Robert Hüneburg, M.D., from the National Center for Hereditary Tumor Syndromes at University Hospital Bonn in Germany, and colleagues examined the diagnostic performance of AI-assisted colonoscopy compared with high-definition white-light endoscopy (HD-WLE) in adult patients with LS, with a pathogenic germline variant (MLH1, MHS2, MHS6) and at least one previous colonoscopy (interval, 10 to 36 months). A total of 96 patients were included in the analysis. The researchers found that adenomas were detected in 12 of 46 and 18 of 50 patients in the HD-WLE and AI arms, respectively (26.1 versus 36.0 percent). Detection of flat adenomas (Paris classification 0 to IIb) was increased significantly with use of AI-assisted colonoscopy (numbers of detected flat adenomas: 17 of 30 versus four of 20).

As artificial intelligence (AI) technology in the gastrointestinal field continues to advance, speakers at Digestive Disease Week 2022 discussed …

The use of machine learning techniques in biomedical research has exploded over the past few years, as exemplified by the dramatic increase in the number of journal articles indexed on PubMed by the term "machine learning", from 3,200 in 2015 to over 18,000 in 2020. While substantial scientific advancements have been made possible thanks to machine learning, the inner working of most machine learning algorithms remains foreign to many clinicians, most of whom are quite familiar with traditional statistical methods but have little formal training on advanced computer algorithms. Unfortunately, journal reviewers and editors are sometimes content with accepting machine learning as a black box operation and fail to analyze the results produced by machine learning models with the same level of scrutiny that is applied to other clinical and basic science research. The goal of this journal club is to help readers develop the knowledge and skills necessary to digest and critique biomedical journal articles involving the use of machine learning techniques. It is hard for a reviewer to know what questions to ask if he/she does not understand how these algorithms work.

The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett's esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.

A robot that can perform colonoscopies may make the procedure simpler and less unpleasant. Pietro Valdastri at the University of Leeds in the UK and his colleagues have developed a robotic arm that uses a machine learning algorithm to move a flexible probe along the colon. The probe is a magnetic endoscope, a tube with a camera lens at the tip, that the robot controls via a magnet external to the body. The system can either work autonomously or be controlled by a human operator using a joystick, which pushes the endoscope tip further along the colon. The system also keeps track of the location and orientation of the endoscope inside the colon.