Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Ulcerative colitis (UC) is an intractable disease that affects young adults. Histological findings are essential for its diagnosis; however, the number of diagnostic pathologists is limited. Herein, we used a no-code artificial intelligence (AI) platform "Teachable Machine" to train a model that could distinguish between histological images of UC, non-UC coloproctitis, adenocarcinoma, and control. A total of 5100 histological images for training and 900 histological images for testing were prepared by pathologists. Our model showed accuracies of 0.99, 1.00, 0.99, and 0.99, for UC, non-UC coloproctitis, adenocarcinoma, and control, respectively. This is the first report in which a no-code easy AI platform has been able to comprehensively recognize the distinctive histologic patterns of UC.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1177/10668969231204955 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Tuberculosis tenosynovitis in the wrist: a case report.
Acta Ortop Mex
September 2025
Universidad de Manizales. Colombia.
Articular tuberculosis is a rare condition, with extrapulmonary presentations most commonly appearing in joints such as the hip or knee. It is usually associated with conditions like immunosuppression or a history of pulmonary tuberculosis. Diagnosis involves imaging or pathology, and treatment typically involves surgical intervention along with medication.
View Article and Find Full Text PDFProgressive lifespan modifications in the corpus callosum following a single concussion in juvenile male mice monitored by diffusion MRI.
Exp Neurol
September 2025
CNRS UMR 5536 RMSB, University of Bordeaux, Bordeaux, France; Basic Science Department, Loma Linda University School of Medicine, Loma Linda, CA, USA; CNRS UMR 7372 CEBC, La Rochelle University, Villiers-en-Bois, France.
Introduction: The vulnerability of white matter (WM) in acute and chronic moderate-severe traumatic brain injury (TBI) has been established. In concussion syndromes, including preclinical rodent models, lacking are comprehensive longitudinal studies spanning the mouse lifespan. We previously reported early WM modifications using clinically relevant neuroimaging and histological measures in a model of juvenile concussion at one month post injury (mpi) who then exhibited cognitive deficits at 12mpi.
View Article and Find Full Text PDFRadiomics nomogram from multiparametric magnetic resonance imaging for preoperative prediction of substantial lymphovascular space invasion in endometrial cancer.
Abdom Radiol (NY)
September 2025
Department of Radiology, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics and Gynecology and Pediatrics, Fujian Medical University, Fuzhou, China.
Background: We aimed to develop and validate a radiomics-based machine learning nomogram using multiparametric magnetic resonance imaging to preoperatively predict substantial lymphovascular space invasion in patients with endometrial cancer.
Methods: This retrospective dual-center study included patients with histologically confirmed endometrial cancer who underwent preoperative magnetic resonance imaging (MRI). The patients were divided into training and test sets.
Deciphering adrenal cysts: unveiling insights into imaging, histology, and optimal management approaches.
Minerva Endocrinol (Torino)
September 2025
Department of Endocrinology, Karolinska University Hospital, Stockholm, Sweden -
Adrenal cysts are rare lesions that are increasingly discovered incidentally during radiological examinations conducted without suspicion of adrenal disease. Typically benign, hormonally nonfunctional, and asymptomatic, these lesions may occasionally manifest mass effect symptoms such as pain or abdominal discomfort, particularly in large cysts. Management approaches vary from no follow-up to hormonal investigation, imaging follow-up, or adrenalectomy, especially if the cyst is growing or exhibits an atypical appearance.
View Article and Find Full Text PDFFmH2ST: foundation model-based spatial transcriptomics generation from histological images.
Nucleic Acids Res
September 2025
School of Software, Shandong University, Jinan 250101, Shandong, China.
Spatial transcriptomics (ST) reveals gene expression distributions within tissues. Yet, predicting spatial gene expression from histological images still faces the challenges of limited ST data that lack prior knowledge, and insufficient capturing of inter-slice heterogeneity and intra-slice complexity. To tackle these challenges, we introduce FmH2ST, a foundation model-based method for spatial gene expression prediction.
View Article and Find Full Text PDF