ArticleJournal of healthcare engineering2021
Deep Learning-Based CT Image Characteristics and Postoperative Anal Function Restoration for Patients with Complex Anal Fistula.
Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Current Status and Role of Artificial Intelligence in Anorectal Diseases and Pelvic Floor Disorders.JSLS : Journal of the Society of Laparoendoscopic SurgeonsPooled it
- Artificial intelligence-assisted detection of internal openings in anal fistulas using endorectal ultrasound: a YOLOv11-based diagnostic system.Translational gastroenterology and hepatology · 2026Article
- The role of MRI in perianal fistulizing disease: diagnostic imaging and classification systems to monitor disease activity.Abdominal radiology (New York) · 2025Review
- Impact of sodium-glucose cotransporter-2 inhibitors on pulmonary vascular cell function and arterial remodeling.World journal of cardiology · 2025Review
- Two-finger digital rectal examination for the diagnosis of anal fistula: protocol for a randomized controlled trial.Journal of gastrointestinal oncology · 2023Article
- Three-dimensional pelvic ultrasound is a practical tool for the assessment of anal fistula.BMC gastroenterology · 2023Article
- Integrated Learning Model-Based Assessment of Enteral Nutrition Support in Neurosurgical Intensive Care Patients.BioMed research international · 2022Article
- The Value of Transrectal Ultrasound in the Preoperative Diagnosis of Complex Anal Fistula (CAF): Based on a Retrospective Cohort Study.Computational and mathematical methods in medicine · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: This study aimed to optimize the CT images of anal fistula patients using a convolutional neural network (CNN) algorithm to investigate the anal function recovery. Methods: 57 patients with complex anal fistulas admitted to our hospital from January 2020 to February 2021 were selected as research subjects. Of them, CT images of 34 cases were processed using the deep learning neural network, defined as the experimental group, and the remaining unprocessed 23 cases were in the control group. Whether to process CT images depended on the patient's own wish. The imaging results were compared with the results observed during the surgery. Results: It was found that, in the experimental group, the images were clearer, with DSC = 0.89, precision = 0.98, and recall = 0.87, indicating that the processing effects were good; that the CT imaging results in the experimental group were more consistent with those observed during the surgery, and the difference was notable ( Conclusion: CT images processed by deep learning are clearer, leading to higher accuracy of preoperative diagnosis, which is suggested in clinics.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.