ArticleJournal of healthcare engineering2021
Deep Learning-Based Computed Tomography Image Features in the Detection and Diagnosis of Perianal Abscess Tissue.
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 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 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
- Study on Automatic Multi-Classification of Spine Based on Deep Learning and Postoperative Infection Screening.Journal of healthcare engineering · 2022Trial
- Artificial intelligence in anal fistula: mapping evidence to IDEAL stages.Annals of coloproctology · 2026Review
- Network diffusion modeling predicts spatiotemporal gray matter alterations in internet gaming disorder.Psychological medicine · 2026Article
- Deep learning-based bone removal in head and neck computed tomography angiography: a comparative study with conventional algorithms.Quantitative imaging in medicine and surgery · 2025Article
- Intervention Effect of Evidence-Based Nursing on Postoperative Recovery of Vacuum Sealing Drainage in Patients with High Perianal Abscess with Magnetic Resonance Imaging Sequence Images.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The performance characteristics of deep learning fully convolutional neural network (DLFCNN) algorithm-based computed tomography (CT) images were investigated in the detection and diagnosis of perianal abscess tissue. 60 patients who were medically diagnosed as perianal abscesses in the hospital were selected as the experimental group, and 60 healthy volunteers were selected as the control group. In this study, the DLFCNN algorithm based on deep learning was compared with the CNN algorithm and applied to the segmentation training of CT images of patients with perianal abscesses. Then, the segmentation metrics Jaccard, Dice coefficient, precision rate, and recall rate were compared by extracting the region of interest. The results showed that Jaccard (0.7326) calculated by the CNN algorithm was sharply lower than that of the DLFCNN algorithm (0.8525), and the Dice coefficient (0.7264) was also steeply lower than that of the DLFCNN algorithm (0.8434) (
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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.