Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
661-04-002 ISMRM Abstract

Cross-compartment phenotyping and machine-learning classification of pelvic foor dysfunction using MR defecography

Primary: Body - Pelvis
Secondary: Analysis Methods - Classification and Prediction
661-04-002 · AI-Based Analysis in MR Imaging · Thursday, 14 May, 2:35 PM–3:30 PM · Digital Posters Row B
Keywords: Machine learning Classification MR Defecography Pelvic Floor Dysfunction Rectocele and Prolapse
Accepted
Aakaar Kapoor1, Tushar Kapoor1, Aakriti Kapoor1, Dharmesh Singh 2, Dileep Kumar3
1City Imaging & Clinical Labs, Delhi, India
2Shanghai United Imaging Healthcare Co., Ltd, Shanghai, China
3Central Research Institute, Global Scientific Collaborations, Shanghai United Imaging Healthcare Co., Ltd, Shanghai, China
Presenting Author: Dharmesh Singh

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References

1. Pugliesi RA, Triscari Barberi M, Roccella G, et al. MR Defecography Improves Diagnosis of Postoperative Pelvic Floor Dysfunction After Gynecological Surgery. Diagnostics (Basel). 2025;15(13):1625. doi:10.3390/diagnostics15131625 [doi]
2. Salvador JC, Coutinho MP, Venâncio JM, Viamonte B. Dynamic magnetic resonance imaging of the female pelvic floor-a pictorial review. Insights Imaging. 2019;10(1):4. doi:10.1186/s13244-019-0687-9 [doi]

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http://echo.ismrm.org/p/ISMRM2026/661-04-002