Abstract
Background
We developed a deep learning algorithm to evaluate defecatory patterns to identify dyssynergic defecation using 3-dimensional high definition anal manometry (3D-HDAM).
Aims
We developed a 3D-HDAM deep learning algorithm to evaluate for dyssynergia.
Methods
Spatial–temporal data were extracted from consecutive 3D-HDAM studies performed between 2018 and 2020 at Dartmouth-Hitchcock Health. The technical procedure and gold standard definition of dyssynergia were based on the London consensus, adapted to the needs of 3D-HDAM technology. Three machine learning models were generated: (1) traditional machine learning informed by conventional anorectal function metrics, (2) deep learning, and (3) a hybrid approach. Diagnostic accuracy was evaluated using bootstrap sampling to calculate area-under-the-curve (AUC). To evaluate overfitting, models were validated by adding 502 simulated defecation maneuvers with diagnostic ambiguity.
Results
302 3D-HDAM studies representing 1208 simulated defecation maneuvers were included (average age 55.2 years; 80.5% women). The deep learning model had comparable diagnostic accuracy [AUC 0.91 (95% confidence interval 0.89–0.93)] to traditional [AUC 0.93(0.92–0.95)] and hybrid [AUC 0.96(0.94–0.97)] predictive models in training cohorts. However, the deep learning model handled ambiguous tests more cautiously than other models; the deep learning model was more likely to designate an ambiguous test as inconclusive [odds ratio 4.21(2.78–6.38)] versus traditional/hybrid approaches.
Conclusions
Deep learning is capable of considering complex spatial–temporal information on 3D-HDAM technology. Future studies are needed to evaluate the clinical context of these preliminary findings.
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Funding
Dr. Shah is supported by the AGA Research Foundation’s 2019 American Gastroenterological Association-Shire Research Scholar Award in Functional GI and Motility Disorders. Dr. Levy is supported under National Institutes of Health subaward P20GM104416.
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ES and JL were involved in study concept and design. CN and JC performed data extraction. JL performed statistical analysis. JL authored the initial draft of the manuscript. All authors critically revised the manuscript and approved the final copy.
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Dr. Shah is a consultant for Laborie/GI Supply. Dr. Baker is a consultant for Laborie/GI Supply, Diversatek, and Medtronic. Dr. Chey is a consultant for Allergan, Biomerica, IM Health, Ironwood, Outpost, QOL Medical, Ritter, Salix, Urovant and has research grants from Commonwealth Diagnostics, Ironwood, QOL Medical, Salix, Urovant, Vibrant, and Zespri. The authors have no relevant disclosures.
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Levy, J.J., Navas, C.M., Chandra, J.A. et al. Video-Based Deep Learning to Detect Dyssynergic Defecation with 3D High-Definition Anorectal Manometry. Dig Dis Sci 68, 2015–2022 (2023). https://doi.org/10.1007/s10620-022-07759-3
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DOI: https://doi.org/10.1007/s10620-022-07759-3