Video-Based Deep Learning to Detect Dyssynergic Defecation with 3D High-Definition Anorectal Manometry

BACKGROUND AND AIMS: Evaluation for dyssynergia is the most common reason that gastroenterologists refer patients for anorectal manometry, because dyssynergia is amenable to biofeedback by physical therapists. High-definition anorectal manometry (3D-HDAM) is a promising technology to evaluate anorectal physiology, but adoption remains limited by its sheer complexity. 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 2018-2020 at a tertiary institution. 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 1,208 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: By considering complex spatial-temporal information beyond conventional anorectal function metrics, deep learning on 3D-HDAM technology may enable gastroenterologists to reliably identify and manage dyssynergia in broader practice.

EVALUATING EXPERIENCE WITH ANORECTAL MANOMETRY IN AN OUTPATIENT SETTING

Purpose of the study: 1) Evaluating application of anorectal manometry in an outpatient setting. 2) Determination of reference manometric and point criteria for normal rectal sphincter indicators and Fecal Incontinence (FI) according to anorectal manometry data using a Peritron anal sensor. Materials and methods: The retrospective study was based on the results of anorectal manometry using Peritron equipment (Cardio Design Pty Ltd, Australia) on 1200 patients with various proctological diseases, who were examined and treated at the Center for Outpatient Proctology of the surgical department of Railway Clinical Hospital (Rostov-on-Don, Russia) from 2015 to 2020. All patients were divided into 4 groups in accordance with the clinical classification of Fecal Incontinence (FI) developed at the National Coloproctology Research Center and the obtained results. As a subjective assessment, the Cleveland Fecal Incontinence Scale (Wexner) was used, and objectively, the results of anorectal manometry using the Peritron sphinctometer. Results: Our manometric data enabled to develop a reliable indicator scale of normal sphincter function and various degrees of fecal incontinence according to the Peritron findings and allowed to recommend the device for a wider introduction into clinical practice, in particular in coloproctology, to perform anorectal manometry.