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

Abstract

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.

Deep learning approach on symptom questionnaire and abdominal radiography for diagnosis of dyssynergic defecation

“…The author proposed a deep learning algorithm to evaluate DD using a newly developed technology called high-definition anorectal manometry (3D-HDAM). Spatial-temporal data extracted from the 3D-HDAM studies at a tertiary healthcare center was used as input data [31]. Although their outcomes were outstanding with comparable diagnostic accuracy, there is a limited access for the 3D-HDAM technology which makes it difficult to obtain the data in small hospitals.…”