TABA Seminar and Networking Event
Location: Astrazeneca, 1004 Middlegate Rd, Mississauga, ON
Time: 14:00 PM EST, light snacks and refreshments provided
Cost: This event is free of charge, kindly sponsored by AstraZeneca

Agenda:

Presenter: Jianyue Bai, University of Toronto
Title: Leveraging Neural Networks to Predict Abscess Formation in Acute Appendicitis Pediatric Patients
Abstract: We performed a retrospective chart review of patients undergoing appendectomy from 2018–2025 at a single institution. Twenty-eight literature-supported pre-and intra-operative risk factors were used as model inputs, with post-operative IAA serving as the binary outcome. We developed a five-step attentive neural network that mimics sequential clinical reasoning using gated linear unit blocks and dynamic attention. Five-fold nested cross-validation (a rotating 3:3:1 train/validation/test split) was used to prevent selection bias, and an ensemble of 50 runs ensured robust estimation. Final performance was evaluated using out-of-fold (OOF) testing, with 95% CIs derived via bootstrapping, and benchmarked against logistic regression, multilayer perceptron, and CatBoost. From 2,850 appendectomies, 1,462 reviewed, n=977 met inclusion criteria: pediatric patients with acute complicated appendicitis (median age 9 years, IQR 7-12), undergoing primary laparoscopic appendectomy. The observed IAA rate was 14% (IAA n=139, no IAA n=838). In OOF testing, our attentive model achieved the best discrimination (Ensemble ROC AUC = 0.77, 95% CI 0.73–0.81). With sensitivity of 0.91 (95% CI 0.85–0.95) and negative predictive value of 0.96 (95% CI 0.94–0.98), it functions as an effective screening tool. Monotonically increasing observed IAA rates across predicted risk strata validate its utility for risk stratification. We conclude IAA risk can be predicted using pre-and intra-operative factors with good discrimination, enabling screening and risk stratification in pediatric complicated appendicitis.

Presenter: Luke Bai
Title: TBD