Interpretable and Intervenable Ultrasonography-based Machine Learning Models for Pediatric Appendicitis
Abstract
Appendicitis is among the most frequent reasons for pediatric abdominal surgeries. With recent advances in machine learning, data-driven decision support could help clinicians diagnose and manage patients while reducing the number of non-critical surgeries. Previous decision support systems for appendicitis focused on clinical, laboratory, scoring and computed tomography data, mainly ignoring abdominal ultrasound, a noninvasive and readily available diagnostic modality. To this end, we developed and validated interpretable machine learning models for predicting the diagnosis, management and severity of suspected appendicitis using ultrasound images. Our models were trained on a dataset comprising 579 pediatric patients with 1709 ultrasound images accompanied by clinical and laboratory data. Our methodological contribution is the generalization of concept bottleneck models to prediction problems with multiple views and incomplete concept sets. Notably, such models lend themselves to interpretation and interaction via high-level concepts understandable to clinicians without sacrificing performance or requiring time-consuming image annotation when deployed. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000601130Publication status
publishedJournal / series
arXivPages / Article No.
Publisher
Cornell UniversitySubject
Appendicitis; Pediatrics; Ultrasound; Machine Learning; Classification; Interpretability; Concepts; Multiview LearningOrganisational unit
09670 - Vogt, Julia / Vogt, Julia
Related publications and datasets
Is previous version of: http://hdl.handle.net/20.500.11850/621635
Is previous version of: https://doi.org/10.3929/ethz-b-000643550
More
Show all metadata
ETH Bibliography
yes
Altmetrics