ArXiv Preprint
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.
Ričards Marcinkevičs, Patricia Reis Wolfertstetter, Ugne Klimiene, Ece Ozkan, Kieran Chin-Cheong, Alyssia Paschke, Julia Zerres, Markus Denzinger, David Niederberger, Sven Wellmann, Christian Knorr, Julia E. Vogt
2023-02-28
