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Eosinophilic esophagitis (EoE) is a chronic, food antigen-driven, allergicinflammatory condition of the esophagus associated with elevated esophagealeosinophils. EoE is a top cause of chronic dysphagia after GERD. Diagnosis ofEoE relies on counting eosinophils in histological slides, a manual andtime-consuming task that limits the ability to extract complexpatient-dependent features. The treatment of EoE includes medication and foodelimination. A personalized food elimination plan is crucial for engagement andefficiency, but previous attempts failed to produce significant results. Inthis work, on the one hand, we utilize AI for inferring histological featuresfrom the entire biopsy slide, features that cannot be extracted manually. Onthe other hand, we develop causal learning models that can process this wealthof data. We applied our approach to the 'Six-Food vs. One-Food EosinophilicEsophagitis Diet Study', where 112 symptomatic adults aged 18-60 years withactive EoE were assigned to either a six-food elimination diet (6FED) or aone-food elimination diet (1FED) for six weeks. Our results show that theaverage treatment effect (ATE) of the 6FED treatment compared with the 1FEDtreatment is not significant, that is, neither diet was superior to the other.We examined several causal models and show that the best treatment strategy wasobtained using T-learner with two XGBoost modules. While 1FED only and 6FEDonly provide improvement for 35%-38% of the patients, which is notsignificantly different from a random treatment assignment, our causal modelyields a significantly better improvement rate of 58.4%. This study illustratesthe significance of AI in enhancing treatment planning by analyzing molecularfeatures' distribution in histological slides through causal learning. Ourapproach can be harnessed for other conditions that rely on histology fordiagnosis and treatment.

Harnessing Digital Pathology And Causal Learning To Improve Eosinophilic Esophagitis Dietary Treatment Assignment