Eosinophilic esophagitis (EoE) is a chronic, food antigen-driven, allergic
inflammatory condition of the esophagus associated with elevated esophageal
eosinophils. EoE is a top cause of chronic dysphagia after GERD. Diagnosis of
EoE relies on counting eosinophils in histological slides, a manual and
time-consuming task that limits the ability to extract complex
patient-dependent features. The treatment of EoE includes medication and food
elimination. A personalized food elimination plan is crucial for engagement and
efficiency, but previous attempts failed to produce significant results. In
this work, on the one hand, we utilize AI for inferring histological features
from the entire biopsy slide, features that cannot be extracted manually. On
the other hand, we develop causal learning models that can process this wealth
of data. We applied our approach to the 'Six-Food vs. One-Food Eosinophilic
Esophagitis Diet Study', where 112 symptomatic adults aged 18-60 years with
active EoE were assigned to either a six-food elimination diet (6FED) or a
one-food elimination diet (1FED) for six weeks. Our results show that the
average treatment effect (ATE) of the 6FED treatment compared with the 1FED
treatment is not significant, that is, neither diet was superior to the other.
We examined several causal models and show that the best treatment strategy was
obtained using T-learner with two XGBoost modules. While 1FED only and 6FED
only provide improvement for 35%-38% of the patients, which is not
significantly different from a random treatment assignment, our causal model
yields a significantly better improvement rate of 58.4%. This study illustrates
the significance of AI in enhancing treatment planning by analyzing molecular
features' distribution in histological slides through causal learning. Our
approach can be harnessed for other conditions that rely on histology for
diagnosis and treatment.

这项研究利用 AI 推断组织学特征，并引入因果学习模型，应用于以组织学诊断为基础的食物消除计划中。研究结果表明，相比于只有一种食物消除计划，六种食物消除计划并不能提供更好的治疗效果，而且单独的食物消除计划对 35% 至 38% 的患者有改善作用，而本研究推出的因果模型治疗策略可以提供 58.4% 的改善率。