Towards Hepatic Cancer Detection with Bayesian Networks for Patients Digital Twins Modelling

In healthcare, Digital Twins (DTs) promise to personalise treatment plans, simulate surgeries, and forecast individual responses to particular therapies. By adopting Machine Learning methodologies, it is possible to figure out some insights hidden among the features for enhancing medical diagnosis. Our contribution leverages the role of intraoperative ultrasound in liver surgery in building a Bayesian Network (BN) model for enabling the early localisation of hepatic cancer. Under this premise, we aim to determine how a possible diagnosis error could be affected by factors such as age, gender, and before-surgery treatment. The mean to this objective is the construction of a BN model by using both an explicit top-down approach and parameter learning approaches. This is the first step toward the DT definition of a patient affected by hepatic cancer in charge of continuously monitoring the health status.