SOCIAL OUTREACH POPULARIZING SUMMARY Peritoneal metastases (PM) occur when cancerous cells spread to the peritoneum, a thin lining that surrounds the abdominal organs. This disease significantly affects patient survival and quality of life. Treatment options include different types of surgery and chemotherapy. Each approach has its own risks. An example of a treatment with curative intent for PM is cytoreductive surgery, a surgical procedure during which the surgeon removes all visible lesions for the peritoneum to effectively and entirely eradicate PM. A treatment option for patients with non-operable or advanced PM, is a recently developed palliative treatment, namely ePIPAC. A palliative treatment is an approach that aims at relieving symptoms and improving quality of life. ePIPAC, meaning Electrostatic Pressurized Intraperitoneal Aerosol Chemotherapy, provides an efficient way of administering chemotherapy through a laparoscopic procedure. Small incisions are made in the abdominal wall. These access points are used to insert a camera and an instrument that is able to spray chemotherapy in the abdominal cavity, creating a mist. In order to improve the distribution of the mist-like chemotherapy, an electric charge is applied. The decision regarding the best therapy to minimize the potential risks for a patient with PM depends on multiple factors. The Peritoneal Cancer Index (PCI) is a scoring system to objectively determine the best treatment choice. The PCI is a crucial tool for identifying patients who may benefit from curative treatments and for guiding decisions toward palliative options when curative treatments are not feasible. The PCI is utilized by surgeons and assesses the spread, the amount and the size of the PM. The PCI score ranges from 0 to 39, with higher scores indicating more severe disease. However, the surgical assessment of the PCI has limitations, as it can be difficult to differentiate between PM and benign lesions, such as scar tissue. Even experienced surgeons can encounter difficulties with this task during complex cases, leading to inter-observer variability. This is where the application of machine learning (ML), a branch of artificial intelligence, becomes relevant. ML can analyze data from laparoscopic videos to recognize patterns in tissue and identify PM, abdominal organs, and surgical instruments. ML has the potential to assist clinicians in distinguishing between cancerous and non-cancerous lesions, ultimately enhancing accuracy during PCI assessment. SOCIAL IMPACT AND ADDED VALUE Automating tumor burden assessment with ML could provide surgeons with valuable and consistent support by assessing the PCI more precisely. This technology could help minimize the variability that can arise from subjective scoring, ensuring that each patient's condition is assessed in a standardized way. By reducing subjectivity, this tool could enhance scoring accuracy and offer a reliable measure for tracking disease progression and treatment response over time. Additionally, this automation could improve patient-specific treatment selection. From this perspective, optimal selection is essential to enhance treatment outcomes and avoid exposing patients to high-risk therapies with potential morbidity and limited benefit.