Overview of iDPP@CLEF 2022: The Intelligent Disease Progression Prediction Challenge

ALS is a severe chronic disease characterized by a progressive but variable impairment of neurological functions, characterized by high heterogeneity both in presentation features and rate of disease progression. As a consequence patients’ needs are different, challenging both caregivers and clinicians. Indeed, the time of relevant events is variable, which is associated with uncertainty regarding the opportunity of critical interventions like non-invasive ventilation and gastrostomy, with implications on the quality of life of patients and their caregivers. For this reason, clinicians need tools able to support their decision in all phases of disease progression and underscore personalized therapeutic decisions. The goal of iDPP CLEF is to design and develop an evaluation infrastructure for AI algorithms able to: 1. better indicate intervention time; 2. stratify patients according to their phenotype and rate of disease progression; 3. predict progression rate in a probabilistic, time dependent fashion. The participation in iDPP CLEF was satisfactory, hinting at the interest of the community concerning the task. More so, the solutions identified by participants range over several different techniques and provided valid input to such a highly relevant domain as the prediction of the ALS progression.