Deep learning for freezing of gait assessment using inertial measurement units: a multicentre validation study

Abstract Video annotation is the gold standard for assessing Freezing of Gait (FOG) in Parkinsonian disorders, but it is time-consuming. Deep learning (DL)-based assessment of FOG using inertial measurement units ameliorates this problem but poses challenges. Particularly, the large heterogeneity between patients and assessment methods potentially affects detection performance between independent cohorts. To evaluate heterogeneity effects, we developed a DL model on a local cohort (85 participants; 2043 trials) and validated it across six external cohorts (256 participants; 1058 trials). Model-expert agreement on the percentage-of-time-frozen was strong locally (ICC = 0.886 [0.79, 0.90]) but reduced in external cohorts (ICC = 0.562 ± 0.141). Fine-tuning the DL model with just 50 min of external cohort data improved the ICC to 0.732 ± 0.138, approaching the lower boundary of the inter-rater agreement between two clinical raters using video annotation (ICC = 0.73–0.99). Therefore, while unified standards are still being developed, we propose a human-in-the-loop workflow as an effective intermediary and present a proof-of-concept web-based platform for fine-tuning and expert review ( aidfog.be ).