{"version":1,"type":"story","url":"https://digestai.news/story/cilia-tracks-model-classifies-primary-ciliary-dyskinesia-with-95-97-ac","json":"https://digestai.news/story/cilia-tracks-model-classifies-primary-ciliary-dyskinesia-with-95-97-ac.json","markdown":"https://digestai.news/story/cilia-tracks-model-classifies-primary-ciliary-dyskinesia-with-95-97-ac.md","slug":"cilia-tracks-model-classifies-primary-ciliary-dyskinesia-with-95-97-ac","headline":"Cilia tracks model classifies primary ciliary dyskinesia with 95-97% accuracy","summary":"The study shows that machine learning can detect primary ciliary dyskinesia (PCD) by analyzing cilia‑driven particle transport. Researchers captured fluorescent microsphere movement on cultured multiciliated cells from 14 PCD patients and 10 healthy donors, producing 602 videos.\n\nThey developed CiliaTracks, an open‑source framework that extracts movement metrics, visualizes transport patterns, and classifies recordings. Classical ML models and a convolutional neural network achieved 95‑97% accuracy in distinguishing PCD from normal samples.\n\nThe authors suggest that this objective, accurate method could serve as a diagnostic tool for PCD, potentially improving early detection. The work was funded by NIH grant #HL173490. No competing interests were declared.","keyPoints":["602 high‑speed videos from 14 PCD patients and 10 healthy donors were analyzed.","CiliaTracks framework extracts microsphere movement metrics and classifies samples with 95‑97% accuracy.","The study proposes machine learning as an objective diagnostic tool for primary ciliary dyskinesia."],"whyItMatters":"By providing an objective, high‑accuracy method to detect PCD, this research could accelerate diagnosis and treatment of a rare genetic disorder. The approach also demonstrates how machine learning can translate detailed cellular imaging into clinically useful diagnostics, potentially inspiring similar tools for other motility‑related diseases.","category":{"slug":"research","name":"Research","url":"https://digestai.news/category/research"},"entities":{"companies":[],"models":["CiliaTracks"],"people":["Hadas, N.","Xu, H.","Neupane, S.","Benjamin Gaston","Michael D Davis","Laura Smith"]},"firstPublishedAt":"2026-10-12T00:00:00Z","updatedAt":"2026-10-12T00:00:00Z","sourceCount":1,"hasPrimarySource":true,"sources":[{"outlet":"Nature Machine Learning","title":"Machine learning analysis of cilia driven particle transport distinguishes primary ciliary dyskinesia from normal cilia","url":"https://nature.com/articles/s42003-026-11149-0","publishedAt":"2026-10-12T00:00:00Z","type":"primary","primary":true,"lead":true}],"sourceNotes":null,"discussions":[],"thread":null,"cite":{"text":"Digest AI, \"Cilia tracks model classifies primary ciliary dyskinesia with 95-97% accuracy\", 12 October 2026, https://digestai.news/story/cilia-tracks-model-classifies-primary-ciliary-dyskinesia-with-95-97-ac","publisher":"Digest AI","title":"Cilia tracks model classifies primary ciliary dyskinesia with 95-97% accuracy","datePublished":"2026-10-12T00:00:00Z","url":"https://digestai.news/story/cilia-tracks-model-classifies-primary-ciliary-dyskinesia-with-95-97-ac"},"generatedBy":"Written by Digest AI's editorial model from the linked sources; the sources are the record.","license":"Headlines, digests and key points are written by Digest AI and may be quoted with a link to the story page. Linked articles belong to their publishers. Terms: https://digestai.news/terms#reuse"}