{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T21:11:34Z","timestamp":1793394694700,"version":"4.5.0"},"reference-count":32,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T00:00:00Z","timestamp":1759881600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurol."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Multiple sclerosis is an inflammatory demyelinating disease of the CNS. Annual MRI exams are crucial for disease monitoring. Interpreting high T2\/FLAIR lesion loads can be laborious. AI aids in lesion detection, and choosing between different solutions can be challenging.<\/jats:p><\/jats:sec><jats:sec><jats:title>Aim<\/jats:title><jats:p>This study compares two distinct software, Pixyl.Neuro.MS<jats:sup>\u00ae<\/jats:sup> and Jazz<jats:sup>\u00ae<\/jats:sup>, to assess their performance in T2\/FLAIR lesion detection between two-time points.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Retrospective analysis included follow-up MRIs from 35 MS patients. Pixyl.Neuro.MS<jats:sup>\u00ae<\/jats:sup> automatically segments and classifies lesions. Jazz<jats:sup>\u00ae<\/jats:sup> automates the reading process and image display. Two readers (15 and 4\u202fyears of experience) conducted radiological analysis, followed by AI-assisted readings. A number of new lesions (NL) and reading times were recorded, with ground truth (GT) established by consensus. AI-detected lesions were classified as true (TP) and false positives (FP). Statistical analysis used SPSS (<jats:italic>p<\/jats:italic>\u202f&amp;lt;\u202f0.05).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Pixyl.Neuro.MS<jats:sup>\u00ae<\/jats:sup> readings averaged 2\u202fmin 46\u202fs\u202f\u00b1\u202f1\u202fmin 4\u202fs while using Jazz<jats:sup>\u00ae<\/jats:sup> 3\u202fmin 33\u202fs\u202f\u00b1\u202f2\u202fmin 24\u202fs. Over 50% of the population had a high lesion load (&amp;gt;20 lesions). Both software significantly improved NL detection (<jats:italic>p<\/jats:italic>\u202f&amp;lt;\u202f0.01 for both), revealing them in more patients than standard readings. Standard reports found 8 NL in 2 patients, while AI-assisted readings detected at least 17 TP in 7 patients and rejected 61 FP lesions. GT detected 21 lesions in 19 patients.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Both AI software have been found to enhance NL detection in MS patients, outperforming standard methods. These tools offer crucial advantages for accurate disease monitoring.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fneur.2025.1678073","type":"journal-article","created":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T04:18:03Z","timestamp":1759897083000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Evaluation of two AI techniques for the detection of new T2\/FLAIR lesions in the follow-up of multiple sclerosis 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