{"title":"磁共振波谱(MRS)转化多发性硬化症(MS)诊断","authors":"Landoline Bonnin , Pascal Bourdon , Carole Guillevin , Remy Guillevin , Clement Giraud , Christine Fernandez-Maloigne","doi":"10.1016/j.sctalk.2025.100427","DOIUrl":null,"url":null,"abstract":"<div><div>Anna is one of the 1.8 million people worldwide with multiple sclerosis who live with the uncertainty of disease progression every day [<span><span>1</span></span>]. Traditional Magnetic Resonance Imaging scans every six months reveal brain lesions but can't predict how the disease will progress [<span><span>2</span></span>]. A new technology, Magnetic Resonance Spectroscopy (MRS), shows promise in predicting disease progression by revealing cerebral metabolism and neurophysiological changes [<span><span>3</span></span>]. However, current MRS measurement methods vary between medical centers, affecting reliability [<span><span>[4]</span></span>, <span><span>[5]</span></span>, <span><span>[6]</span></span>]. Standardizing these measurements using Physics-Informed Neural Networks (PINNs), which are more reliable than traditional neural networks because they are based on the physics of spectra, could ensure accurate, comparable results worldwide [<span><span>[7]</span></span>, <span><span>[8]</span></span>, <span><span>[9]</span></span>]. This would reassure doctors and patients like Anna, and potentially improve their quality of life by enabling earlier and more precise treatment.</div></div>","PeriodicalId":101148,"journal":{"name":"Science Talks","volume":"13 ","pages":"Article 100427"},"PeriodicalIF":0.0000,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Magnetic Resonance Spectroscopy (MRS) transforming multiple sclerosis (MS) diagnosis\",\"authors\":\"Landoline Bonnin , Pascal Bourdon , Carole Guillevin , Remy Guillevin , Clement Giraud , Christine Fernandez-Maloigne\",\"doi\":\"10.1016/j.sctalk.2025.100427\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Anna is one of the 1.8 million people worldwide with multiple sclerosis who live with the uncertainty of disease progression every day [<span><span>1</span></span>]. Traditional Magnetic Resonance Imaging scans every six months reveal brain lesions but can't predict how the disease will progress [<span><span>2</span></span>]. A new technology, Magnetic Resonance Spectroscopy (MRS), shows promise in predicting disease progression by revealing cerebral metabolism and neurophysiological changes [<span><span>3</span></span>]. However, current MRS measurement methods vary between medical centers, affecting reliability [<span><span>[4]</span></span>, <span><span>[5]</span></span>, <span><span>[6]</span></span>]. Standardizing these measurements using Physics-Informed Neural Networks (PINNs), which are more reliable than traditional neural networks because they are based on the physics of spectra, could ensure accurate, comparable results worldwide [<span><span>[7]</span></span>, <span><span>[8]</span></span>, <span><span>[9]</span></span>]. This would reassure doctors and patients like Anna, and potentially improve their quality of life by enabling earlier and more precise treatment.</div></div>\",\"PeriodicalId\":101148,\"journal\":{\"name\":\"Science Talks\",\"volume\":\"13 \",\"pages\":\"Article 100427\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Science Talks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S277256932500009X\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/27 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Science Talks","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S277256932500009X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/27 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
Magnetic Resonance Spectroscopy (MRS) transforming multiple sclerosis (MS) diagnosis
Anna is one of the 1.8 million people worldwide with multiple sclerosis who live with the uncertainty of disease progression every day [1]. Traditional Magnetic Resonance Imaging scans every six months reveal brain lesions but can't predict how the disease will progress [2]. A new technology, Magnetic Resonance Spectroscopy (MRS), shows promise in predicting disease progression by revealing cerebral metabolism and neurophysiological changes [3]. However, current MRS measurement methods vary between medical centers, affecting reliability [[4], [5], [6]]. Standardizing these measurements using Physics-Informed Neural Networks (PINNs), which are more reliable than traditional neural networks because they are based on the physics of spectra, could ensure accurate, comparable results worldwide [[7], [8], [9]]. This would reassure doctors and patients like Anna, and potentially improve their quality of life by enabling earlier and more precise treatment.