{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T17:19:44Z","timestamp":1792689584316,"version":"4.5.0"},"reference-count":35,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T00:00:00Z","timestamp":1730678400000},"content-version":"vor","delay-in-days":64,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["21K09098"],"award-info":[{"award-number":["21K09098"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["22H03020"],"award-info":[{"award-number":["22H03020"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,9,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Disproportionately enlarged subarachnoid-space hydrocephalus (DESH) is a key feature of Hakim\u2019s disease (synonymous with idiopathic normal pressure hydrocephalus; iNPH). However, it previously had been only subjectively evaluated.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Purpose<\/jats:title>\n                  <jats:p>This study aims to evaluate the usefulness of MRI indices, derived from deep learning segmentation of cerebrospinal fluid (CSF) spaces, for DESH detection and to establish their optimal thresholds.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Materials and Methods<\/jats:title>\n                  <jats:p>This study retrospectively enrolled a total of 1009 participants, including 77 patients diagnosed with Hakim\u2019s disease, 380 healthy volunteers, 163 with mild cognitive impairment, 256 with Alzheimer\u2019s disease, and 217 with other types of neurodegenerative diseases. DESH, ventriculomegaly, tightened sulci in the high convexities, and Sylvian fissure dilatation were evaluated on three-dimensional T1-weighted MRI by radiologists. The total ventricles, high-convexity part of the subarachnoid space, and Sylvian fissure and basal cistern were automatically segmented using the CSF Space Analysis application (FUJIFILM Corporation). Moreover, DESH, Venthi, and Sylhi indices were calculated based on these 3 regions. The area under the receiver-operating characteristic curves of these indices and region volumes (volume ratios) for DESH detection were calculated.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Of the 1009 participants, 101 (10%) presented with DESH. The DESH, Venthi, and Sylhi indices performed well with 95.0%-96.0% sensitivity and 91.5%-96.8% specificity at optimal thresholds. All patients with Hakim\u2019s disease were diagnosed with DESH, despite variations in severity. In patients with Hakim\u2019s disease, with or without Alzheimer\u2019s disease, the DESH index and total ventricular volume were significantly higher compared to patients with Alzheimer\u2019s disease, although the total intracranial cerebrospinal fluid volume was significantly lower.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>DESH, Venthi, and Sylhi indices, and the volumes and volume ratios of the ventricle and high-convexity part of the subarachnoid space computed using deep learning were useful for the DESH detection that may help to improve the diagnosis of Hakim\u2019s disease (ie, iNPH).<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/radadv\/umae027","type":"journal-article","created":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T15:00:16Z","timestamp":1730732416000},"source":"Crossref","is-referenced-by-count":9,"title":["Deep learning assessment of 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107-0052,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chifumi","family":"Iseki","sequence":"additional","affiliation":[{"name":"Department of Behavioral Neurology and Cognitive Neuroscience, Tohoku University Graduate School of Medicine , Sendai, Miyagi, 980-8575,","place":["Japan"]},{"name":"Division of Neurology and Clinical Neuroscience, Department of Internal Medicine III, Yamagata University School of Medicine , Yamagata, Yamagata, 990-2331,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Toshiyuki","family":"Kondo","sequence":"additional","affiliation":[{"name":"Division of Neurology and Clinical Neuroscience, Department of Internal Medicine III, Yamagata University School of Medicine , Yamagata, Yamagata, 990-2331,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomoyasu","family":"Yamanaka","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Nagoya City University Graduate School of Medical Science , Nagoya, Aichi, 467-8601,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Motoki","family":"Tanikawa","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Nagoya City University Graduate School of Medical Science , Nagoya, Aichi, 467-8601,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6431-2703","authenticated-orcid":false,"given":"Tomohiro","family":"Otani","sequence":"additional","affiliation":[{"name":"Department of Mechanical Science and Bioengineering, Graduate School of Engineering Science, Osaka University , Toyonaka, Osaka, 560-8531,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Satoshi","family":"Ii","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, School of Engineering, Institute of Science Tokyo , Tokyo, 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