{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T16:05:32Z","timestamp":1753891532898,"version":"3.41.2"},"reference-count":30,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"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>Objectives<\/jats:title><jats:p>The diagnosis of intracranial atherosclerotic stenosis (ICAS) is of great significance for the prevention of stroke. Deep learning (DL)-based artificial intelligence techniques may aid in the diagnosis. The study aimed to identify ICAS in the middle cerebral artery (MCA) based on a modified DL model.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This retrospective study included two datasets. Dataset1 consisted of 3,068 transcranial Doppler (TCD) images of the MCA from 1,729 patients, which were assessed as normal or stenosis by three physicians with varying levels of experience, in conjunction with other medical imaging data. The data were used to improve and train the VGG16 models. Dataset2 consisted of TCD images of 90 people who underwent physical examination, which were used to verify the robustness of the model and compare the consistency between the model and human physicians.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The accuracy, precision, specificity, sensitivity, and area under curve (AUC) of the best model VGG16\u2009+\u2009Squeeze-and-Excitation (SE)\u2009+\u2009skip connection (SC) on dataset1 reached 85.67\u2009\u00b1\u20090.43(%),87.23\u2009\u00b1\u20091.17(%),87.73\u2009\u00b1\u20091.47(%),83.60\u2009\u00b1\u20091.60(%), and 0.857\u2009\u00b1\u20090.004, while those of dataset2 were 93.70\u2009\u00b1\u20092.80(%),62.65\u2009\u00b1 11.27(%),93.00\u2009\u00b1\u20093.11(%),100.00\u2009\u00b1\u20090.00(%), and 0.965\u2009\u00b1\u20090.016. The kappa coefficient showed that it reached the recognition level of senior doctors.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The improved DL model has a good diagnostic effect for MCV stenosis in TCD images and is expected to help in ICAS screening.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fneur.2024.1394435","type":"journal-article","created":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T04:51:52Z","timestamp":1729054312000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Identification of middle cerebral artery stenosis in transcranial Doppler using a modified VGG-16"],"prefix":"10.3389","volume":"15","author":[{"given":"Dong","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fanghui","family":"Su","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sizheng","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huixia","family":"Tong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meifeng","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianzhong","family":"Yao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2024,10,16]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"e46","DOI":"10.1161\/STR.0000000000000158","article-title":"2018 guidelines for the early Management of Patients with Acute Ischemic Stroke: a guideline for healthcare professionals from the American Heart Association\/American Stroke Association","volume":"49","author":"Powers","year":"2018","journal-title":"Stroke"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1016\/S1474-4422(21)00376-8","article-title":"Intracranial atherosclerotic stenosis: risk factors, diagnosis, and treatment","volume":"21","author":"Gutierrez","year":"2022","journal-title":"Lancet Neurol"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"CD010722","DOI":"10.1002\/14651858.CD010722.pub2","article-title":"Transcranial Doppler sonography for detecting stenosis or occlusion of intracranial arteries in people with acute ischaemic stroke","volume":"2","author":"Mattioni","year":"2020","journal-title":"Cochrane Database Syst Rev"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"181","DOI":"10.4103\/bc.bc_21_20","article-title":"Transcranial Doppler and magnetic resonance angiography assessment of intracranial stenosis: an analysis of screening modalities","volume":"6","author":"Mowla","year":"2020","journal-title":"Brain Circ"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"f6804","DOI":"10.1136\/bmj.f6804","article-title":"Shortage of health workers is set to double, says WHO","volume":"347","author":"Gulland","year":"2013","journal-title":"BMJ"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-33128-3_1","article-title":"Deep learning in medical image analysis","volume":"1213","author":"Chan","year":"2020","journal-title":"Adv Exp Med Biol"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"101940","DOI":"10.1016\/j.compmedimag.2021.101940","article-title":"Role of deep learning in brain tumor detection and classification (2015 to 2020): a review","volume":"91","author":"Nazir","year":"2021","journal-title":"Comput Med Imaging Graph"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"795","DOI":"10.3390\/diagnostics12040795","article-title":"An embedded system using convolutional neural network model for online and real-time ECG signal classification and prediction","volume":"12","author":"Caesarendra","year":"2022","journal-title":"Diagnostics"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"adv00532","DOI":"10.2340\/00015555-3893","article-title":"A deep learning approach for histopathological diagnosis of onychomycosis: not inferior to analogue diagnosis by Histopathologists","volume":"101","author":"Decroos","year":"2021","journal-title":"Acta Derm Venereol"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1038\/s41571-019-0252-y","article-title":"Artificial intelligence in digital pathology - 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