Momodou L Sonko, T Campbell Arnold, Ivan A Kuznetsov
{"title":"机器学习在护理超声中的应用。","authors":"Momodou L Sonko, T Campbell Arnold, Ivan A Kuznetsov","doi":"10.24908/pocus.v7iKidney.15345","DOIUrl":null,"url":null,"abstract":"When a patient presents to the ED, clinicians often turn to medical imaging to better understand their condition. Traditionally, imaging is collected from the patient and interpreted by a radiologist remotely. However, scanning devices are increasingly equipped with analytical software that can provide quantitative assessments at the patient’s bedside. These assessments often rely on machine learning algorithms as a means of interpreting medical images.","PeriodicalId":74470,"journal":{"name":"POCUS journal","volume":"7 Kidney","pages":"78-87"},"PeriodicalIF":0.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9994292/pdf/","citationCount":"2","resultStr":"{\"title\":\"Machine Learning in Point of Care Ultrasound.\",\"authors\":\"Momodou L Sonko, T Campbell Arnold, Ivan A Kuznetsov\",\"doi\":\"10.24908/pocus.v7iKidney.15345\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"When a patient presents to the ED, clinicians often turn to medical imaging to better understand their condition. Traditionally, imaging is collected from the patient and interpreted by a radiologist remotely. However, scanning devices are increasingly equipped with analytical software that can provide quantitative assessments at the patient’s bedside. These assessments often rely on machine learning algorithms as a means of interpreting medical images.\",\"PeriodicalId\":74470,\"journal\":{\"name\":\"POCUS journal\",\"volume\":\"7 Kidney\",\"pages\":\"78-87\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9994292/pdf/\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"POCUS journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.24908/pocus.v7iKidney.15345\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"POCUS journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.24908/pocus.v7iKidney.15345","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
When a patient presents to the ED, clinicians often turn to medical imaging to better understand their condition. Traditionally, imaging is collected from the patient and interpreted by a radiologist remotely. However, scanning devices are increasingly equipped with analytical software that can provide quantitative assessments at the patient’s bedside. These assessments often rely on machine learning algorithms as a means of interpreting medical images.