Deep Learning–Assisted Identification of Femoroacetabular Impingement (FAI) on Routine Pelvic Radiographs.
To use a novel deep learning system to localize the hip joints and detect findings of cam-type femoroacetabular impingement (FAI). A retrospective search of hip/pelvis radiographs obtained in patients to evaluate for FAI yielded 3050 total studies. Each hip was classified separately by the original...
| Published in: | Journal of Digital Imaging Vol. 37; no. 1; pp. 339 - 347 |
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| Main Authors: | , , , , , |
| Format: | diagnostic images research tables/charts Journal Article |
| Published: |
Springer Nature
Feb2024
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=175966512&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 175966512 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2024 vid: 37 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 175966512 175966512 175966512 10.1007/s10278-023-00920-y 175966512 ppf: 339 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep Learning–Assisted Identification of Femoroacetabular Impingement (FAI) on Routine Pelvic Radiographs. aug: au: Hoy, Michael K. Desai, Vishal Mutasa, Simukayi Hoy, Robert C. Gorniak, Richard Belair, Jeffrey A. affil: https://ror.org/00ysqcn41 Thomas Jefferson University, Philadelphia, PA, USA sug: subj: Femoracetabular Impingement Diagnosis Pelvic Bones Radiography Tomography, X-Ray Computed Methods Diagnostic Tests, Routine Deep Learning Human Descriptive Statistics Data Analysis Software Case Control Studies Retrospective Design Health Insurance Portability and Accountability Act Neural Networks (Computer) Machine Learning Male Female Child Adolescence Young Adult Adult Middle Age Aged Aged, 80 and Over Child: 6-12 years Adolescent: 13-18 years Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Male Female ab: To use a novel deep learning system to localize the hip joints and detect findings of cam-type femoroacetabular impingement (FAI). A retrospective search of hip/pelvis radiographs obtained in patients to evaluate for FAI yielded 3050 total studies. Each hip was classified separately by the original interpreting radiologist in the following manner: 724 hips had severe cam-type FAI morphology, 962 moderate cam-type FAI morphology, 846 mild cam-type FAI morphology, and 518 hips were normal. The anteroposterior (AP) view from each study was anonymized and extracted. After localization of the hip joints by a novel convolutional neural network (CNN) based on the focal loss principle, a second CNN classified the images of the hip as cam positive, or no FAI. Accuracy was 74% for diagnosing normal vs. abnormal cam-type FAI morphology, with aggregate sensitivity and specificity of 0.821 and 0.669, respectively, at the chosen operating point. The aggregate AUC was 0.736. A deep learning system can be applied to detect FAI-related changes on single view pelvic radiographs. Deep learning is useful for quickly identifying and categorizing pathology on imaging, which may aid the interpreting radiologist. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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