Artificial Intelligence System for Automatic Quantitative Analysis and Radiology Reporting of Leg Length Radiographs.
Leg length discrepancies are common orthopedic problems with the potential for poor functional outcomes. These are frequently assessed using bilateral leg length radiographs. The objective was to determine whether an artificial intelligence (AI)-based image analysis system can accurately interpret l...
| Publicado en: | Journal of Digital Imaging Vol. 35; no. 6; pp. 1494 - 1506 |
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| Autores principales: | , , , , , , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=160503249&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160503249 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2022 vid: 35 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 160503249 157837904 160503249 160503249 10.1007/s10278-022-00671-2 160503249 ppf: 1494 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Artificial Intelligence System for Automatic Quantitative Analysis and Radiology Reporting of Leg Length Radiographs. aug: au: Larson, Nathan Nguyen, Chantal Do, Bao Kaul, Aryan Larson, Anna Wang, Shannon Wang, Erin Bultman, Eric Stevens, Kate Pai, Jason Ha, Audrey Boutin, Robert Fredericson, Michael Do, Long Fang, Charles affil: Computer Science Department, Brigham Young University, Campus Dr, 3361 TMCB84604, Provo, UT, USA sug: subj: Leg Length Inequality Radiography Artificial Intelligence Lower Extremity Radiography Institutional Review Extremities Radiography Tertiary Health Care Neural Networks (Computer) Specialties, Medical Human Descriptive Statistics Correlation Coefficient Deep Learning Femur Anatomy and Histology Tibia Anatomy and Histology Pelvic Tilt ab: Leg length discrepancies are common orthopedic problems with the potential for poor functional outcomes. These are frequently assessed using bilateral leg length radiographs. The objective was to determine whether an artificial intelligence (AI)-based image analysis system can accurately interpret long leg length radiographic images. We built an end-to-end system to analyze leg length radiographs and generate reports like radiologists, which involves measurement of lengths (femur, tibia, entire leg) and angles (mechanical axis and pelvic tilt), describes presence and location of orthopedic hardware, and reports laterality discrepancies. After IRB approval, a dataset of 1,726 extremities (863 images) from consecutive examinations at a tertiary referral center was retrospectively acquired and partitioned into train/validation and test sets. The training set was annotated and used to train a fasterRCNN-ResNet101 object detection convolutional neural network. A second-stage classifier using a EfficientNet-D0 model was trained to recognize the presence or absence of hardware within extracted joint image patches. The system was deployed in a custom web application that generated a preliminary radiology report. Performance of the system was evaluated using a holdout 220 image test set, annotated by 3 musculoskeletal fellowship trained radiologists. At the object detection level, the system demonstrated a recall of 0.98 and precision of 0.96 in detecting anatomic landmarks. Correlation coefficients between radiologist and AI-generated measurements for femur, tibia, and whole-leg lengths were > 0.99, with mean error of < 1%. Correlation coefficients for mechanical axis angle and pelvic tilt were 0.98 and 0.86, respectively, with mean absolute error of < 1°. AI hardware detection demonstrated an accuracy of 99.8%. Automatic quantitative and qualitative analysis of leg length radiographs using deep learning is feasible and holds potential in improving radiologist workflow. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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