Peyman Tahghighi, Ryan B. Appleby, Nicole Norena, Eran Ukwatta, Amin Komeili
{"title":"Classification of the quality of canine and feline ventrodorsal and dorsoventral thoracic radiographs through machine learning","authors":"Peyman Tahghighi, Ryan B. Appleby, Nicole Norena, Eran Ukwatta, Amin Komeili","doi":"10.1111/vru.13373","DOIUrl":null,"url":null,"abstract":"Thoracic radiographs are an essential diagnostic tool in companion animal medicine and are frequently used as a part of routine workups in patients presenting for coughing, respiratory distress, cardiovascular diseases, and for staging of neoplasia. Quality control is a critical aspect of radiology practice in preventing misdiagnosis and ensuring consistent, accurate, and reliable diagnostic imaging. Implementing an effective quality control procedure in radiology can impact patient outcomes, facilitate clinical decision‐making, and decrease healthcare costs. In this study, a machine learning‐based quality classification model is suggested for canine and feline thoracic radiographs captured in both ventrodorsal and dorsoventral positions. The problem of quality classification was divided into collimation, positioning, and exposure, and then an automatic classification method was proposed for each based on deep learning and machine learning. We utilized a dataset of 899 radiographs of dogs and cats. Evaluations using fivefold cross‐validation resulted in an F1 score and AUC score of 91.33 (95% CI: 88.37–94.29) and 91.10 (95% CI: 88.16–94.03), respectively. Results indicated that the proposed automatic quality classification has the potential to be implemented in radiology clinics to improve radiograph quality and reduce nondiagnostic images.","PeriodicalId":23581,"journal":{"name":"Veterinary Radiology & Ultrasound","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Veterinary Radiology & Ultrasound","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1111/vru.13373","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"VETERINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Thoracic radiographs are an essential diagnostic tool in companion animal medicine and are frequently used as a part of routine workups in patients presenting for coughing, respiratory distress, cardiovascular diseases, and for staging of neoplasia. Quality control is a critical aspect of radiology practice in preventing misdiagnosis and ensuring consistent, accurate, and reliable diagnostic imaging. Implementing an effective quality control procedure in radiology can impact patient outcomes, facilitate clinical decision‐making, and decrease healthcare costs. In this study, a machine learning‐based quality classification model is suggested for canine and feline thoracic radiographs captured in both ventrodorsal and dorsoventral positions. The problem of quality classification was divided into collimation, positioning, and exposure, and then an automatic classification method was proposed for each based on deep learning and machine learning. We utilized a dataset of 899 radiographs of dogs and cats. Evaluations using fivefold cross‐validation resulted in an F1 score and AUC score of 91.33 (95% CI: 88.37–94.29) and 91.10 (95% CI: 88.16–94.03), respectively. Results indicated that the proposed automatic quality classification has the potential to be implemented in radiology clinics to improve radiograph quality and reduce nondiagnostic images.
期刊介绍:
Veterinary Radiology & Ultrasound is a bimonthly, international, peer-reviewed, research journal devoted to the fields of veterinary diagnostic imaging and radiation oncology. Established in 1958, it is owned by the American College of Veterinary Radiology and is also the official journal for six affiliate veterinary organizations. Veterinary Radiology & Ultrasound is represented on the International Committee of Medical Journal Editors, World Association of Medical Editors, and Committee on Publication Ethics.
The mission of Veterinary Radiology & Ultrasound is to serve as a leading resource for high quality articles that advance scientific knowledge and standards of clinical practice in the areas of veterinary diagnostic radiology, computed tomography, magnetic resonance imaging, ultrasonography, nuclear imaging, radiation oncology, and interventional radiology. Manuscript types include original investigations, imaging diagnosis reports, review articles, editorials and letters to the Editor. Acceptance criteria include originality, significance, quality, reader interest, composition and adherence to author guidelines.