Manal Hamdan, Sergio E Uribe, Lyudmila Tuzova, Dmitry Tuzoff, Zaid Badr, André Mol, Donald A Tyndall
{"title":"深度学习工具对口腔颌面放射科医生检测根尖放射线透明的影响。","authors":"Manal Hamdan, Sergio E Uribe, Lyudmila Tuzova, Dmitry Tuzoff, Zaid Badr, André Mol, Donald A Tyndall","doi":"10.1093/dmfr/twae054","DOIUrl":null,"url":null,"abstract":"<p><strong>Title: </strong>The Influence of a Deep Learning Tool on the Performance of Oral and Maxillofacial Radiologists in the Detection of Apical Radiolucencies.</p><p><strong>Objectives: </strong>This study aimed to assess the impact of a deep learning model on oral radiologists' ability to detect periapical radiolucencies on periapical radiographs. The secondary objective was to conduct a regression analysis to evaluate the effects of years of experience, time to diagnose, and specialty.</p><p><strong>Methods: </strong>This study used an annotated dataset and a beta-version of a deep learning model (Denti.AI). The testing subset comprised 68 intraoral periapical radiographs confirmed with cone-beam computed tomography for presence/absence of apical radiolucencies. Four oral radiologists participated in a crossover reading scenario, analyzing the radiographs under two conditions: initially without AI assistance and later with AI predictions. The study evaluated reader performance using AFROC-AUC, sensitivity, specificity, and ROC-AUC per case. It also assessed sensitivity per lesion. Regression analysis investigated how experience, time spent on images, and specialty influenced reader performance.</p><p><strong>Results: </strong>No statistically significant differences were found in AFROC-AUC, sensitivity, specificity, and ROC-AUC. Regression analysis identified factors influencing diagnostic outcomes: unaided reading significantly prolonged diagnostic time (Beta = 12, 95% CI [11, 13], p < 0.001), while radiologists' professional status was positively associated with diagnostic accuracy (Beta = 0.02, 95% CI [0.00, 0.04], p = 0.015). These findings underscore the impact of AI on diagnostic efficiency and the critical role of radiologists' experience in diagnostic accuracy.</p><p><strong>Conclusion: </strong>AI did not significantly enhance radiologists' overall diagnostic accuracy. However, it showed potential to enhance efficiency, particularly advantageous for non-expert clinicians. The expertise of radiologists remains vital for accuracy, underscoring the complementary role of AI in dental diagnostics.</p><p><strong>Advances in knowledge: </strong>AI algorithms may have more notable effects on radiologists' workflow than on the accuracy of detecting apical radiolucencies.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Influence of a Deep Learning Tool on the Performance of Oral and Maxillofacial Radiologists in the Detection of Apical Radiolucencies.\",\"authors\":\"Manal Hamdan, Sergio E Uribe, Lyudmila Tuzova, Dmitry Tuzoff, Zaid Badr, André Mol, Donald A Tyndall\",\"doi\":\"10.1093/dmfr/twae054\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Title: </strong>The Influence of a Deep Learning Tool on the Performance of Oral and Maxillofacial Radiologists in the Detection of Apical Radiolucencies.</p><p><strong>Objectives: </strong>This study aimed to assess the impact of a deep learning model on oral radiologists' ability to detect periapical radiolucencies on periapical radiographs. The secondary objective was to conduct a regression analysis to evaluate the effects of years of experience, time to diagnose, and specialty.</p><p><strong>Methods: </strong>This study used an annotated dataset and a beta-version of a deep learning model (Denti.AI). The testing subset comprised 68 intraoral periapical radiographs confirmed with cone-beam computed tomography for presence/absence of apical radiolucencies. Four oral radiologists participated in a crossover reading scenario, analyzing the radiographs under two conditions: initially without AI assistance and later with AI predictions. The study evaluated reader performance using AFROC-AUC, sensitivity, specificity, and ROC-AUC per case. It also assessed sensitivity per lesion. Regression analysis investigated how experience, time spent on images, and specialty influenced reader performance.</p><p><strong>Results: </strong>No statistically significant differences were found in AFROC-AUC, sensitivity, specificity, and ROC-AUC. Regression analysis identified factors influencing diagnostic outcomes: unaided reading significantly prolonged diagnostic time (Beta = 12, 95% CI [11, 13], p < 0.001), while radiologists' professional status was positively associated with diagnostic accuracy (Beta = 0.02, 95% CI [0.00, 0.04], p = 0.015). These findings underscore the impact of AI on diagnostic efficiency and the critical role of radiologists' experience in diagnostic accuracy.</p><p><strong>Conclusion: </strong>AI did not significantly enhance radiologists' overall diagnostic accuracy. However, it showed potential to enhance efficiency, particularly advantageous for non-expert clinicians. The expertise of radiologists remains vital for accuracy, underscoring the complementary role of AI in dental diagnostics.</p><p><strong>Advances in knowledge: </strong>AI algorithms may have more notable effects on radiologists' workflow than on the accuracy of detecting apical radiolucencies.</p>\",\"PeriodicalId\":11261,\"journal\":{\"name\":\"Dento maxillo facial radiology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2024-12-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Dento maxillo facial radiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1093/dmfr/twae054\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"DENTISTRY, ORAL SURGERY & MEDICINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Dento maxillo facial radiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1093/dmfr/twae054","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"DENTISTRY, ORAL SURGERY & MEDICINE","Score":null,"Total":0}
The Influence of a Deep Learning Tool on the Performance of Oral and Maxillofacial Radiologists in the Detection of Apical Radiolucencies.
Title: The Influence of a Deep Learning Tool on the Performance of Oral and Maxillofacial Radiologists in the Detection of Apical Radiolucencies.
Objectives: This study aimed to assess the impact of a deep learning model on oral radiologists' ability to detect periapical radiolucencies on periapical radiographs. The secondary objective was to conduct a regression analysis to evaluate the effects of years of experience, time to diagnose, and specialty.
Methods: This study used an annotated dataset and a beta-version of a deep learning model (Denti.AI). The testing subset comprised 68 intraoral periapical radiographs confirmed with cone-beam computed tomography for presence/absence of apical radiolucencies. Four oral radiologists participated in a crossover reading scenario, analyzing the radiographs under two conditions: initially without AI assistance and later with AI predictions. The study evaluated reader performance using AFROC-AUC, sensitivity, specificity, and ROC-AUC per case. It also assessed sensitivity per lesion. Regression analysis investigated how experience, time spent on images, and specialty influenced reader performance.
Results: No statistically significant differences were found in AFROC-AUC, sensitivity, specificity, and ROC-AUC. Regression analysis identified factors influencing diagnostic outcomes: unaided reading significantly prolonged diagnostic time (Beta = 12, 95% CI [11, 13], p < 0.001), while radiologists' professional status was positively associated with diagnostic accuracy (Beta = 0.02, 95% CI [0.00, 0.04], p = 0.015). These findings underscore the impact of AI on diagnostic efficiency and the critical role of radiologists' experience in diagnostic accuracy.
Conclusion: AI did not significantly enhance radiologists' overall diagnostic accuracy. However, it showed potential to enhance efficiency, particularly advantageous for non-expert clinicians. The expertise of radiologists remains vital for accuracy, underscoring the complementary role of AI in dental diagnostics.
Advances in knowledge: AI algorithms may have more notable effects on radiologists' workflow than on the accuracy of detecting apical radiolucencies.
期刊介绍：
Dentomaxillofacial Radiology (DMFR) is the journal of the International Association of Dentomaxillofacial Radiology (IADMFR) and covers the closely related fields of oral radiology and head and neck imaging.
Established in 1972, DMFR is a key resource keeping dentists, radiologists and clinicians and scientists with an interest in Head and Neck imaging abreast of important research and developments in oral and maxillofacial radiology.
The DMFR editorial board features a panel of international experts including Editor-in-Chief Professor Ralf Schulze. Our editorial board provide their expertise and guidance in shaping the content and direction of the journal.
Quick Facts:
- 2015 Impact Factor - 1.919
- Receipt to first decision - average of 3 weeks
- Acceptance to online publication - average of 3 weeks
- Open access option
- ISSN: 0250-832X
- eISSN: 1476-542X
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