M. Pérez Laencina , J.M. Plasencia Martínez , M. Sánchez Canales , C. Jiménez Pulido , R. Rodríguez Mondéjar , L. Martínez Encarnación , C. García Hidalgo , D. Galdo Galián , P. Hernández Madrid , L. Chico Caballero , E. Guillén García , M.N. Plasencia Martínez , S. Martínez Romero , J. García Molina , J.M. García Santos
{"title":"对 COVID-19 使用未经培训的商业人工智能工具可略微提高对 COVID-19 肺炎影像的判读,尤其是对缺乏经验的读者而言。","authors":"M. Pérez Laencina , J.M. Plasencia Martínez , M. Sánchez Canales , C. Jiménez Pulido , R. Rodríguez Mondéjar , L. Martínez Encarnación , C. García Hidalgo , D. Galdo Galián , P. Hernández Madrid , L. Chico Caballero , E. Guillén García , M.N. Plasencia Martínez , S. Martínez Romero , J. García Molina , J.M. García Santos","doi":"10.1016/j.rx.2024.01.007","DOIUrl":null,"url":null,"abstract":"<div><h3>Introduction</h3><div>Our objective is to evaluate how useful an artificial intelligence (AI) tool is to chest radiograph readers with various levels of expertise for the diagnosis of COVID-19 pneumonia when the tool has been trained on a non-COVID-19 pneumonia pathology.</div></div><div><h3>Methods</h3><div>Data was collected for patients who had previously undergone a chest radiograph and digital tomosynthesis due to suspected COVID-19 pneumonia. The gold standard consisted of the readings of two expert radiologists who assessed the presence and distribution of COVID-19 pneumonia on the images. Six medical students, two radiology trainees, and two other expert thoracic radiologists participated as additional readers. Two radiograph readings and a third supported by the AI Thoracic Care Suite tool were performed. COVID-19 pneumonia distribution and probability were assessed along with the contribution made by AI. Agreement and diagnostic performance were analysed.</div></div><div><h3>Results</h3><div>The sample consisted of 113 cases, of which 56 displayed lung opacities, 52.2% were female, and the mean age was 50.70<!--> <!-->±<!--> <!-->14.9. Agreement with the gold standard differed between students, trainees, and radiologists. There was a non-significant improvement for four of the six students when AI was used. The use of AI by students did not improve the COVID-19 pneumonia diagnostic performance but it did reduce the difference in diagnostic performance with the more expert radiologists. Furthermore, it had more influence on the interpretation of mild pneumonia than severe pneumonia and normal radiograph findings. AI resolved more doubts than it generated, especially among students (31.30 vs 8.32%), followed by trainees (14.45 vs 5.7%) and radiologists (10.05% vs 6.15%).</div></div><div><h3>Conclusion</h3><div>For expert and lesser experienced radiologists, this commercial AI tool has shown no impact on chest radiograph readings of patients with suspected COVID-19 pneumonia. However, it aided the assessment of inexperienced readers and in cases of mild pneumonia.</div></div>","PeriodicalId":31509,"journal":{"name":"RADIOLOGIA","volume":"67 3","pages":"Pages 273-286"},"PeriodicalIF":1.1000,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Usar una herramienta comercial de inteligencia artificial no entrenada para COVID-19 mejora ligeramente la interpretación de las radiografías con neumonía COVID-19, especialmente entre lectores inexpertos\",\"authors\":\"M. Pérez Laencina , J.M. Plasencia Martínez , M. Sánchez Canales , C. Jiménez Pulido , R. Rodríguez Mondéjar , L. Martínez Encarnación , C. García Hidalgo , D. Galdo Galián , P. Hernández Madrid , L. Chico Caballero , E. Guillén García , M.N. Plasencia Martínez , S. Martínez Romero , J. García Molina , J.M. García Santos\",\"doi\":\"10.1016/j.rx.2024.01.007\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Introduction</h3><div>Our objective is to evaluate how useful an artificial intelligence (AI) tool is to chest radiograph readers with various levels of expertise for the diagnosis of COVID-19 pneumonia when the tool has been trained on a non-COVID-19 pneumonia pathology.</div></div><div><h3>Methods</h3><div>Data was collected for patients who had previously undergone a chest radiograph and digital tomosynthesis due to suspected COVID-19 pneumonia. The gold standard consisted of the readings of two expert radiologists who assessed the presence and distribution of COVID-19 pneumonia on the images. Six medical students, two radiology trainees, and two other expert thoracic radiologists participated as additional readers. Two radiograph readings and a third supported by the AI Thoracic Care Suite tool were performed. COVID-19 pneumonia distribution and probability were assessed along with the contribution made by AI. Agreement and diagnostic performance were analysed.</div></div><div><h3>Results</h3><div>The sample consisted of 113 cases, of which 56 displayed lung opacities, 52.2% were female, and the mean age was 50.70<!--> <!-->±<!--> <!-->14.9. Agreement with the gold standard differed between students, trainees, and radiologists. There was a non-significant improvement for four of the six students when AI was used. The use of AI by students did not improve the COVID-19 pneumonia diagnostic performance but it did reduce the difference in diagnostic performance with the more expert radiologists. Furthermore, it had more influence on the interpretation of mild pneumonia than severe pneumonia and normal radiograph findings. AI resolved more doubts than it generated, especially among students (31.30 vs 8.32%), followed by trainees (14.45 vs 5.7%) and radiologists (10.05% vs 6.15%).</div></div><div><h3>Conclusion</h3><div>For expert and lesser experienced radiologists, this commercial AI tool has shown no impact on chest radiograph readings of patients with suspected COVID-19 pneumonia. 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Usar una herramienta comercial de inteligencia artificial no entrenada para COVID-19 mejora ligeramente la interpretación de las radiografías con neumonía COVID-19, especialmente entre lectores inexpertos
Introduction
Our objective is to evaluate how useful an artificial intelligence (AI) tool is to chest radiograph readers with various levels of expertise for the diagnosis of COVID-19 pneumonia when the tool has been trained on a non-COVID-19 pneumonia pathology.
Methods
Data was collected for patients who had previously undergone a chest radiograph and digital tomosynthesis due to suspected COVID-19 pneumonia. The gold standard consisted of the readings of two expert radiologists who assessed the presence and distribution of COVID-19 pneumonia on the images. Six medical students, two radiology trainees, and two other expert thoracic radiologists participated as additional readers. Two radiograph readings and a third supported by the AI Thoracic Care Suite tool were performed. COVID-19 pneumonia distribution and probability were assessed along with the contribution made by AI. Agreement and diagnostic performance were analysed.
Results
The sample consisted of 113 cases, of which 56 displayed lung opacities, 52.2% were female, and the mean age was 50.70 ± 14.9. Agreement with the gold standard differed between students, trainees, and radiologists. There was a non-significant improvement for four of the six students when AI was used. The use of AI by students did not improve the COVID-19 pneumonia diagnostic performance but it did reduce the difference in diagnostic performance with the more expert radiologists. Furthermore, it had more influence on the interpretation of mild pneumonia than severe pneumonia and normal radiograph findings. AI resolved more doubts than it generated, especially among students (31.30 vs 8.32%), followed by trainees (14.45 vs 5.7%) and radiologists (10.05% vs 6.15%).
Conclusion
For expert and lesser experienced radiologists, this commercial AI tool has shown no impact on chest radiograph readings of patients with suspected COVID-19 pneumonia. However, it aided the assessment of inexperienced readers and in cases of mild pneumonia.
RADIOLOGIARADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
1.60
自引率
7.70%
发文量
105
审稿时长
52 days
期刊介绍:
La mejor revista para conocer de primera mano los originales más relevantes en la especialidad y las revisiones, casos y notas clínicas de mayor interés profesional. Además es la Publicación Oficial de la Sociedad Española de Radiología Médica.