ChestView
Using AI to improve radiologist performance in detecting abnormalities on chest radiographs
Bennani, Souhail; Regnard, Nor-Eddine; Ventre, Jeanne; Lassalle, Louis; Nguyen, Toan; Ducarouge, Alexis; Dargent, Lucas; Guillo, Enora; Gouhier, Elodie; Zaimi, Sophie-Hélène; Canniff, Emma; Malandrin, Cécile; Khafagy, Philippe; Koulakian, Hasmik; Revel, Marie-Pierre; Chassagnon, Guillaume
Population
500 chest X-rays with CT scanner performed within 72 hours from Hôpital Cochin (AP-HP)
Design
- Gold standard: CT-based annotation by chest radiologist
- Readers: 4 chest radiologists, 4 general radiologists, 4 radiology residents
- MRMC study design: reading with and without AI
Highlights
- AI-assisted chest radiography interpretation resulted in an increased sensitivity of 5.9 to 26.2 % (P<.001) for all readers including thoracic radiologists, general radiologists, and radiology residents.
- General radiologists and radiology residents assisted by AI achieved the performance of chest radiologists without AI
- Mean reading time was 81s without AI vs 56s with AI (-31%, P<.001), with a 17% reduction for radiographs with abnormalities vs 38% for no abnormalities.
Conclusion
AI assistance can improve the detection accuracy of thoracic abnormalities on chest radiographs across radiologists of varying expertise, leading to marked improvements in sensitivity and a reduction in interpretation time.
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Efficacy of a deep learning-based software for chest X-ray analysis in an emergency department.
Sathiyamurthy Selvam, Olivier Peyrony, Arben Elezi, Adelia Braganca, Anne-Marie Zagdanski, Lucie Biard, Jessica Assouline, Guillaume Chassagnon, Guillaume Mulier, Constance de Margerie-Mellon
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Learning from the machine: AI assistance is not an effective learning tool for resident education in chest x-ray interpretation
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AI solution for critical chest pathology