Public articles linked to the same research event.
Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery In this retrospective two-center study of 2 365 patients (1 562 from the First Affiliated Hospital of Sun Yat-sen University and 803 from Ruijin Hospital, Shanghai Jiao Tong University School of Medicine), the authors developed ENDOVISTA-ENT, an integrated AI system with Model 1 for inside/outside-body image determination, Model 2 for recognition of 11 standard anatomical sites, and Model 3 for lesion localization and benign/malignant classification, reporting internal/external accuracies of 99.44%/99.84% and 96.09%/94.73%, malignant-lesion AUCs of 0.986/0.968, early nasopharyngeal, laryngeal, and hypopharyngeal cancer AUCs of 0.878-0.932, an increase in six physicians' overall interpretation accuracy on 200 pathologically confirmed cases from 78.50% to 88.20% with AI assistance (χ²=40.
In this retrospective two-center study of 2 365 patients (1 562 from the First Affiliated Hospital of Sun Yat-sen University and 803 from Ruijin Hospital, Shanghai Jiao Tong University School of Medicine), the authors developed ENDOVISTA-ENT, an integrated AI system with Model 1 for inside/outside-body image determination, Model 2 for recognition of 11 standard anatomical sites, and Model 3 for lesion localization and benign/malignant classification, reporting internal/external accuracies of 99.44%/99.84% and 96.09%/94.73%, malignant-lesion AUCs of 0.986/0.968, early nasopharyngeal, laryngeal, and hypopharyngeal cancer AUCs of 0.878-0.932, an increase in six physicians' overall interpretation accuracy on 200 pathologically confirmed cases from 78.50% to 88.20% with AI assistance (χ²=40.
In this retrospective two-center study of 2 365 patients (1 562 from the First Affiliated Hospital of Sun Yat-sen University and 803 from Ruijin Hospital, Shanghai Jiao Tong University School of Medicine), the authors developed ENDOVISTA-ENT, an integrated AI system with Model 1 for inside/outside-body image determination, Model 2 for recognition of 11 standard anatomical sites, and Model 3 for lesion localization and benign/malignant classification, reporting internal/external accuracies of 99.44%/99.84% and 96.09%/94.73%, malignant-lesion AUCs of 0.986/0.968, early nasopharyngeal, laryngeal, and hypopharyngeal cancer AUCs of 0.878-0.932, an increase in six physicians' overall interpretation accuracy on 200 pathologically confirmed cases from 78.50% to 88.20% with AI assistance (χ²=40.
In this retrospective two-center study of 2 365 patients (1 562 from the First Affiliated Hospital of Sun Yat-sen University and 803 from Ruijin Hospital, Shanghai Jiao Tong University School of Medicine), the authors developed ENDOVISTA-ENT, an integrated AI system with Model 1 for inside/outside-body image determination, Model 2 for recognition of 11 standard anatomical sites, and Model 3 for lesion localization and benign/malignant classification, reporting internal/external accuracies of 99.44%/99.84% and 96.09%/94.73%, malignant-lesion AUCs of 0.986/0.968, early nasopharyngeal, laryngeal, and hypopharyngeal cancer AUCs of 0.878-0.932, an increase in six physicians' overall interpretation accuracy on 200 pathologically confirmed cases from 78.50% to 88.20% with AI assistance (χ²=40.