AI-Augmented Pathology for Head and Neck Squamous Lesions Improves Non-HN Pathologist Agreement to Expert Level - Mines Paris Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2023

AI-Augmented Pathology for Head and Neck Squamous Lesions Improves Non-HN Pathologist Agreement to Expert Level

Yaëlle Bellahsen-Harrar
Charles Lépine
  • Fonction : Auteur
Aurélie Beaufrère
  • Fonction : Auteur
Claire Bocciarelli
  • Fonction : Auteur
Anaïs Brunet
  • Fonction : Auteur
Elise Decroix
  • Fonction : Auteur
Franck Neil El-Sissy
  • Fonction : Auteur
Bettina Fabiani
  • Fonction : Auteur
Aurélien Morini
  • Fonction : Auteur
Cyprien Tilmant
  • Fonction : Auteur
Cécile Badoual
  • Fonction : Auteur

Résumé

Abstract Importance Diagnosis of head and neck squamous dysplasias and carcinomas is challenging, with a moderate inter-rater agreement. Nowadays, new artificial intelligence (AI) models are developed to automatically detect and grade lesions, but their contribution to the performance of pathologists hasn’t been assessed. Objective To evaluate the contribution of our AI tool in assisting pathologists in diagnosing squamous dysplasia and carcinoma in the head and neck region. Design, Setting, and Participants We evaluated the effectiveness of our previously described AI model, which combines an automatic classification of laryngeal and pharyngeal squamous lesions with a confidence score, on a panel of eight pathologists coming from different backgrounds and with different levels of experience on a subset of 115 slides. Main Outcomes and Measures The main outcome was the inter-rater agreement, measured by the weighted linear kappa. Other outcomes on diagnostic efficiency were assessed using paired t tests. Results AI-Assistance significantly improved the inter-rater agreement (linear kappa 0.73, 95%CI [0.711-0.748] with assistance versus 0.675, 95%CI [0.579-0.765] without assistance, p < 0.001). The agreement was even better on high confidence predictions (mean linear kappa 0.809, 95%CI [0.784-0.834] for assisted review, versus 0.731, 95%CI [0.681-0.781] non-assisted, p = 0.018). These improvements were particularly strong for non-specialized and younger pathologists. Hence, the AI-Assistance enabled the panel to perform on par with the expert panel described in the literature. Conclusions and Relevance Our AI-Assistance is of great value for helping pathologists in the difficult task of diagnosing squamous dysplasias and carcinomas, improving for the first time the inter-rater agreement. It demonstrates the possibility of a truly Augmented Pathology in complex tasks such as the classification of head and neck squamous lesions.
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Dates et versions

hal-04396487 , version 1 (16-01-2024)

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Yaëlle Bellahsen-Harrar, Mélanie Lubrano, Charles Lépine, Aurélie Beaufrère, Claire Bocciarelli, et al.. AI-Augmented Pathology for Head and Neck Squamous Lesions Improves Non-HN Pathologist Agreement to Expert Level. 2024. ⟨hal-04396487⟩
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