Méthodes d'apprentissage automatisé pour l'amélioration du diagnostic des cancers épidermoïdes du larynx à partir d'images histologiques

This thesis explores the use of computational pathology to evaluate and grade precancerous and cancerous lesions of the larynx, a condition subject to subjective classification by pathologists. By using Al models and a vast database of histological images, we have developed diagnostic assistive tools that enhance the inter and intra observer reproducibility, thereby improvings patient care.Cette thèse explore l'utilisation de la pathologie computationnelle pour évaluer et grader les lésions précancéreuses et cancéreuses du larynx, une condition sujette à une classification subjective de la part des pathologistes. En utilisant des modèles d'IA et une vaste base de données d'images histologiques, nous avons développé des outils d'aide au diagnostic améliorant la reproductibilité inter et intra observateurs, permettant ainsi une meilleure prise en charge des patients

Méthodes d'apprentissage automatisé pour l'amélioration du diagnostic des cancers épidermoïdes du larynx à partir d'images histologiques

This thesis explores the use of computational pathology to evaluate and grade precancerous and cancerous lesions of the larynx, a condition subject to subjective classification by pathologists. By using Al models and a vast database of histological images, we have developed diagnostic assistive tools that enhance the inter and intra observer reproducibility, thereby improvings patient care.Cette thèse explore l'utilisation de la pathologie computationnelle pour évaluer et grader les lésions précancéreuses et cancéreuses du larynx, une condition sujette à une classification subjective de la part des pathologistes. En utilisant des modèles d'IA et une vaste base de données d'images histologiques, nous avons développé des outils d'aide au diagnostic améliorant la reproductibilité inter et intra observateurs, permettant ainsi une meilleure prise en charge des patients

Simple and Efficient Confidence Score for Grading Whole Slide Images

Grading precancerous lesions on whole slide images is a challenging task: the continuous space of morphological phenotypes makes clear-cut decisions between different grades often difficult, leading to low inter- and intra-rater agreements. More and more Artificial Intelligence (AI) algorithms are developed to help pathologists perform and standardize their diagnosis. However, those models can render their prediction without consideration of the ambiguity of the classes and can fail without notice which prevent their wider acceptance in a clinical context. In this paper, we propose a new score to measure the confidence of AI models in grading tasks. Our confidence score is specifically adapted to ordinal output variables, is versatile and does not require extra training or additional inferences nor particular architecture changes. Comparison to other popular techniques such as Monte Carlo Dropout and deep ensembles shows that our method provides state-of-the art results, while being simpler, more versatile and less computationally intensive. The score is also easily interpretable and consistent with real life hesitations of pathologists. We show that the score is capable of accurately identifying mispredicted slides and that accuracy for high confidence decisions is significantly higher than for low-confidence decisions (gap in AUC of 17.1% on the test set). We believe that the proposed confidence score could be leveraged by pathologists directly in their workflow and assist them on difficult tasks such as grading precancerous lesions

Diagnosis with Confidence: Deep Learning for Reliable Classification of Squamous Lesions of the Upper Aerodigestive Tract

Abstract Diagnosis of head and neck squamous dysplasia and carcinomas is critical for patient care, cure and follow-up. It can be challenging, especially for intraepithelial lesions. Even though the last WHO classification simplified the grading of dysplasia with only two grades (except for oral or oropharyngeal lesions), the inter and intra-observer variability remains substantial, especially for non-specialized pathologists. In this study we investigated the potential of deep learning to assist the pathologist with automatic and reliable classification of head and neck squamous lesions following the 2022 WHO classification system for the hypopharynx, larynx, trachea and parapharyngeal space. We created, for the first time, a large scale database of histological samples intended for developing an automatic diagnostic tool. We developed and trained a weakly supervised model performing classification from whole slides images. A dual blind review was carried out to define a gold standard test set on which our model was able to classify lesions with high accuracy on every class (average AUC: 0.878 (95% CI: [0.834-0.918])). Finally, we defined a confidence score for the model predictions, which can be used to identify ambiguous or difficult cases. When the algorithm is applied as a screening tool, such cases can then be submitted to pathologists in priority. Our results demonstrate that the model, associated with confidence measurements, can help in the difficult task of classifying head and neck squamous lesions

Diagnosis with confidence: deep learning for reliable classification of laryngeal dysplasia

International audienceBackground Diagnosis of head and neck (HN) squamous dysplasias and carcinomas is critical for patient care, cure, and follow‐up. It can be challenging, especially for grading intraepithelial lesions. Despite recent simplification in the last WHO grading system, the inter‐ and intraobserver variability remains substantial, particularly for nonspecialized pathologists, exhibiting the need for new tools to support pathologists. Methods In this study we investigated the potential of deep learning to assist the pathologist with automatic and reliable classification of HN lesions following the 2022 WHO classification system. We created, for the first time, a large‐scale database of histological samples (>2000 slides) intended for developing an automatic diagnostic tool. We developed and trained a weakly supervised model performing classification from whole‐slide images (WSI). We evaluated our model on both internal and external test sets and we defined and validated a new confidence score to assess the predictions that can be used to identify difficult cases. Results Our model demonstrated high classification accuracy across all lesion types on both internal and external test sets (respectively average area under the curve [AUC]: 0.878 (95% confidence interval [CI]: [0.834–0.918]) and 0.886 (95% CI: [0.813–0.947])) and the confidence score allowed for accurate differentiation between reliable and uncertain predictions. Conclusion Our results demonstrate that the model, associated with confidence measurements, can help in the difficult task of classifying HN squamous lesions by limiting variability and detecting ambiguous cases, taking us one step closer to a wider adoption of AI‐based assistive tools

A Deep Learning System Using Optical Coherence Tomography Angiography to Detect Glaucoma and Anterior Ischemic Optic Neuropathy

Introduction. Glaucoma and non-arteritic anterior ischemic optic neuropathy (NAION) are optic neuropathies that can both lead to irreversible blindness. Several studies have compared optical coherence tomography angiography (OCTA) findings in glaucoma and NAION in the presence of similar functional and structural damages with contradictory results. The goal of this study was to use a deep learning system to differentiate OCTA in glaucoma and NAION. Material and methods. Sixty eyes with glaucoma (including primary open angle glaucoma, angle-closure glaucoma, normal tension glaucoma, pigmentary glaucoma, pseudoexfoliative glaucoma and juvenile glaucoma), thirty eyes with atrophic NAION and forty control eyes (NC) were included. All patients underwent OCTA imaging and automatic segmentation was used to analyze the macular superficial capillary plexus (SCP) and the radial peripapillary capillary (RPC) plexus. We used the classic convolutional neural network (CNN) architecture of ResNet50. Attribution maps were obtained using the “Integrated Gradients” method. Results. The best performances were obtained with the SCP + RPC model achieving a mean area under the receiver operating characteristics curve (ROC AUC) of 0.94 (95% CI 0.92–0.96) for glaucoma, 0.90 (95% CI 0.86–0.94) for NAION and 0.96 (95% CI 0.96–0.97) for NC. Conclusion. This study shows that deep learning architecture can classify NAION, glaucoma and normal OCTA images with a good diagnostic performance and may outperform the specialist assessment

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

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

How specialized are writing-specific brain regions? An fMRI study of writing, drawing and oral spelling

International audienceSeveral brain imaging studies identified brain regions that are consistently involved in writing tasks; the left premotor and superior parietal cortices have been associated with the peripheral components of writing performance as opposed to other regions that support the central, orthographic components. Based on a meta-analysis by Planton, Jucla, Roux, and Demonet (2013), we focused on five such writing areas and questioned the task-specificity and hemispheric lateralization profile of the brain response in an functional magnetic resonance imaging (fMRI) experiment where 16 right-handed participants wrote down, spelled out orally object names, and drew shapes from object pictures. All writing-related areas were activated by drawing, and some of them by oral spelling, thus questioning their specialization for written production. The graphemic/motor frontal area (GMFA), a subpart of the superior premotor cortex close to Exner's area (Roux et al., 2009), was the only area with a writing-specific lateralization profile, that is, clear left lateralization during handwriting, and bilateral activity during drawing. Furthermore, the relative lateralization and levels of activation in the superior parietal cortex, ventral premotor cortex, ventral occipitotemporal cortex and right cerebellum across the three tasks brought out new evidence regarding their respective contributions to the writing processes