Browsing by Autor "Brian Befano"

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Assessing generalizability of an AI-based visual test for cervical cancer screening
(2023) Syed Rakin Ahmed; Didem Egemen; Brian Befano; Ana Cecilia Rodríguez; José Jerónimo; Kanan Desai; Carolina Terán; Karla Alfaro; Joël Fokom Domgue; Kittipat Charoenkwan
ABSTRACT A number of challenges hinder artificial intelligence (AI) models from effective clinical translation. Foremost among these challenges are: (1) reproducibility or repeatability, which is defined as the ability of a model to make consistent predictions on repeat images from the same patient taken under identical conditions; (2) the presence of clinical uncertainty or the equivocal nature of certain pathologies, which needs to be acknowledged in order to effectively, accurately and meaningfully separate true normal from true disease cases; and (3) lack of portability or generalizability, which leads AI model performance to differ across axes of data heterogeneity. We recently investigated the development of an AI pipeline on digital images of the cervix, utilizing a multi-heterogeneous dataset (“SEED”) of 9,462 women (17,013 images) and a multi-stage model selection and optimization approach, to generate a diagnostic classifier able to classify images of the cervix into “normal”, “indeterminate” and “precancer/cancer” (denoted as “precancer+”) categories. In this work, we investigated the performance of this multiclass classifier on external data (“EXT”) not utilized in training and internal validation, to assess the portability of the classifier when moving to new settings. We assessed both the repeatability and classification performance of our classifier across the two axes of heterogeneity present in our dataset: image capture device and geography, utilizing both out-of-the-box inference and retraining with “EXT”. Our results indicate strong repeatability of our multiclass model utilizing Monte-Carlo (MC) dropout, which carries over well to “EXT” (95% limit of agreement range = 0.2 - 0.4) even in the absence of retraining, as well as strong classification performance of our model on “EXT” that is achieved with retraining (% extreme misclassifications = 4.0% for n = 26 “EXT” individuals added to “SEED” in a 2n normal : 2n indeterminate : n precancer+ ratio), and incremental improvement of performance following retraining with images from additional individuals. We additionally find that device-level heterogeneity affects our model performance more than geography-level heterogeneity. Our work supports both (1) the development of comprehensively designed AI pipelines, with design strategies incorporating multiclass ground truth and MC dropout, on multi-heterogeneous data that are specifically optimized to improve repeatability, accuracy, and risk stratification; and (2) the need for optimized retraining approaches that address data heterogeneity (e.g., when moving to a new device) to facilitate effective use of AI models in new settings. AUTHOR SUMMARY Artificial intelligence (AI) model robustness has emerged as a pressing issue, particularly in medicine, where model deployment requires rigorous standards of approval. In the context of this work, model robustness refers to both the reproducibility of model predictions across repeat images, as well as the portability of model performance to external data. Real world clinical data is often heterogeneous across multiple axes, with distribution shifts in one or more of these axes often being the norm. Current deep learning (DL) models for cervical cancer and in other domains exhibit poor repeatability and overfitting, and frequently fail when evaluated on external data. As recently as March 2023, the FDA issued a draft guidance on effective implementation of AI/DL models, proposing the need for adapting models to data distribution shifts. To surmount known concerns, we conducted a thorough investigation of the generalizability of a deep learning model for cervical cancer screening, utilizing the distribution shifts present in our large, multi-heterogenous dataset. We highlight optimized strategies to adapt an AI-based clinical test, which in our case was a cervical cancer screening triage test, to external data from a new setting. Given the severe clinical burden of cervical cancer, and the fact that existing screening approaches, such as visual inspection with acetic acid (VIA), are unreliable, inaccurate, and invasive, there is a critical need for an automated, AI-based pipeline that can more consistently evaluate cervical lesions in a minimally invasive fashion. Our work represents one of the first efforts at generating and externally validating a cervical cancer diagnostic classifier that is reliable, consistent, accurate, and clinically translatable, in order to triage women into appropriate risk categories.
Assessing generalizability of an AI-based visual test for cervical cancer screening
(Public Library of Science, 2024) Syed Rakin Ahmed; Didem Egemen; Brian Befano; Ana Cecilia Rodríguez; José Jerónimo; Kanan Desai; Carolina Terán; Karla Alfaro; Joël Fokom Domgue; Kittipat Charoenkwan
A number of challenges hinder artificial intelligence (AI) models from effective clinical translation. Foremost among these challenges is the lack of generalizability, which is defined as the ability of a model to perform well on datasets that have different characteristics from the training data. We recently investigated the development of an AI pipeline on digital images of the cervix, utilizing a multi-heterogeneous dataset of 9,462 women (17,013 images) and a multi-stage model selection and optimization approach, to generate a diagnostic classifier able to classify images of the cervix into "normal", "indeterminate" and "precancer/cancer" (denoted as "precancer+") categories. In this work, we investigate the performance of this multiclass classifier on external data not utilized in training and internal validation, to assess the generalizability of the classifier when moving to new settings. We assessed both the classification performance and repeatability of our classifier model across the two axes of heterogeneity present in our dataset: image capture device and geography, utilizing both out-of-the-box inference and retraining with external data. Our results demonstrate that device-level heterogeneity affects our model performance more than geography-level heterogeneity. Classification performance of our model is strong on images from a new geography without retraining, while incremental retraining with inclusion of images from a new device progressively improves classification performance on that device up to a point of saturation. Repeatability of our model is relatively unaffected by data heterogeneity and remains strong throughout. Our work supports the need for optimized retraining approaches that address data heterogeneity (e.g., when moving to a new device) to facilitate effective use of AI models in new settings.