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bioRxiv · 10.1101/324541

Development of algorithms for automated detection of cervical pre-cancers with a low-cost, point-of-care, Pocket Colposcope

Asiedu, M. N.·Simhal, A. K.·Chaudhary, U.·Mueller, J. L.·Lam, C. T.·Schmitt, J. W.·Venegas, G.·Sapiro, G.·Ramanujam, N.
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Abstract

GoalIn this work, we propose methods for (1) automatic feature extraction and classification for acetic acid and Lugols iodine cervigrams and (2) methods for combining features/diagnosis of different contrasts in cervigrams for improved performance.\n\nMethodsWe developed algorithms to pre-process pathology-labeled cervigrams and to extract simple but powerful color and textural-based features. The features were used to train a support vector machine model to classify cervigrams based on corresponding pathology for visual inspection with acetic acid, visual inspection with Lugols iodine, and a combination of the two contrasts.\n\nResultsThe proposed framework achieved a sensitivity, specificity, and accuracy of 81.3%, 78.6%, and 80.0%, respectively when used to distinguish cervical intraepithelial neoplasia (CIN+) relative to normal and benign tissues. This is superior to the average values achieved by three expert physicians on the same data set for discriminating normal/benign cases from CIN+ (77% sensitivity, 51% specificity, 63% accuracy).\n\nConclusionThe results suggest that utilizing simple color- and textural-based features from visual inspection with acetic acid and visual inspection with Lugols iodine images may provide unbiased automation of cervigrams.\n\nSignificanceThis would enable automated, expert-level diagnosis of cervical pre-cancer at the point-of-care.

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Asiedu, M. N., Simhal, A. K., Chaudhary, U., Mueller, J. L., Lam, C. T., Schmitt, J. W., Venegas, G., Sapiro, G., Ramanujam, N.. 2018-05-17. Development of algorithms for automated detection of cervical pre-cancers with a low-cost, point-of-care, Pocket Colposcope. https://doi.org/10.1101/324541

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