Publications by authors named "Dipayan Banik"
Objectives: Visual inspection with acetic acid (VIA) is a low-cost approach for cervical cancer screening used in most low- and middle-income countries (LMICs) but, similar to other visual tests like histopathology, is subjective and requires sustained training and quality assurance. We developed, trained, and validated an artificial-intelligence-based "Automated Visual Evaluation" (AVE) tool that can be adapted to run on smartphones to assess smartphone-captured images of the cervix and identify precancerous lesions, helping augment performance of VIA.
Design: Prospective study.
Rapid tests for SARS-COV-2 infection are important tools for pandemic control, but current rapid tests are based on proprietary designs and reagents. We report clinical validation results of an open-access lateral flow assay (OA-LFA) design using commercially available materials and reagents, along with RT-qPCR and commercially available comparators (BinaxNOW® and Sofia®). Adult patients with suspected COVID-19 based on clinical signs and symptoms, and with symptoms ≤7 days duration, underwent anterior nares (AN) sampling for the OA-LFA, Sofia®, BinaxNOW ™, and RT-qPCR, along with nasopharyngeal (NP) RT-qPCR.
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- - Cervical cancer remains a leading cause of death among women globally, making early screening for its precursors, particularly Cervical Intraepithelial Neoplasia (CIN), crucial for improving survival rates.
- - Visual Inspection with Acetic Acid (VIA) is a common method for detecting cervical lesions but relies heavily on the subjective assessment of health workers, which can vary in quality.
- - A new deep learning algorithm called Automated Visual Evaluation (AVE) can analyze cervigrams more accurately than human experts and, when combined with image quality assessment tools, has shown promising results on low-end smartphones for efficient cervical cancer screening.