Potential of educational cystoscopy atlas for augmented intelligence

Okyaz Eminaga; Mark Laurie; Timothy Lee; Xiao Jia; Lei Xing; Joseph C. Liao

doi:10.1117/12.2650920

6 March 2023 Potential of educational cystoscopy atlas for augmented intelligence

Okyaz Eminaga, Mark Laurie, Timothy Lee, Xiao Jia, Lei Xing, Joseph C. Liao

Proceedings Volume 12368, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XXI; 123680P (2023) https://doi.org/10.1117/12.2650920
Event: SPIE BiOS, 2023, San Francisco, California, United States

Conference Poster

Abstract

Background: Cystoscopy is a common urological procedure for evaluation and treatment of diseases of the lower urinary tract. Cystoscopic recognition of cancerous and benign lesions of the bladder may be challenging given the wide spectrum of pathology and the experience of the urologist. Although computer-aided detection tools hold the potential to improve the urologist’s performance, compiling a comprehensive dataset to train such tools remain a challenge. Educational cystoscopy atlas represents an alternative strategy to overcome this challenge. Materials and Methods: Mimicking the human behavior, we utilized an educational cystoscopy atlas to develop deep learning models for lesion detection. We applied a total of 312 images representing 7 major urologic findings from a cystoscopy atlas. A random image augmentation was applied to these images and included color contrast manipulation, image flipping and shearing. We utilized a neural architecture search to determine the optimal model design; For each case, we examined the area under the receiver operating characteristic (AUROC), the specificity, sensitivity, the positive predictive value (PPV) and the negative predictive value (NPV) and to classify frames according to the cancer presence status on 68 cystoscopy videos. Results: The median per-case AUROC for the frame classification was 0.680 by a median specificity of 0.312 and a median sensitivity of 0.867. At frame level, the median per-case PPV was 0.347 and the median per-case NPV was 0.837. All lesions were correctly flagged by our model.

Citation Download Citation

Okyaz Eminaga, Mark Laurie, Timothy Lee, Xiao Jia, Lei Xing, and Joseph C. Liao "Potential of educational cystoscopy atlas for augmented intelligence", Proc. SPIE 12368, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XXI, 123680P (6 March 2023); https://doi.org/10.1117/12.2650920

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