The application of deep learning based diagnostic system to cervical squamous intraepithelial lesions recognition in colposcopy images

Abstract

Background Deep learning has presented considerable potential and is gaining more importance in computer assisted diagnosis. As the gold standard for pathologically diagnosing cervical intraepithelial lesions and invasive cervical cancer, colposcopy-guided biopsy faces challenges in improving accuracy and efficiency worldwide, especially in developing countries. To ease the heavy burden of cervical cancer screening, it is urgent to establish a scientific, accurate and efficient method for assisting diagnosis and biopsy. Methods The data were collected to establish three deeplearning-based models. For every case, one saline image, one acetic image, one iodine image and the corresponding clinical information, including age, the results of human papillomavirus testing and cytology, type of transformation zone, and pathologic diagnosis, were collected. The dataset was proportionally divided into three subsets including the training set, the test set and the validation set, at a ratio of 8:1:1. The validation set was used to evaluate model performance. After model establishment, an independent dataset of high-definition images was collected to further evaluate the model performance. In addition, the comparison of diagnostic accuracy between colposcopists and models weas performed. Results The sensitivity, specificity and accuracy of the classification model to differentiate negative cases from positive cases were 85.38%, 82.62% and 84.10% respectively, with an AUC of 0.93. The recall and DICE of the segmentation model to segment suspicious lesions in acetic images were 84.73% and 61.64%, with an average accuracy of 95.59%. Furthermore, 84.67% of highgrade lesions were detected by the acetic detection model. Compared to colposcopists, the diagnostic system performed better in ordinary colposcopy images but slightly unsatisfactory in high-definition images. Implications The deep learning-based diagnostic system could help assist colposcopy diagnosis and biopsy for HSILs.

Cervical lesion assessment using real‐time microendoscopy image analysis in Brazil: The CLARA study

“…Recent advances in visual evaluation using deep learning could potentially provide automated wide-field assessment of the uterine cervix and highlight suspected lesions for subsequent high-resolution imaging. 29,30 Ongoing studies to evaluate HRME with automated visual assessment are under way. Additional studies to further assess the safety of proflavine use for HRME imaging as well as explore alternative fluorescent dyes will be useful moving forward.…”

“…However, in order for HRME to be effectively utilized in very low‐resource settings, improved methods to guide probe placement may be needed. Recent advances in visual evaluation using deep learning could potentially provide automated wide‐field assessment of the uterine cervix and highlight suspected lesions for subsequent high‐resolution imaging 29,30 . Ongoing studies to evaluate HRME with automated visual assessment are under way.…”

An artificial intelligence‑assisted diagnostic system improves the accuracy of image diagnosis of uterine cervical lesions

“…Yuan et al (9) report that the sensitivity, specificity and accuracy of the classification model to differentiate negative cases from positive cases were 85.38, 82.62 and 84.10%, respectively, with an AUC of 0.93. The recall and Figure 1.…”

“…This could be attributed to the evaluation of AI image diagnosis in four categories in the current study. In previous reports (7,(9)(10)(11)(12), the cervical pathology was divided into two or three categories. In the present study, the diagnostic accuracy when divided into two categories was 79.4% in HSIL and 87.0% in LSIL, comparable to other reports (7,(9)(10)(11)(12).…”

Incorporating Artificial Fish Swarm in Ensemble Classification Framework for Recurrence Prediction of Cervical Cancer

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