Colposcopy is an important tool in diagnosing cervical cancer, and the International Federation of Cervical Pathology and Colposcopy (IFCPC) issued the latest version of the guidelines in 2011. This study aims to systematically assess the accuracy of colposcopy in predicting low-grade squamous intraepithelial lesions or worse (LSIL+) / high-grade squamous intraepithelial lesions or worse (HSIL+) under the 2011 IFCPC terminology.We performed a systematic review and meta-analysis, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched for studies about the performance of colposcopy in diagnosing cervical intraepithelial neoplasia under the new IFCPC colposcopy terminology from PubMed, Embase, Web of Science and the Cochrane database. Data were independently extracted by two authors and an overall diagnostic performance index was calculated under two colposcopic thresholds.Totally, fifteen articles with 22,764 participants in compliance with the criteria were included in meta-analysis. When colposcopy was used to detect LSIL+, the combined sensitivity and specificity were 0.92 (95% CI 0.88-0.95) and 0.51 (0.43-0.59), respectively. When colposcopy was used to detect HSIL+, the combined sensitivity and specificity were 0.68 (0.58-0.76) and 0.93 (0.88-0.96), respectively.In accordance with the 2011 IFCPC terminology, the accuracy of colposcopy has improved in terms of both sensitivity and specificity. Colposcopy is now more sensitive with LSIL+ taken as the cut-off value and is more specific to HSIL+. These findings suggest we are avoiding under- or overdiagnosis both of which impact on patients' well-being.© 2023. The Author(s).

Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of these methods to medical imaging requires further assessment and validation.To apply deep learning to create an algorithm for automated detection of diabetic retinopathy and diabetic macular edema in retinal fundus photographs.A specific type of neural network optimized for image classification called a deep convolutional neural network was trained using a retrospective development data set of 128 175 retinal images, which were graded 3 to 7 times for diabetic retinopathy, diabetic macular edema, and image gradability by a panel of 54 US licensed ophthalmologists and ophthalmology senior residents between May and December 2015. The resultant algorithm was validated in January and February 2016 using 2 separate data sets, both graded by at least 7 US board-certified ophthalmologists with high intragrader consistency.Deep learning-trained algorithm.The sensitivity and specificity of the algorithm for detecting referable diabetic retinopathy (RDR), defined as moderate and worse diabetic retinopathy, referable diabetic macular edema, or both, were generated based on the reference standard of the majority decision of the ophthalmologist panel. The algorithm was evaluated at 2 operating points selected from the development set, one selected for high specificity and another for high sensitivity.The EyePACS-1 data set consisted of 9963 images from 4997 patients (mean age, 54.4 years; 62.2% women; prevalence of RDR, 683/8878 fully gradable images [7.8%]); the Messidor-2 data set had 1748 images from 874 patients (mean age, 57.6 years; 42.6% women; prevalence of RDR, 254/1745 fully gradable images [14.6%]). For detecting RDR, the algorithm had an area under the receiver operating curve of 0.991 (95% CI, 0.988-0.993) for EyePACS-1 and 0.990 (95% CI, 0.986-0.995) for Messidor-2. Using the first operating cut point with high specificity, for EyePACS-1, the sensitivity was 90.3% (95% CI, 87.5%-92.7%) and the specificity was 98.1% (95% CI, 97.8%-98.5%). For Messidor-2, the sensitivity was 87.0% (95% CI, 81.1%-91.0%) and the specificity was 98.5% (95% CI, 97.7%-99.1%). Using a second operating point with high sensitivity in the development set, for EyePACS-1 the sensitivity was 97.5% and specificity was 93.4% and for Messidor-2 the sensitivity was 96.1% and specificity was 93.9%.In this evaluation of retinal fundus photographs from adults with diabetes, an algorithm based on deep machine learning had high sensitivity and specificity for detecting referable diabetic retinopathy. Further research is necessary to determine the feasibility of applying this algorithm in the clinical setting and to determine whether use of the algorithm could lead to improved care and outcomes compared with current ophthalmologic assessment.

The aim of the present study was to explore the feasibility of using deep learning as artificial intelligence (AI) to classify cervical squamous epithelial lesions (SIL) from colposcopy images. A total of 330 patients who underwent colposcopy and biopsy by gynecologic oncologists were enrolled in the current study. A total of 97 patients received a pathological diagnosis of low-grade SIL (LSIL) and 213 of high-grade SIL (HSIL). An original AI-classifier with 11 layers of the convolutional neural network was developed and trained. The accuracy, sensitivity, specificity and Youden's J index of the AI-classifier and oncologists for diagnosing HSIL were 0.823 and 0.797, 0.800 and 0.831, 0.882 and 0.773, and 0.682 and 0.604, respectively. The area under the receiver-operating characteristic curve was 0.826±0.052 (mean ± standard error), and the 95% confidence interval 0.721-0.928. The optimal cut-off point was 0.692. Cohen's Kappa coefficient for AI and colposcopy was 0.437 (P<0.0005). The AI-classifier performed better than oncologists, although not significantly. Although further study is required, the clinical use of AI for the classification of HSIL/LSIL from by colposcopy may be feasible.Copyright: © Miyagi et al.

Colposcopy diagnosis and directed biopsy are the key components in cervical cancer screening programs. However, their performance is limited by the requirement for experienced colposcopists. This study aimed to develop and validate a Colposcopic Artificial Intelligence Auxiliary Diagnostic System (CAIADS) for grading colposcopic impressions and guiding biopsies.Anonymized digital records of 19,435 patients were obtained from six hospitals across China. These records included colposcopic images, clinical information, and pathological results (gold standard). The data were randomly assigned (7:1:2) to a training and a tuning set for developing CAIADS and to a validation set for evaluating performance.The agreement between CAIADS-graded colposcopic impressions and pathology findings was higher than that of colposcopies interpreted by colposcopists (82.2% versus 65.9%, kappa 0.750 versus 0.516, p < 0.001). For detecting pathological high-grade squamous intraepithelial lesion or worse (HSIL+), CAIADS showed higher sensitivity than the use of colposcopies interpreted by colposcopists at either biopsy threshold (low-grade or worse 90.5%, 95% CI 88.9-91.4% versus 83.5%, 81.5-85.3%; high-grade or worse 71.9%, 69.5-74.2% versus 60.4%, 57.9-62.9%; all p < 0.001), whereas the specificities were similar (low-grade or worse 51.8%, 49.8-53.8% versus 52.0%, 50.0-54.1%; high-grade or worse 93.9%, 92.9-94.9% versus 94.9%, 93.9-95.7%; all p > 0.05). The CAIADS also demonstrated a superior ability in predicting biopsy sites, with a median mean-intersection-over-union (mIoU) of 0.758.The CAIADS has potential in assisting beginners and for improving the diagnostic quality of colposcopy and biopsy in the detection of cervical precancer/cancer.

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 deep-learning-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 high-grade 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.

Colposcopy is important for triaging any abnormal cervical screening test. Scarcity of trained colposcopists and colposcopy centers is a big hurdle to screening programs in low- and middle-income countries.The objective was to assess the performance of the artificial intelligence incorporated into the mobile optical device technologies (ODT) Enhanced Visual Assessment (EVA visual check) against physician colposcopic diagnosis and the gold standard of histopathology.It was a cross-sectional observational study conducted on women referred to a colposcopy clinic following an abnormal screening test. Colposcopic examination was performed by colposcopists using the MobileODT EVA system. Physician's impression and Visual Check analysis were compared with the final histopathological analysis or cytology. Cases with normal cytology and normal colposcopy did not undergo biopsy, and these were considered normal.A total of 2050 women were screened, and 147 screen-positive women were recruited in the study. EVA Visual Check had a sensitivity of 86.8% (75-95), specificity of 28.7% (20-39), positive predictive value (PPV) of 40.7% (32-50), negative predictive value (NPV) of 79.4% (62-91), and diagnostic accuracy of 49.7% (41-58) for diagnosing cervical intraepithelial neoplasia (CIN) 1+ lesions. EVA Visual Check has a sensitivity of 89.3% (72-98), specificity of 26.1% (18-35), PPV of 22.1% (15-31), NPV of 91.2% (76-98), and diagnostic accuracy of 38.1% (30-46) for CIN 2+ lesions.MobileODT EVA colposcope with AI has sensitivity comparable to physician's diagnosis, whereas specificity, PPV, and NPV were less than that of physician's diagnosis. It could prove valuable for triage of screen-positive women for further management.Copyright: © 2024 Journal of Mid-life Health.

With the rapid development of deep learning, automatic image recognition is widely used in medical development. In this study, a deep learning convolutional neural network model was developed to recognize and classify chronic cervicitis and cervical cancer. A total of 10,012 colposcopy images of 1,081 patients from Hunan Provincial People’s Hospital in China were recorded. Five different colposcopy image features of the cervix including chronic cervicitis, intraepithelial lesions, cancer, polypus, and free hyperplastic squamous epithelial tissue were extracted to be applied in our deep learning network convolutional neural network model. However, the result showed a low accuracy (42.16%) due to computer misrecognition of chronic cervicitis, intraepithelial lesions, and free hyperplastic squamous epithelial tissue with high similarity. To optimize this model, we selected two significant feature images: chronic cervicitis and cervical cancer to input into a deep learning network. The result indicates high accuracy and robustness with an accuracy of 95.19%, which can be applied to detect whether the patient has chronic cervicitis or cervical cancer based on the patient’s colposcopy images.

Cervical cancer can be prevented and treated better if it is diagnosed early. Colposcopy, a way of clinically looking at the cervix region, is an efficient method for cervical cancer screening and its early detection. The cervix region segmentation significantly affects the performance of computer-aided diagnostics using a colposcopy, particularly cervical intraepithelial neoplasia (CIN) classification. However, there are few studies of cervix segmentation in colposcopy, and no studies of fully unsupervised cervix region detection without image pre- and post-processing. In this study, we propose a deep learning-based unsupervised method to identify cervix regions without pre- and post-processing. A new loss function and a novel scheduling scheme for the baseline W-Net are proposed for fully unsupervised cervix region segmentation in colposcopy. The experimental results showed that the proposed method achieved the best performance in the cervix segmentation with a Dice coefficient of 0.71 with less computational cost. The proposed method produced cervix segmentation masks with more reduction in outliers and can be applied before CIN detection or other diagnoses to improve diagnostic performance. Our results demonstrate that the proposed method not only assists medical specialists in diagnosis in practical situations but also shows the potential of an unsupervised segmentation approach in colposcopy.

Colposcopy is an important method in the diagnosis of cervical lesions. However, experienced colposcopists are lacking at present, and the training cycle is long. Therefore, the artificial intelligence-based colposcopy-assisted examination has great prospects. In this paper, a cervical lesion segmentation model (CLS-Model) was proposed for cervical lesion region segmentation from colposcopic post-acetic-acid images and accurate segmentation results could provide a good foundation for further research on the classification of the lesion and the selection of biopsy site.