Deep learning-based image evaluation for cervical precancer screening with a smartphone targeting low resource settings - Engineering approach.

Cervical cancer is the fourth most common cancer among women and still one of the major causes of women's death around the world. Early screening of high grade Cervical Intraepithelial Neoplasia (CIN), precursors to cervical cancer, is vital to efforts aimed at improving survival rate and eventually eliminating cervical cancer. Visual Inspection with Acetic acid (VIA) is an assessment method which can inspect the cervix and potentially detect lesions caused by human papillomavirus (HPV), which is a major cause of cervical cancer. VIA has the potential to be an effective screening method in low resource settings when triaged with HPV test, but it has the drawback that it depends on the subjective evaluation of health workers with varying levels of training. A new deep learning algorithm called Automated Visual Evaluation (AVE) for analyzing cervigram images has been recently reported that can automatically detect cervical precancer better than human experts. In this paper, we address the question of whether mobile phone-based cervical cancer screening is feasible. We consider the capabilities of two key components of a mobile phone platform for cervical cancer screening: (1) the core AVE algorithm and (2) an image quality algorithm. We consider both accuracy and speed in our assessment. We show that the core AVE algorithm, by refactoring to a new deep learning detection framework, can run in ~30 seconds on a low-end smartphone (i.e. Samsung J8), with equivalent accuracy. We developed an image quality algorithm that can localize the cervix and assess image quality in ~1 second on a low-end smartphone, achieving an area under the ROC curve (AUC) of 0.95. Field validation of the mobile phone platform for cervical cancer screening is in progress.

Ensemble Deep Learning for Cervix Image Selection toward Improving Reliability in Automated Cervical Precancer Screening.

Automated Visual Examination (AVE) is a deep learning algorithm that aims to improve the effectiveness of cervical precancer screening, particularly in low- and medium-resource regions. It was trained on data from a large longitudinal study conducted by the National Cancer Institute (NCI) and has been shown to accurately identify cervices with early stages of cervical neoplasia for clinical evaluation and treatment. The algorithm processes images of the uterine cervix taken with a digital camera and alerts the user if the woman is a candidate for further evaluation. This requires that the algorithm be presented with images of the cervix, which is the object of interest, of acceptable quality, i.e., in sharp focus, with good illumination, without shadows or other occlusions, and showing the entire squamo-columnar transformation zone. Our prior work has addressed some of these constraints to help discard images that do not meet these criteria. In this work, we present a novel algorithm that determines that the image contains the cervix to a sufficient extent. Non-cervix or other inadequate images could lead to suboptimal or wrong results. Manual removal of such images is labor intensive and time-consuming, particularly in working with large retrospective collections acquired with inadequate quality control. In this work, we present a novel ensemble deep learning method to identify cervix images and non-cervix images in a smartphone-acquired cervical image dataset. The ensemble method combined the assessment of three deep learning architectures, RetinaNet, Deep SVDD, and a customized CNN (Convolutional Neural Network), each using a different strategy to arrive at its decision, i.e., object detection, one-class classification, and binary classification. We examined the performance of each individual architecture and an ensemble of all three architectures. An average accuracy and F-1 score of 91.6% and 0.890, respectively, were achieved on a separate test dataset consisting of more than 30,000 smartphone-captured images.

Smartphone-Enhanced Training, QA, Monitoring, and Evaluation of a Platform for Secondary Prevention of Cervical Cancer: Opportunities and Challenges to Implementation in Tanzania.

PURPOSE: Until human papillomavirus (HPV)-based cervical screening is more affordable and widely available, visual inspection with acetic acid (VIA) is recommended by the WHO for screening in lower-resource settings. Visual inspection will still be required to assess the cervix for women whose screening is positive for high-risk HPV. However, the quality of VIA can vary widely, and it is difficult to maintain a well-trained cadre of providers. We developed a smartphone-enhanced VIA platform (SEVIA) for real-time secure sharing of cervical images for remote supportive supervision, data monitoring, and evaluation. METHODS: We assessed programmatic outcomes so that findings could be translated into routine care in the Tanzania National Cervical Cancer Prevention Program. We compared VIA positivity rates (for HIV-positive and HIV-negative women) before and after implementation. We collected demographic, diagnostic, treatment, and loss-to-follow-up data. RESULTS: From July 2016 to June 2017, 10,545 women were screened using SEVIA at 24 health facilities across 5 regions of Tanzania. In the first 6 months of implementation, screening quality increased significantly from the baseline rate in the prior year, with a well-trained cadre of more than 50 health providers who "graduated" from the supportive-supervision training model. However, losses to follow-up for women referred for further evaluation or to a higher level of care were considerable. CONCLUSION: The SEVIA platform is a feasible, quality improvement, mobile health intervention that can be integrated into a national cervical screening program. Our model demonstrates potential for scalability. As HPV screening becomes more affordable, the platform can be used for visual assessment of the cervix to determine amenability for same-day ablative therapy and/or as a secondary triage step, if needed.