Quantitative Screening of Cervical Cancers for Low-Resource Settings: Pilot Study of Smartphone-Based Endoscopic Visual Inspection After Acetic Acid Using Machine Learning Techniques

Abstract

Background Approximately 90% of global cervical cancer (CC) is mostly found in low- and middle-income countries. In most cases, CC can be detected early through routine screening programs, including a cytology-based test. However, it is logistically difficult to offer this program in low-resource settings due to limited resources and infrastructure, and few trained experts. A visual inspection following the application of acetic acid (VIA) has been widely promoted and is routinely recommended as a viable form of CC screening in resource-constrained countries. Digital images of the cervix have been acquired during VIA procedure with better quality assurance and visualization, leading to higher diagnostic accuracy and reduction of the variability of detection rate. However, a colposcope is bulky, expensive, electricity-dependent, and needs routine maintenance, and to confirm the grade of abnormality through its images, a specialist must be present. Recently, smartphone-based imaging systems have made a significant impact on the practice of medicine by offering a cost-effective, rapid, and noninvasive method of evaluation. Furthermore, computer-aided analyses, including image processing–based methods and machine learning techniques, have also shown great potential for a high impact on medicinal evaluations. Objective In this study, we demonstrate a new quantitative CC screening technique and implement a machine learning algorithm for smartphone-based endoscopic VIA. We also evaluated the diagnostic performance and practicability of the approach based on the results compared to the gold standard and from physicians’ interpretation. Methods A smartphone-based endoscope system was developed and applied to the VIA screening. A total of 20 patients were recruited for this study to evaluate the system. Overall, five were healthy, and 15 were patients who had shown a low to high grade of cervical intraepithelial neoplasia (CIN) from both colposcopy and cytology tests. Endoscopic VIA images were obtained before a loop electrosurgical excision procedure for patients with abnormal tissues, and their histology tissues were collected. Endoscopic VIA images were assessed by four expert physicians relative to the gold standard of histopathology. Also, VIA features were extracted from multiple steps of image processing techniques to find the differences between abnormal (CIN2+) and normal (≤CIN1). By using the extracted features, the performance of different machine learning classifiers, such as k-nearest neighbors (KNN), support vector machine, and decision tree (DT), were compared to find the best algorithm for VIA. After determining the best performing classifying model, it was used to evaluate the screening performance of VIA. Results An average accuracy of 78%, with a Cohen kappa of 0.571, was observed for the evaluation of the system by four physicians. Through image processing, 240 sliced images were obtained from the cervicogram at each clock position, and five features of VIA were extracted. Among the three models, KNN showed the best performance for finding VIA within holdout 10-fold cross-validation, with an accuracy of 78.3%, area under the curve of 0.807, a specificity of 80.3%, and a sensitivity of 75.0%, respectively. The trained model performed using an unprovided data set resulted in an accuracy of 80.8%, specificity of 84.1%, and sensitivity of 71.9%. Predictions were visualized with intuitive color labels, indicating the normal/abnormal tissue using a circular clock-type segmentation. Calculating the overlapped abnormal tissues between the gold standard and predicted value, the KNN model overperformed the average assessments of physicians for finding VIA. Conclusions We explored the potential of the smartphone-based endoscopic VIA as an evaluation technique and used the cervicogram to evaluate normal/abnormal tissue using machine learning techniques. The results of this study demonstrate its potential as a screening tool in low-resource settings.

Addition of digital VIA/VILI to conventional naked-eye examination for triage of HPV-positive women: A study conducted in a low-resource setting

“…Other avenues for improving cervical cancer screening exist, and new strategies to improve accuracy of visual assessment need to be investigated. The development of a smartphone application recognizing pathological lesions of the cervix, for example, is underway, and might contribute to improving the screening process [ 33 ].…”

Smartphone-Based Visual Inspection with Acetic Acid: An Innovative Tool to Improve Cervical Cancer Screening in Low-Resource Setting

“…Kudva et al used an android device with a camera to develop AI classification tools, achieving very high performances [ 44 ]. Bae et al developed a smartphone-based endoscope that acquires and classifies images before and after application of the acetic acid [ 45 ]. Finally, Viñals et al developed an AI algorithm based on smartphone images without any additional component [ 46 ].…”

Smartphone-based imaging systems for medical applications: a critical review

“…Endoscopic procedures using both rigid and flexible optical probes have incorporated into SBI systems for otolaryngological, esophageal, and cervical examination of epithelial tissues. 10 – 15 , 135 In the context of endoscopy applications, handheld systems with rigid optical probes seem more synergistic with smartphone utilization as the probe and the device are compact enough to simultaneously manipulate and position. 10 , 135 By contrast, other microendoscope systems utilize thin flexible coherent fiber bundles for image relay.…”

“… 10 – 15 , 135 In the context of endoscopy applications, handheld systems with rigid optical probes seem more synergistic with smartphone utilization as the probe and the device are compact enough to simultaneously manipulate and position. 10 , 135 By contrast, other microendoscope systems utilize thin flexible coherent fiber bundles for image relay. 14 , 15 These systems benefit less from smartphone utilization, as the optical probe is typically manipulated independently of the monitor screen and the optical probes can relay images over longer distances (often several feet), reducing the need for an ultracompact optical enclosure.…”