With current advancements in computer science, artificial intelligence (AI) has made remarkable progress recently. The hypothetical moment in time when AI becomes so advanced that humanity undergoes a dramatic and irreversible change (1) is likely to arrive in this century. AI has already exceeded human experts in the field of games with perfect information, such as Go (2), showing us novel tactics. Therefore, since AI can recognize some information that conventional procedures cannot detect, it may provide a more precise diagnosis in practical medicine. AI may also be able to support clinicians in practical medicine, reducing the time and effort necessary. The aim of the present study was to investigate the feasibility of applying deep learning, a type of AI, for gynecological clinical practice.

Uterine cervical cancer continues to be a major public health problem. Cervical cancer is the third most common female cancer and the leading cause of cancer-related mortality among women in Eastern, Western and Middle Africa, Central America, South-Central Asia and Melanesia. New methodologies of cervical cancer prevention should be made available and accessible to women of all countries (3).

Colposcopy is a well-established tool for examining the cervix under magnification (4–6). When lesions are treated with acetic acid diluted to 3–5%, colposcopy can detect and recognize cervical intraepithelial neoplasia (CIN). Classification systems, such as the Bethesda System in 2002 are used to categorize lesions as high-grade squamous intraepithelial lesions (HSIL) or low-grade SIL (LSIL) (7,8). HSIL and LSIL were previously referred to as CIN2/CIN3 and CIN1, respectively. In clinical practice, it is important for clinicians to distinguish HSIL from LSIL in biopsy specimens, since further examination or treatment, such as conization, may be required for HSIL. Expert gynecologic oncologists spend much time and effort to provide precise colposcopy findings.

For these reasons, we explored whether AI can evaluate colposcopy findings as well as a gynecologic oncologist. In the present study, we applied deep learning with a convolutional neural network to the realm of AI to develop an original classifier for predicting HSIL or LSIL from colposcopy images. Deep learning is becoming very popular among machine learning methods, such as logistic regression (9), naive Bayes (10), nearest neighbor (11), random forest (12) and neural network (13). The classifier program was developed using supervised deep learning with a convolutional neural network (14) that tried to mimic the visual cortex of the mammal brain (15–23), in order to categorize colposcopy images as either HSIL or LSIL. The present study demonstrated the effective use of the AI colposcopy image classifier in predicting HSIL or LSIL by comparing the colposcopic diagnosis to that of gynecologic oncologists.

We developed a classifier using deep learning with convolutional neural networks using images of cervical SILs to predict the pathological diagnosis. In the present study, the accuracy values achieved by the classifier and by gynecologic oncologists were 0.823 and 0.797, respectively (Table III). The sensitivity values of the classifier and gynecologic oncologists were 0.800 and 0.831, respectively. The specificity values of the classifier and gynecologic oncologists were 0.882 and 0.773, respectively. The accuracy and specificity of the classifier were superior to those of gynecologic oncologists, although the difference was not significant. Only moderate agreement was obtained between conventional colposcopy diagnosis and AI colposcopy diagnosis with the Kappa value of 0.437. McHugh reported that Cohen suggested 0.41 might be acceptable and the Kappa result be interpreted as follows: Values ≤0 as indicating no agreement and 0.01–0.20 as none to slight, 0.21–0.40 as fair, 0.41–0.60 as moderate, 0.61–0.80 as substantial and 0.81–1.00 (54). Thus, the Kappa value of 0.437 was acceptable. But, AI colposcopy that might have a potential would not be an alternative to conventional colposcopy without further studies.

Several reports have used AI (55) for deep learning with convolutional neural networks in medicine (56). The accuracy values of this method with deep learning have been published and include 0.997 for the histopathological diagnosis of breast cancer (57), 0.90–0.83 for the early diagnosis of Alzheimer's disease (58), 0.83 for urological dysfunctions (59), 0.72 (60) and 0.50 (61) for colposcopy, 0.68–0.70 for localization of rectal cancer (62), 0.83 for the diagnostic imaging of orthopedic trauma (63), 0.98 for the morphological quality of blastocysts and evaluation by an embryologist (64), 0.65 for predicting live birth without aneuploidy from a blastocyst image (65) and 0.64–0.88 for predicting live birth from a blastocyst image of patients by age (66).

Several studies have reported a limitation of conventional colposcopy. An investigation of the accuracy of colposcopically-directed biopsy reported a total biopsy failure rate, comprising both non-biopsy and incorrect selection of biopsy site, of 0.20 in CIN1, 0.11 in CIN2 and 0.09 in CIN3 (67). The colposcopic impression of high-grade CIN had a sensitivity of 0.54 and a specificity of 0.88, as determined by 9 expert colposcopists in 100 cervigrams (68). The sensitivity of an online colpophotographic assessment of high-grade disease (CIN2 and CIN3) by 20 colposcopists was 0.39 (69). Thus, conventional colposcopy does not provide good sensitivity, even by colposcopists. By contrast, the accuracy and sensitivity reported in this study for predicting HSIL from colposcopy images using deep learning was 0.823 and 0.800, respectively, which appears favorable. Since the classifier was not trained in colposcopy findings such as acetowhite epithelium, mosaic, punctuation, it may recognize some features of cervical SILs by itself in high-dimensional space. It is possible that the AI-classifier may recognize features that colposcopists do not, such as complexity of the shape of the lesion, relative or absolute brightness of acetowhite, distribution of punctuation density, and quantitative marginal evaluation of borders. The pathological results were obtained and defined by punch biopsy in this study, as it was not ethically recommended for patients with LSIL (CIN1) diagnosed by colposcopy to undergo conization or hysterectomy. If the pathological results were defined by conization or hysterectomy, the advanced lesion would have been be revealed, and thus both conventional colposcopy and the AI classifier may have demonstrated different results. In the present study, we only tried to compare the effectiveness of AI with that of conventional colposcopy for SIL. When the AI will be used to predict more advanced diseases, such as squamous cell carcinoma, adenocarcinoma and AIS, the pathological diagnosis should be provided by not punch biopsy but conization or hysterectomy.

In clinical practice, it is important for clinicians to distinguish HSIL from LSIL in biopsy specimens. Further examination or treatment, such as conization, may be required for HSIL. When a reliable classifier indicates HSIL from colposcopy images in clinical practice, the clinician should consider biopsy. The accuracy values of the classifier and gynecologists for detecting HSIL were 0.823 and 0.797, respectively. The classifier might help untrained clinicians to avoid or reduce the risks of overlooking HSIL. When the AI-classifier can perform higher in terms of accuracy, sensitivity and specificity for classifying HSIL/LSIL, clinicians will be able to perform more precise practice, referencing AI. Furthermore, a gynecologist could reduce the time and effort it takes to become a colposcopy expert and, as a result, improve in other skills, training and activities.

The architecture of the neural network has progressed. The LeNet study published in 1998 (70) consisted of 5 layers. AlexNet, published in 2012 (38), consisted of 14 and Google Net, published in 2014 (35), was constructed from a combination of micro networks. ResNet-50, published in 2015 (41), consisted of modules with a shortcut process. The Squeeze-and-Excitation Networks were published in 2017 (71). AI used for image recognition is still being developed. Progress in AI will allow us to achieve better results. Image information is one of the parameters that need to be investigated. Only 15×15 pixels are used to detect cervical cancer (72). In a colposcopy study (61), it was reported that the accuracy for images of 150×150 pixels was better than those for 32×32 or 300×300 pixels. Hence, image size remains an issue. We used 70×70 and 111×111 pixels for our images, in order to use the original neural networks and the modified ResNet-50, respectively. The original conventional neural network was better than the modified ResNet-50, although not significantly. We believe that a pixel size of 70×70 falls within the acceptable range. Regularization values are also important parameters for constructing a good classifier that avoids overfitting. If the regularization value is too low, overfitting occurs. If the value is too large, the classifier will not be trained well. Choosing the appropriate number of training datasets is also very important. If the number of training datasets is too high, the accuracy will be lower and more variances will be observed. The validation dataset, as well as L2 regularization, also prevent overfitting. The appropriate number of training datasets must be achieved to obtain a good classifier. More varied patterns of images may be needed for datasets. Ordinarily, 500–1,000 images are prepared for each class during image classification with deep learning (61,73). Such a large number of datasets will improve the accuracy and specificity of the classifier with deep learning.

In the present study, a classifier was developed based on deep learning, which used images of uterine cervical SILs to predict pathological HSIL/LSIL. Its accuracy was 0.823. Although further study may be required to validate the classifier, we demonstrated that AI may have a clinical use in colposcopic examinations and may provide benefits to both patients and medical personnel.