Abstract
BackgroundHysteroscopy is a commonly used technique for diagnosing endometrial lesions. It is essential to develop an objective model to aid clinicians in lesion diagnosis, as each type of lesion has a distinct treatment, and judgments of hysteroscopists are relatively subjective. This study constructs a convolutional neural network model that can automatically classify endometrial lesions using hysteroscopic images as input.MethodsAll histopathologically confirmed endometrial lesion images were obtained from the Shengjing Hospital of China Medical University, including endometrial hyperplasia without atypia, atypical hyperplasia, endometrial cancer, endometrial polyps, and submucous myomas. The study included 1851 images from 454 patients. After the images were preprocessed (histogram equalization, addition of noise, rotations, and flips), a training set of 6478 images was input into a tuned VGGNet-16 model; 250 images were used as the test set to evaluate the model’s performance. Thereafter, we compared the model’s results with the diagnosis of gynecologists.ResultsThe overall accuracy of the VGGNet-16 model in classifying endometrial lesions is 80.8%. Its sensitivity to endometrial hyperplasia without atypia, atypical hyperplasia, endometrial cancer, endometrial polyp, and submucous myoma is 84.0%, 68.0%, 78.0%, 94.0%, and 80.0%, respectively; for these diagnoses, the model’s specificity is 92.5%, 95.5%, 96.5%, 95.0%, and 96.5%, respectively. When classifying lesions as benign or as premalignant/malignant, the VGGNet-16 model’s accuracy, sensitivity, and specificity are 90.8%, 83.0%, and 96.0%, respectively. The diagnostic performance of the VGGNet-16 model is slightly better than that of the three gynecologists in both classification tasks. With the aid of the model, the overall accuracy of the diagnosis of endometrial lesions by gynecologists can be improved.ConclusionsThe VGGNet-16 model performs well in classifying endometrial lesions from hysteroscopic images and can provide objective diagnostic evidence for hysteroscopists.
Highlights
Hysteroscopy is a commonly used technique for diagnosing endometrial lesions
The diagnostic performance of hysteroscopy for these lesions depends on the experience of the hysteroscopist, resulting in a degree of subjectivity in the gynecologist’s diagnosis [7]
This study considers the five most common endometrial lesions: endometrial hyperplasia without atypia (EH), including simple and complex hyperplasia; atypical hyperplasia (AH); endometrial cancer (EC); endometrial polyps (EPs); and submucous myomas (SMs) [20]
Summary
Hysteroscopy is a commonly used technique for diagnosing endometrial lesions. It is essential to develop an objective model to aid clinicians in lesion diagnosis, as each type of lesion has a distinct treatment, and judgments of hysteroscopists are relatively subjective. Patients are often diagnosed with suspected endometrial lesion due to symptoms such as abnormal uterine bleeding or infertility [1, 2]. Zhang et al J Transl Med (2021) 19:10 diagnosis [6]. These endometrial lesions include endometrial polyps, submucous myomas, intrauterine adhesions, endometrial hyperplasia, malignancies, intrauterine foreign bodies, placental remnants, and endometritis [6]. The diagnostic performance of hysteroscopy for these lesions depends on the experience of the hysteroscopist, resulting in a degree of subjectivity in the gynecologist’s diagnosis [7]. A stable and objective computer-aided diagnosis (CAD) system could shorten the learning curve of inexperienced gynecologists and effectively reduce the subjectivity (interobserver error) of gynecologist diagnosis
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