Kuo Miao

Discriminative diagnosis of ovarian endometriosis cysts and benign mucinous cystadenomas based on the ConvNeXt algorithm

The objective of this study was to develop a deep learning model, using the ConvNeXt algorithm, that can effectively differentiate between ovarian endometriosis cysts (OEC) and benign mucinous cystadenomas (MC) by analyzing ultrasound images. The performance of the model in the diagnostic differentiation of these two conditions was also evaluated. A retrospective analysis was conducted on OEC and MC patients who had sought medical attention at the Fourth Affiliated Hospital of Harbin Medical University between August 2018 and May 2023. The diagnosis was established based on postoperative pathology or the characteristics of aspirated fluid guided by ultrasound, serving as the gold standard. Ultrasound images were collected and subjected to screening and preprocessing procedures. The data set was randomly divided into training, validation, and testing sets in a ratio of 5:3:2. Transfer learning was utilized to determine the initial weights of the ConvNeXt deep learning algorithm, which were further adjusted by retraining the algorithm using the training and validation ultrasound images to establish a new deep learning model. The weights that yielded the highest accuracy were selected to evaluate the diagnostic performance of the model using the validation set. Receiver operating characteristic (ROC) curves were generated, and the area under the curve (AUC) was calculated. Additionally, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, and odds ratio were calculated. Decision curve analysis (DCA) curves were plotted. The study included 786 ultrasound images from 184 patients diagnosed with either OEC or MC. The deep learning model achieved an AUC of 0.90 (95 % CI: 0.85-0.95) in accurately distinguishing between the two conditions, with a sensitivity of 90 % (95 % CI: 84 %-95 %), specificity of 90 % (95 % CI: 77 %-97 %), a positive predictive value of 96 % (95 % CI: 91 %-99 %), a negative predictive value of 77 % (95 % CI: 63 %-88 %), a positive likelihood ratio of 9.27 (95 % CI: 3.65-23.56), and a negative likelihood ratio of 0.11 (95 % CI: 0.06-0.19). The DCA curve demonstrated the practical clinical utility of the model. The deep learning model developed using the ConvNeXt algorithm exhibits high accuracy (90 %) in distinguishing between OEC and MC. This model demonstrates excellent diagnostic performance and clinical utility, providing a novel approach for the clinical differentiation of these two conditions.

Prediction of benign and malignant ovarian tumors using Resnet34 on ultrasound images

AbstractObjectiveTo develop deep learning (DL) prediction models using transvaginal ultrasound (TVS), transabdominal ultrasound (TAS), and color Doppler flow imaging (CDFI) of TVS (CDFI_TVS) to automatically predict benign or malignant ovarian tumors.MethodsThis retrospective study included women with ovarian tumors who underwent ultrasound between August 2018 and October 2022. Histopathological analysis was used as a reference standard. The dataset was preprocessed by clipping, flipping, and rotating images to generate a larger, more complicated, and diverse dataset to improve accuracy and generalizability. The dataset was then divided into training (80%) and test (20%) sets. The weights of the models, modified from the residual network (ResNet) with the TVS, TAS, and CDFI_TVS images (hereafter, referred to as DLTVS, DLTAS, and DLCDFI_TVS, respectively) were developed. The area under the receiver operating characteristic curve (AUC) analysis in the test set was used to compare the predictive value of DL for malignancy.ResultsA total of 2340 images from 1350 women with adnexal masses were included. DLTVS had an AUC of 0.95 (95% CI: 0.93–0.97) for classifying malignant and benign ovarian tumors, comparable with that of DLTAS (AUC, 0.95; 95% CI: 0.91–0.98; p = 0.96) and DLCDFI_TVS (AUC, 0.88; 95% CI: 0.84–0.93; p = 0.02). Decision curve analysis indicated that DLTVS performed better than DLTAS and DLCDFI_TVS.ConclusionWe developed DL models based on TVS, TAS, and CDFI_TVS on ultrasound images to predict benign and malignant ovarian tumors with high diagnostic performance. The DLTVS model had the best prediction compared with the DLTAS and DLCDFI_TVS models.