Publications by authors named "Kuo Miao"
Article Synopsis
- This study developed a method to automatically classify ovarian lesions in sonograms using a deep convolutional neural network (DCNN) model called ConvNeXt-Tiny and compared it to other models.
- A large dataset of sonograms was classified by experienced sonographers according to O-RADS guidelines, and the DCNN models were trained to predict these classifications.
- The ConvNeXt-Tiny model demonstrated high accuracy and reduced classification time for sonographers, indicating it could effectively assist in clinical settings.
Purpose: The study aimed to create a deep convolutional neural network (DCNN) model based on ConvNeXt-Tiny to identify classic benign lesions (CBL) from other lesions (OL) within the Ovarian-Adnexal Reporting and Data System (O-RADS), enhancing the system's utility for novice ultrasonographers.
Methods: Two sets of sonographic images of pathologically confirmed adnexal lesions were retrospectively collected [development dataset (DD) and independent test dataset (ITD)]. The ConvNeXt-Tiny model, optimized through transfer learning, was trained on the DD using the original images directly and after automatic lesion segmentation by a U-Net model.
Discriminative diagnosis of ovarian endometriosis cysts and benign mucinous cystadenomas based on the ConvNeXt algorithm.
Eur J Obstet Gynecol Reprod Biol
July 2024
Purpose: 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.
Methods: 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.
Prediction model of adnexal masses with complex ultrasound morphology.
Front Med (Lausanne)
December 2023
Background: Based on the ovarian-adnexal reporting and data system (O-RADS), we constructed a nomogram model to predict the malignancy potential of adnexal masses with sophisticated ultrasound morphology.
Methods: In a multicenter retrospective study, a total of 430 subjects with masses were collected in the adnexal region through an electronic medical record system at the Fourth Hospital of Harbin Medical University during the period of January 2019-April 2023. A total of 157 subjects were included in the exception validation cohort from Harbin Medical University Tumor Hospital.
Article Synopsis
- Developed deep learning models to predict whether ovarian tumors are benign or malignant using various ultrasound techniques, including transvaginal and transabdominal ultrasounds, plus color Doppler imaging.
- The study analyzed a substantial set of ultrasound images from women with ovarian tumors, processing the data to enhance model accuracy and ensure generalizability.
- Results showed the deep learning models achieved high accuracy in tumor classification, with one model outperforming the others in predictive ability.
Background: Thyroid disease and thyroid nodules are common clinical problems. Iodine nutrition plays an important role in thyroid disease evolution. Here, we aimed to estimate the iodine nutritional status and prevalence of thyroid disease in the adults of the Heilongjiang Province in northeast China.
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