Dataset Information

Artificial intelligence diagnosis of intrauterine adhesion by 3D ultrasound imaging: a prospective study.

ABSTRACT:

Background

There were a very large number of intrauterine adhesion (IUA) patients. As improving the classification of three-dimensional transvaginal ultrasound (3D-TVUS) of IUA or non-IUA images remains a clinical challenge and is needed to avoid inappropriate surgery. Our study aimed to evaluate deep learning as a method to classify 3D-TVUS of IUA or non-IUA images taken with panoramic technology.

Methods

After meeting an inclusion/exclusion criteria, a total of 4,401 patients were selected for this study. This included 2,803 IUA patients and 1,598 non-IUA patients. IUA was confirmed by hysteroscopy, and each patient underwent one 3D-TVUS examination. Four well-known convolutional neural network (CNN) architectures were selected to classify the IUA images: Visual Geometry Group16 (VGG16), InceptionV3, ResNet50, and ResNet101. We used these pretrained CNNs on ImageNet by applying both TensorFlow and PyTorch. All 3D-TVUS images were normalized and mixed together. We split the data set into a training set, validation set, and test set. The performance of our classification model was evaluated according to sensitivity, precision, F1-score, and accuracy, which were determined by equations that used true-positive (TP), false-positive (FP), true-negative (TN), and false-negative (FN) numbers.

Results

The overall performances of VGG16, InceptionV3, ResNet50, and ResNet101 were better in PyTorch as opposed to TensorFlow. Through PyTorch, the best CNN model was InceptionV3 with its performance measured as 94.2% sensitivity, 99.4% precision, 96.8% F1-score, and 97.3% accuracy. The area under the curve (AUC) results of VGG16, InceptionV3, ResNet50, and ResNet101 were 0.959, 0.999, 0.997, and 0.999, respectively. PyTorch also successfully transferred information from the source to the target domain where we were able to use another center's data as an external test data set. No overfitting that could have adversely affected the classification accuracy occurred. Finally, we successfully established a webpage to diagnose IUA based on the 3D-TVUS images.

Conclusions

Deep learning can assist in the binary classification of 3D-TVUS images to diagnose IUA. This study lays the foundation for future research into the integration of deep learning and blockchain technology.

SUBMITTER: Zhao X

PROVIDER: S-EPMC10102785 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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Publications

Artificial intelligence diagnosis of intrauterine adhesion by 3D ultrasound imaging: a prospective study.

Zhao Xingping X Liu Minqiang M Wu Susu S Zhang Baiyun B Burjoo Arvind A Yang Yimin Y Xu Dabao D

Quantitative imaging in medicine and surgery 20230322 4

<h4>Background</h4>There were a very large number of intrauterine adhesion (IUA) patients. As improving the classification of three-dimensional transvaginal ultrasound (3D-TVUS) of IUA or non-IUA images remains a clinical challenge and is needed to avoid inappropriate surgery. Our study aimed to evaluate deep learning as a method to classify 3D-TVUS of IUA or non-IUA images taken with panoramic technology.<h4>Methods</h4>After meeting an inclusion/exclusion criteria, a total of 4,401 patients we ...[more]

PMID: 37064348

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Dataset Information

Artificial intelligence diagnosis of intrauterine adhesion by 3D ultrasound imaging: a prospective study.

Background

Methods

Results

Conclusions

Publications

Artificial intelligence diagnosis of intrauterine adhesion by 3D ultrasound imaging: a prospective study.

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