Deep Learning Based on MR Imaging for Predicting Outcome of Uterine Fibroid Embolization

Luo, Yongheng; Xi, I.; Wang, Robin; Abdallah, Hatem Omar; Wu, Jing; Vance, Ansar Z.; Chang, Ken; Kohi, Maureen P.; Jones, Lisa P.; Reddy, Shilpa; Zhang, Zishu; Bai, Harrison X.; Shlansky-Goldberg, Richard D.

doi:10.1016/j.jvir.2019.11.032

Cited by 18 publications

(12 citation statements)

References 20 publications

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“…Images were padded and resized to 512 by 512 pixels. Single-channel images were converted to 3-channel images by repeating the single channel 3 times [ 12 , 19 , 20 ]. Pixel values were normalized by scaling values into the range [0, 1], then subtracting (0•485, 0•456, 0•406) and dividing by (0•229, 0•224, 0•225) channel-wise.…”

Section: Methodsmentioning

confidence: 99%

Deep learning-based classification of primary bone tumors on radiographs: A preliminary study

Pan

Bao

et al. 2020

eBioMedicine

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Background: To develop a deep learning model to classify primary bone tumors from preoperative radiographs and compare performance with radiologists. Methods: A total of 1356 patients (2899 images) with histologically confirmed primary bone tumors and preoperative radiographs were identified from five institutions' pathology databases. Manual cropping was performed by radiologists to label the lesions. Binary discriminatory capacity (benign versus not-benign and malignant versus not-malignant) and three-way classification (benign versus intermediate versus malignant) performance of our model were evaluated. The generalizability of our model was investigated on data from external test set. Final model performance was compared with interpretation from five radiologists of varying level of experience using the Permutations tests. Findings: For benign vs. not benign, model achieved area under curve (AUC) of 0894 and 0877 on cross-validation and external testing, respectively. For malignant vs. not malignant, model achieved AUC of 0907 and 0916 on cross-validation and external testing, respectively. For three-way classification, model achieved 721% accuracy vs. 746% and 721% for the two subspecialists on cross-validation (p = 003 and p = 052, respectively). On external testing, model achieved 734% accuracy vs. 693%, 734%, 731%, 679%, and 634% for the two subspecialists and three junior radiologists (p = 014, p = 089, p = 093, p = 002, p < 001 for radiologists 1À5, respectively). Interpretation: Deep learning can classify primary bone tumors using conventional radiographs in a multiinstitutional dataset with similar accuracy compared to subspecialists, and better performance than junior radiologists.

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Section: Methodsmentioning

confidence: 99%

Deep learning-based classification of primary bone tumors on radiographs: A preliminary study

Pan

Bao

et al. 2020

eBioMedicine

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“…Dilna et al used the MBF-CDNN method to detect uterine fibroids in ultrasound images [ 27 ]. Several studies have investigated MRIs of endometrial fibroids using deep-learning-based methods [ 28 , 29 , 30 , 31 ]. Overall, these studies demonstrate the potential of deep learning for improving the detection of uterine lesions.…”

Section: Discussionmentioning

confidence: 99%

Automated Detection of Endometrial Polyps from Hysteroscopic Videos Using Deep Learning

Zhao

Yuan

et al. 2023

Diagnostics

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Endometrial polyps are common gynecological lesions. The standard treatment for this condition is hysteroscopic polypectomy. However, this procedure may be accompanied by misdetection of endometrial polyps. To improve the diagnostic accuracy and reduce the risk of misdetection, a deep learning model based on YOLOX is proposed to detect endometrial polyps in real time. Group normalization is employed to improve its performance with large hysteroscopic images. In addition, we propose a video adjacent-frame association algorithm to address the problem of unstable polyp detection. Our proposed model was trained on a dataset of 11,839 images from 323 cases provided by a hospital and was tested on two datasets of 431 cases from two hospitals. The results show that the lesion-based sensitivity of the model reached 100% and 92.0% for the two test sets, compared with 95.83% and 77.33%, respectively, for the original YOLOX model. This demonstrates that the improved model may be used effectively as a diagnostic tool during clinical hysteroscopic procedures to reduce the risk of missing endometrial polyps.

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“…Thus, researchers and clinicians who rely on radiological methods to make diagnosis or manage treatment optimization will benefit further through automation-based algorithms. Predicting clinical outcomes, for example, of uterine fibroid embolization (UFE) based on pre-procedural magnetic resonance imaging (MRI) scans, 44 using time-lapse images to identify high-quality embryos in IVF 45 and evaluating myometrial invasion (MI) depth based on endometrial cancer MRI 46 are a few examples of DL model applicability within the OB/GYN domain. DL techniques are also widely used in cases other than analysing images.…”

Section: Data Analysis Frameworkmentioning

confidence: 99%

“…MRI is used before and after UFE to determine response to treatment. Luo et al 44 have used a DL model to predict clinical outcomes of UFE based on pre-procedure MRI scans. They trained and tested a Residual Convolutional Neural Network (ResNet) to predict fibroid volume reduction and clinical outcome using a retrospective cohort of 409 patients with 727 fibroids at a single institution.…”

Section: Ob/gyn-mh Sequelaementioning

confidence: 99%

Artificial intelligence: A rapid case for advancement in the personalization of Gynaecology/Obstetric and Mental Health care

Delanerolle

Yang

Shetty

et al. 2021

Womens Health (Lond Engl)

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To evaluate and holistically treat the mental health sequelae and potential psychiatric comorbidities associated with obstetric and gynaecological conditions, it is important to optimize patient care, ensure efficient use of limited resources and improve health-economic models. Artificial intelligence applications could assist in achieving the above. The World Health Organization and global healthcare systems have already recognized the use of artificial intelligence technologies to address ‘system gaps’ and automate some of the more cumbersome tasks to optimize clinical services and reduce health inequalities. Currently, both mental health and obstetric and gynaecological services independently use artificial intelligence applications. Thus, suitable solutions are shared between mental health and obstetric and gynaecological clinical practices, independent of one another. Although, to address complexities with some patients who may have often interchanging sequelae with mental health and obstetric and gynaecological illnesses, ‘holistically’ developed artificial intelligence applications could be useful. Therefore, we present a rapid review to understand the currently available artificial intelligence applications and research into multi-morbid conditions, including clinical trial-based validations. Most artificial intelligence applications are intrinsically data-driven tools, and their validation in healthcare can be challenging as they require large-scale clinical trials. Furthermore, most artificial intelligence applications use rate-limiting mock data sets, which restrict their applicability to a clinical population. Some researchers may fail to recognize the randomness in the data generating processes in clinical care from a statistical perspective with a potentially minimal representation of a population, limiting their applicability within a real-world setting. However, novel, innovative trial designs could pave the way to generate better data sets that are generalizable to the entire global population. A collaboration between artificial intelligence and statistical models could be developed and deployed with algorithmic and domain interpretability to achieve this. In addition, acquiring big data sets is vital to ensure these artificial intelligence applications provide the highest accuracy within a real-world setting, especially when used as part of a clinical diagnosis or treatment.

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Deep Learning Based on MR Imaging for Predicting Outcome of Uterine Fibroid Embolization

Cited by 18 publications

References 20 publications

Deep learning-based classification of primary bone tumors on radiographs: A preliminary study

Deep learning-based classification of primary bone tumors on radiographs: A preliminary study

Automated Detection of Endometrial Polyps from Hysteroscopic Videos Using Deep Learning

Artificial intelligence: A rapid case for advancement in the personalization of Gynaecology/Obstetric and Mental Health care

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