Ovarian cancer is one of the three most common gynecological cancers in the world, and is regarded as a priority in terms of women's cancer. In the past few years, many researchers have attempted to develop and apply artificial intelligence (AI) techniques to multiple clinical scenarios of ovarian cancer, especially in the field of medical imaging. AI-assisted imaging studies have involved computer tomography (CT), ultrasonography (US), and magnetic resonance imaging (MRI). In this review, we perform a literature search on the published studies that using AI techniques in the medical care of ovarian cancer, and bring up the advances in terms of four clinical aspects, including medical diagnosis, pathological classification, targeted biopsy guidance, and prognosis prediction. Meanwhile, current status and existing issues of the researches on AI application in ovarian cancer are discussed.
Artificial Intelligence ; Female ; Humans ; Magnetic Resonance Imaging ; Ovarian Neoplasms/diagnostic imaging* ; Prognosis ; Tomography, X-Ray Computed

Objective: To evaluate the feasibility and accuracy of deep learning in CT image segmentation and further lesion-volume assessment of spontaneous intracerebral hemorrhage. Methods: A total of 1 223 cases of spontaneous intracerebral hemorrhage including parenchymal hemorrhage, ventricular hemorrhage, subarachnoid hemorrhage and mixture hemorrhage, from April 2016 to April 2018 in Tianjin Medical University General Hospital, were retrospectively enrolled and analyzed. The patients were randomly divided into training set (905 cases), validation set (156 cases) and test set (162 cases), among each group, the number of parenchymal hemorrhage was 498, 107 and 100, respectively. The bleeding area manually outlined by physician was served as the reference standard to build the segmentation model and to evaluate the performance of the validation set. Patients were divided into 3 groups according to the volume calculated by reference standard. The volume of hematoma in group 1 was less than 5 ml, while group 2 was 5-25 ml, and group 3 was more than 25 ml. Comparison of the hematoma volume calculated by segmentation model and that calculated by ABC/2 formula was conducted in 97 simple intraparenchymal hemorrhage cases. Results: In 162 cases of test set, the Dice coefficients of the segmentation model were 0.87, 0.85, 0.67 and 0.77 in parenchymal hemorrhage, intraventricular hemorrhage, subarachnoid hemorrhage and mixture hemorrhage, respectively. The estimated hematoma volume in the 97 intraparenchymal hemorrhage cases calculated by the segmentation model was (29.55±37.69) ml, and that calculated by the ABC/2 formula was (24.04±31.22) ml. Compared with reference standard, the absolute errors of three segmentation model were (0.52±0.54), (1.53±1.22) and (7.93±8.49) ml in group 1, 2 and 3 respectively. The absolute errors of the ABC/2 formula were (0.68±0.60), (3.16±2.90) and (19.31±17.23) ml in group 1, 2 and 3. Conclusion Deep learning based segmentation model improved detection of intraparenchymal hematoma volume, compared with ABC/2 formula.