Establishment and Clinical Application of AI-based Multimodal Diagnosis System for Ovarian Tumors

Methods/Design Study Aim The first objective of this study are to construct a multimodal ovarian tumor public dataset that includes ultrasound, MRI images, and clinical indicator data to support AI-driven tasks. This dataset will integrate diverse types of medical data to facilitate comprehensive AI analysis and model training. By employing convolutional neural network (CNN) technology, the study seeks to achieve intelligent multiclass recognition of multimodal images. This involves extracting and analyzing features from imaging data and integrating them with multidimensional clinical data at the feature level, with the goal of training and validating an auxiliary diagnostic model capable of accurately predicting at least the ten most common pathological types of ovarian tumors. Additionally, the study will utilize privacy-preserving computing and federated learning methods to conduct multicenter, prospective optimization and validation of the model. These methods will ensure data security and privacy while enabling collaborative model training. The ultimate goal is to develop a clinical auxiliary diagnostic system that can predict the pathological types of most ovarian tumors and be implemented in clinical settings. This comprehensive approach aims to leverage the strengths of AI technology in medical imaging, ensuring data security while enhancing the accuracy and reliability of ovarian tumor diagnosis.

Study Setting This study is conducted in Beijing, China. The study subjects are sourced from Beijing Shijitan Hospital, Beijing Friendship Hospital, and Beijing Obstetrics and Gynecology Hospital, all of which are affiliated with Capital Medical University. These hospitals provide a diverse patient population, which supports the generalizability of the study findings.

Study Subjects The study subjects are patients diagnosed with ovarian tumors who have undergone surgical treatment. The cases used for developing the predictive model are sourced from patients who were consecutively enrolled between January 2018 and January 2023. Clinical application validation cases will be prospectively collected from patients between June 2023 and June 2025. The inclusion criteria for both parts of the study are as follows: (1) patients diagnosed with ovarian tumors who have undergone surgical treatment; (2) patients with complete imaging data (ultrasound or MRI) and tumor marker results obtained within three months prior to surgery. Additionally, the second part requires informed consent from the participants. The exclusion criteria are as follows: (1) patients with surgical pathology not originating from the ovary; (2) duplicate cases; (3) patients who have received chemotherapy or radiotherapy; (4) recurrent cases; (5) poor-quality imaging of ovarian lesions; and (6) incomplete case information.

Ethics The study protocol has been reviewed and approved by the Ethics Committee of Beijing Shijitan Hospital, affiliated with Capital Medical University. The ethics approval number is: [provide number]. The study will be conducted in accordance with the principles of the Declaration of Helsinki. For the model development phase, retrospective cases have been granted a waiver of informed consent. For the clinical validation phase, all participants will read an informed consent form before the study begins, voluntarily agree to participate, and sign the informed consent form.

Data Collection The investigators will collect imaging data (pelvic ultrasound, pelvic MRI) and clinical data (general information, medical history, family history, physical examination, laboratory tests, pathology reports, clinical diagnosis, etc.) from the study subjects. This will help construct a multimodal ovarian tumor dataset with classification and segmentation annotations, providing AI dataset support for the subsequent development of multimodal ovarian tumor auxiliary diagnostic models. As the research progresses, the investigators aim to achieve flexible expansion and deep mining of the dataset, establish standardized protocols for multimodal datasets, and enable effective data sharing.