Developing a Prediction System for Emergency Cesarean Sections Using White-Boxed Artificial Intelligence.

2023 Fiscal Year Final Research Report

• Project/Area Number: 22K16845
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 56040:Obstetrics and gynecology-related
• Research Institution: Osaka Medical and Pharmaceutical University
• Principal Investigator: Nagayasu Yoko 大阪医科薬科大学, 医学部, 講師 (80843408)
• Project Period (FY): 2022-04-01 – 2024-03-31
• Keywords: 緊急帝王切開 / 人工知能 / 説明可能AI

Outline of Final Research Achievements

The academic significance of this study lies in providing new insights into the prediction of emergency cesarean sections (CS), potentially enhancing the quality of existing obstetric care. It enables healthcare professionals to perform more accurate risk assessments, contributing to safer deliveries.
The social significance of this predictive model is the expected enhancement of risk management. Specifically, the model allows for the early detection of the need for emergency CS, enabling swift responses to ensure the safety of both mother and child. Additionally, it contributes to the efficient use of medical resources by reducing unnecessary cesarean sections, thereby lowering medical costs and alleviating the workload on hospitals. This improvement in perinatal care and optimization of the healthcare system is highly anticipated.

Free Research Field

周産期

Academic Significance and Societal Importance of the Research Achievements

学術的意義は、緊急帝王切開の予測に関する新たな知見を提供し、既存の産科医療の質を向上させる可能性がある。また、医療従事者はより正確なリスク評価を行うことが可能となり、より安全な分娩を実現する一助となる。社会的意義は、この予測モデルを導入することでリスク管理の強化が期待される。具体的には、緊急帝王切開の必要性を早期に察知することができ、母子の安全を確保するための迅速な対応が可能となり得る。また、医療リソースの効率的な活用にも寄与し、不要な帝王切開を減少させることで、医療費の削減や病院の業務負荷の軽減にも繋がる。このように、周産期医療の向上と医療システムの効率化に貢献することが期待される。