โก Quick Summary
This narrative review explores the application of machine learning (ML) and deep learning (DL) in the diagnosis and treatment of inguinal hernia (IH), highlighting their potential to enhance diagnostic accuracy and optimize surgical protocols. The findings suggest that these technologies can significantly improve patient outcomes and revolutionize hernia management.
๐ Key Details
- ๐ Focus: Application of ML and DL in inguinal hernia management
- โ๏ธ Technologies: Machine Learning (ML) and Deep Learning (DL)
- ๐ฅ Clinical Applications: Risk prediction for surgical complications and intraoperative navigation
- ๐ Data Types: Structured and unstructured data, including medical images
๐ Key Takeaways
- ๐ค ML models can predict risks of postoperative complications such as infections and thromboembolism.
- ๐ธ DL excels in processing unstructured data, enhancing medical imaging and surgical training.
- ๐ Generative AI shows promise in medical consultations but needs further validation for accuracy.
- ๐ Future prospects include real-time feedback and interdisciplinary collaboration in hernia management.
- ๐ Improved outcomes are anticipated through optimized surgical protocols and personalized treatment plans.
๐ Background
The rapid advancement of artificial intelligence (AI) technologies has opened new avenues in healthcare, particularly in the diagnosis and treatment of conditions like inguinal hernia. Traditional methods often lack precision and can lead to varied patient outcomes. The integration of ML and DL into clinical practice promises to enhance diagnostic capabilities and streamline treatment processes.
๐๏ธ Study
This review synthesizes current research on the applications of ML and DL in managing inguinal hernia. It highlights how these technologies can predict surgical risks, improve diagnostic accuracy, and facilitate real-time feedback during surgical procedures. The authors, Liu Y et al., provide a comprehensive overview of the potential benefits and challenges associated with implementing these technologies in clinical settings.
๐ Results
The review indicates that ML models are effective in predicting various postoperative complications, including surgical site infections and venous thromboembolism. Furthermore, DL techniques have shown remarkable success in identifying anatomical landmarks during surgery, which enhances the precision of surgical interventions and supports training for new surgeons.
๐ Impact and Implications
The integration of ML and DL into the management of inguinal hernia could lead to a paradigm shift in surgical practices. By improving diagnostic accuracy and optimizing surgical protocols, these technologies have the potential to enhance patient outcomes significantly. As healthcare continues to evolve, the collaboration between technology and medicine will be crucial in addressing complex medical challenges.
๐ฎ Conclusion
This review underscores the transformative potential of machine learning and deep learning in the diagnosis and treatment of inguinal hernia. By leveraging these advanced technologies, healthcare professionals can achieve better diagnostic precision and improved patient outcomes. The future of hernia management looks promising, and ongoing research will be essential to fully realize the benefits of AI in clinical practice.
๐ฌ Your comments
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Application of machine learning and deep learning in the diagnosis and treatment of inguinal hernia: a narrative review.
Abstract
With the rapid development of artificial intelligence (AI) technology, its application in the diagnosis and treatment of inguinal hernia (IH) has gradually become a research hotspot. As core components of AI, machine learning (ML) and deep learning (DL) demonstrate tremendous potential in medical imaging, disease prediction, and personalized treatment planning. Currently, models developed using ML can effectively predict the risks of postoperative surgical site infection, surgical site occurrence, intestinal resection in incarcerated IH, and postoperative lower extremity venous thromboembolism. DL, as a subset of ML, excels in processing unstructured data such as images and videos. It utilizes deep neural networks to automatically extract data features, thereby enhancing medical image diagnosis and intraoperative navigation capabilities. Studies have shown that DL is highly effective in identifying anatomical landmarks during surgery, which facilitates real-time feedback and surgical training. Generative AI, built on ML theories, shows promise in medical consultations, but its accuracy and reliability require further validation. Overall, ML and DL are revolutionizing the management of IH by improving diagnostic accuracy, optimizing surgical protocols, and enhancing patient outcomes. Future prospects include data integration, real-time feedback, and interdisciplinary collaboration. This article provides a review of the applications of ML and DL in the diagnosis and treatment of IH, offering references for clinical practice and technological innovation.
Author: [‘Liu Y’, ‘Xie TH’, ‘Fu Y’, ‘Ha SN’, ‘Jin XS’, ‘Ren XX’]
Journal: Front Med (Lausanne)
Citation: Liu Y, et al. Application of machine learning and deep learning in the diagnosis and treatment of inguinal hernia: a narrative review. Application of machine learning and deep learning in the diagnosis and treatment of inguinal hernia: a narrative review. 2026; 13:1743178. doi: 10.3389/fmed.2026.1743178