APPROACHES TO RISK ASSESSMENT AND EARLY HERNIA DETECTION USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
DOI:
https://doi.org/10.61841/306wz009Keywords:
Artificial Intelligence, Support Vector Machine, Machine Learning, Risk Assessment, Hernia Detection.Abstract
Hernia detection is one of the critical medical diagnostics, and hence, promising advancements are made in early prediction and risk assessment using AI and ML techniques. In this paper, different AI and ML models are assessed, ranging from deep learning to traditional techniques, classifying patients into high-risk or low-risk categories. It discusses performance of models like CNNs, SVM, RF, RNN, and ANN while dealing with medical images, the possibility of training more than one model for better accuracy. There are some challenges ahead for implementing AI in a clinical setting, such as the privacy and validation of data; this work points to future potential in hernia detection.
References
Prieto, J. M., Harting, M. T., Calvo, R. Y., Carroll, J. M., Sykes, A. G., Ignacio, R. C., ... & Congenital Diaphragmatic Hernia Study Group. (2021). Identifying risk factors for enteral access procedures in neonates with congenital diaphragmatic hernia: A novel risk-assessment score. Journal of Pediatric Surgery, 56(6), 1130-1134.
Ortega-Deballon, P., Renard, Y., De Launay, J., Lafon, T., Roset, Q., & Passot, G. (2023). Incidence, risk factors, and burden of incisional hernia repair after abdominal surgery in France: a nationwide study. Hernia, 27(4), 861-871.
Soppe, S., Slieker, S., Keerl, A., Muller, M. K., Wirsching, A., & Nocito, A. (2022). Emergency repair and smoking predict recurrence in a large cohort of ventral hernia patients. Hernia, 26(5), 1337-1345.
O'Brien, W. J., Ramos, R. D., Gupta, K., & Itani, K. M. (2021). Neural network model to detect long-term skin and soft tissue infection after hernia repair. Surgical Infections, 22(7), 668-674.
Ashuvanth, S., Anandhi, A., & Sureshkumar, S. (2022). Validation of ventral hernia risk score in predicting surgical site infections. Hernia, 26(3), 911-917.
Harada, G. K., Siyaji, Z. K., Mallow, G. M., Hornung, A. L., Hassan, F., Basques, B. A., ... & An, H. S. (2021). Artificial intelligence predicts disk re-herniation following lumbar microdiscectomy: Development of the “RAD” risk profile. European Spine Journal, 30(8), 2167-2175.
Lima, D. L., Kasakewitch, J., Nguyen, D. Q., Nogueira, R., Cavazzola, L. T., Heniford, B. T., &Malcher, F. (2024). Machine learning, deep learning and hernia surgery. Are we pushing the limits of abdominal core health? A qualitative systematic review. Hernia, 1-8.
Hassan, A. M., Lu, S. C., Asaad, M., Liu, J., Offodile, A. C., Sidey-Gibbons, C., & Butler, C. E. (2022). Novel machine learning approach for the prediction of hernia recurrence, surgical complication, and 30-day readmission after abdominal wall reconstruction. Journal of the American College of Surgeons, 234(5), 918-927.
Dai, T., Bao, M., Zhang, M., Wang, Z., Tang, J., & Liu, Z. (2024). A risk prediction model based on machine learning algorithm for parastomal hernia after permanent colostomy. BMC Medical Informatics and Decision Making, 24(1), 224.
Taha, A., Enodien, B., Frey, D. M., & Taha-Mehlitz, S. (2022). The development of artificial intelligence in hernia surgery: a scoping review. Frontiers in Surgery, 9, 908014.
Shan, Z. M., Ren, X. S., Shi, H., Zheng, S. J., Zhang, C., Zhuang, S. Y., ... & Xie, X. H. (2024). Machine learning prediction model and risk factor analysis of reoperation in recurrent lumbar disc herniation patients after percutaneous endoscopic lumbar discectomy. Global Spine Journal, 14(8), 2240-2251.
Assaf, D., Rayman, S., Segev, L., Neuman, Y., Zippel, D., & Goitein, D. (2022). Improving pre-bariatric surgery diagnosis of hiatal hernia using machine learning models. Minimally Invasive Therapy & Allied Technologies, 31(5), 760-767.
Talwar, A. A., Desai, A. A., McAuliffe, P. B., Broach, R. B., Hsu, J. Y., Liu, T., ... & Fischer, J. P. (2024). Optimal computed tomography-based biomarkers for prediction of incisional hernia formation. Hernia, 28(1), 17-24.
Conte, L., Amodeo, I., De Nunzio, G., Raffaeli, G., Borzani, I., Persico, N., ... & Cavallaro, G. (2024). A machine learning approach to predict mortality and pulmonary hypertension severity in newborns with congenital diaphragmatic hernia. medRxiv, 2024-07.
Yan, Y. D., Yu, Z., Ding, L. P., Zhou, M., Zhang, C., Pan, M. M., ... & Gu, Z. C. (2023). Machine learning to dynamically predict in-hospital venous thromboembolism after inguinal hernia surgery: results from the CHAT-1 study. Clinical and Applied Thrombosis/Hemostasis, 29, 10760296231171082.
Al Jalbout, N., Liteplo, A., &Shokoohi, H. (2022). Point-of care ultrasound in stratifying clinically evolving inguinal and femoral hernias. The Journal of Emergency Medicine, 63(2), 257-264.
Garcia, E. M., Pietryga, J. A., Kim, D. H., Fowler, K. J., Chang, K. J., Kambadakone, A. R., ... & Carucci, L. R. (2022). ACR appropriateness criteria hernia. Journal of the American College of Radiology, 19(11), S329-S340.
Dadashzadeh, E. R., Huckaby, L. V., Handzel, R., Hossain, M. S., Sanin, G. D., Anto, V. P., ... & Van Der Windt, D. J. (2022). The risk of incarceration during nonoperative management of incisional hernias: a population-based analysis of 30,998 patients. Annals of surgery, 275(2), e488-e495.
Amodeo, I., Borzani, I., Raffaeli, G., Persico, N., Amelio, G. S., Gulden, S., ... & Cavallaro, G. (2022). The role of magnetic resonance imaging in the diagnosis and prognostic evaluation of fetuses with congenital diaphragmatic hernia. European journal of pediatrics, 181(9), 3243-3257.
Zhang, J., Zhang, C. L., Kuang, L. Q., Li, X. G., Tang, W., & Wang, Y. (2021). Prediction of bowel obstruction caused by obturator hernia using risk factor categories on clinical characteristics and multidetector computed tomographic findings. Abdominal Radiology, 46, 4069-4078.
Niemiec, S. M., Louiselle, A. E., Phillips, R., Gien, J., Zaretsky, M. V., Derderian, S. C., ... & Meyers, M. L. (2023). Third-trimester percentage predicted lung volume and percentage liver herniation as prognostic indicators in congenital diaphragmatic hernia. Pediatric Radiology, 53(3), 479-486.
Puligandla, P., Skarsgard, E., Baird, R., Guadagno, E., Dimmer, A., Ganescu, O., ... & Chiu, P. (2024). Diagnosis and management of congenital diaphragmatic hernia: a 2023 update from the Canadian congenital diaphragmatic hernia collaborative. Archives of Disease in Childhood-Fetal and Neonatal Edition, 109(3), 239-252.
Shaikh(2024), The Investigation of Machine Learning And Deep Learning Classification of Internet of Things (IoT) Enabled Medical Devices. IJIRAE:International Journal of Innovative Research in Advanced Engineering, Volume 11, Issue 10 of 2024 pages 787-793
V Chunduri, SA Hannan, GM Devi, VK Nomula, V Tripathi, SS Rajest, “Deep Convolutional Neural Networks for Lung Segmentation for Diffuse Interstitial Lung Disease on HRCT and Volumetric CT, Optimizing Intelligent Systems for Cross-Industry Application, IGI Global Scientific Publishing, August 2024, Pages 335-350
Basurto, D., Russo, F. M., Papastefanou, I., Bredaki, E., Allegaert, K., Pertierra, A., ... & Deprest, J. (2022). Pulmonary hypertension in congenital diaphragmatic hernia: Antenatal prediction and impact on neonatal mortality. Prenatal Diagnosis, 42(10), 1303-1311.
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