In recent years, with the growth of life pressure and rhythm, the number of patients with lumbar disc herniation has been increasing rapidly, and the age of the affected population has been decreasing, which has had a great impact on people’s daily work, study and life. This article aims to study the diagnosis of lumbar disc herniation based on artificial intelligence neural network. The diagnosis of lumbar disc herniation is mainly diagnosed by imaging, but there are errors in the accuracy of the diagnosis of lumbar disc herniation and the detection of special types of lumbar disc herniation. In this work, this paper proposes BP neural network and fuzzy neural network algorithms and studies these algorithms. Through algorithm improvement and neural network model construction, an intelligent medical diagnosis system is designed through the model, simulation experiments are carried out on the diagnosis of lumbar disc herniation, and the pathological characteristics of lumbar disc herniation are analyzed. The experimental results in this paper show that based on the improved artificial intelligence neural network algorithm in this paper, the diagnosis rate of lumbar disc herniation is close to 96.15%, with an average error of 0.06556. The intelligent medical diagnosis system designed in this paper forms the basis of system decision-making based on expert diagnosis history, can share expert experience, facilitate doctors to assist diagnosis or patient self-diagnosis, reduce expert expenses and save costs.

Huy Phan. Diagnosis of Lumbar Intervertebral Disc Herniation and Pathological Characteristics Based on Artificial Intelligence Neural Network. International Journal of Health and Pharmaceutical Medicine (2023), Vol. 4, Issue 1: 58-76. https://doi.org/10.38007/IJHPM.2023.040106.

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