Liawrungrueang Wongthawat, Cholamjiak Watcharaporn, Sarasombath Peem, Jitpakdee Khanathip, Kotheeranurak Vit
Department of Orthopaedics, School of Medicine, University of Phayao, Phayao, Thailand.
Department of Mathematics, School of Science, University of Phayao, Phayao, Thailand.
Spine Surg Relat Res. 2024 Aug 6;8(6):552-559. doi: 10.22603/ssrr.2024-0154. eCollection 2024 Nov 27.
Intervertebral disc degeneration (IDD) is a primary cause of chronic back pain and disability, highlighting the need for precise detection and grading for effective treatment. This study focuses on developing and validating a convolutional neural network (CNN) with a You Only Look Once (YOLO) architecture model using the Pfirrmann grading system to classify and grade lumbar intervertebral disc degeneration based on magnetic resonance imaging (MRI) scans.
We developed a deep learning model trained on a dataset of anonymized MRI studies of patients with symptomatic back pain. MRI images were segmented and annotated by radiologists according to the Pfirrmann grading for the datasets. The segmentation MRI-disc image dataset was prepared for three groups: a training set (1,000), a testing set (500), and an external validation set (500) to assess model generalizability without overlapping images. The model's performance was evaluated using accuracy, sensitivity, specificity, F1 score, prediction error, and ROC-AUC.
The AI model showed high performance across all metrics. For Grade I IDD, the model achieved an accuracy of 97%, 95%, and 92% in the training, testing, and external validation sets, respectively. For Grade II, the sensitivity was 100% in both training and testing sets and 98% in the validation set. For Grade III, the specificity was 95.4% in the training set and 94% in both testing and validation sets. For Grade IV, the F1 score was 97.77% in the training set and 95% in both testing and validation sets. For Grade V, the prediction error was 2.3%, 2%, and 2.5% in the training, testing, and validation sets, respectively. The overall ROC-AUC was 97%, 92%, and 95% in the training, testing, and validation sets, respectively.
The AI-based classification model exhibits high accuracy, sensitivity, and specificity in detecting and grading lumbar IDD using the Pfirrmann grading. AI has significantly enhanced diagnostic precision and reliability, providing a powerful tool for clinicians in managing IDD. The potential impact is substantial, although further clinical validation is necessary before integrating this model into routine practice.
椎间盘退变（IDD）是慢性背痛和残疾的主要原因，这凸显了精确检测和分级以进行有效治疗的必要性。本研究重点在于开发并验证一种具有“你只看一次”（YOLO）架构模型的卷积神经网络（CNN），该模型使用Pfirrmann分级系统，基于磁共振成像（MRI）扫描对腰椎间盘退变进行分类和分级。
我们开发了一个深度学习模型，该模型在一组有症状背痛患者的匿名MRI研究数据集上进行训练。放射科医生根据数据集的Pfirrmann分级对MRI图像进行分割和标注。分割后的MRI椎间盘图像数据集被分为三组：训练集（1000例）、测试集（500例）和外部验证集（500例），以评估模型的通用性且图像不重叠。使用准确率、灵敏度、特异性、F1分数、预测误差和ROC-AUC来评估模型的性能。
人工智能模型在所有指标上均表现出高性能。对于I级IDD，该模型在训练集、测试集和外部验证集中的准确率分别达到97%、95%和92%。对于II级，训练集和测试集的灵敏度均为100%，验证集为98%。对于III级，训练集的特异性为95.4%，测试集和验证集均为94%。对于IV级，训练集的F1分数为97.77%，测试集和验证集均为95%。对于V级，训练集、测试集和验证集的预测误差分别为2.3%、2%和2.5%。训练集、测试集和验证集的总体ROC-AUC分别为97%、92%和95%。
基于人工智能的分类模型在使用Pfirrmann分级检测和分级腰椎IDD方面表现出高准确率、灵敏度和特异性。人工智能显著提高了诊断的准确性和可靠性，为临床医生管理IDD提供了一个强大的工具。尽管在将该模型整合到常规实践之前还需要进一步的临床验证，但其潜在影响是巨大的。
