探讨一种基于深度卷积神经网络（deep convolutional neural network, DCNN）的肌骨超声图像分析在肌筋膜疼痛综合征（myofascial pain syndrome, MPS）智能诊断中的应用效果。

选取2021年5月至2025年6月于本院就诊的500例颈肩背痛患者为研究对象，采集其肌骨超声影像资料。剔除不合格图像后，最终纳入481例患者的1 692幅超声图像，以此构建图像数据集，3名主治医师对图像进行标记。将数据集分为训练集和测试集，分别有1 383张、309张肌骨超声图像，采用深度学习算法构建诊断模型（DCNN1、DCNN2、DCNN3），检测模型识别活跃性、潜伏性肌筋膜触发点（myofascial trigger points, MTrPs）的准确率。另外选取200张独立于数据库的可用的颈肩背痛患者的肌骨超声检查图像，由3位主任超声医师进行评估，记录耗时，并与模型诊断结果进行比较。再选取独立于数据库的另外130张可用的颈肩背痛患者的肌骨超声检查图像，由6位超声住院医师在模型辅助前后分别对图像进行评估，对前后两次的诊断结果进行比较。

DCNN3模型诊断活跃性、潜伏性MTrPs的准确率分别为0.983、0.876。模型识别活跃性MTrPs与潜伏性MTrPs的准确率与3位主任医师诊断水平相当，模型评估图像的耗时明显短于主任医师（P<0.05）。经模型辅助后，住院医师对图像诊断的准确率明显提高，诊断图像的耗时显著减少（P<0.013）。

该智能评估模型诊断活跃性、潜伏性MTrPs的准确率较高，且能明显提高诊断效率，能有效辅助临床医师对颈肩背部MPS进行正确评估。

Myofascial pain syndrome (MPS) is a Myofascial strain disease characterized by myofascial trigger points (MTrPs), presenting with local pain, dysfunction, and decreased muscle strength. Epidemiological data show that the proportions of visits to basic medical care and pain clinics are 15% and 90%, respectively. The incidence rate in China ranges from 30% to 93%, and the difference is due to the non-uniform diagnostic criteria. At present, clinical diagnosis mainly relies on doctors' palpation, which is highly subjective and lacks objective detection methods. With the changing modern lifestyle (such as long-term head-down posture), MPS is showing a trend of younger onset, but public awareness and diagnostic systems remain insufficient. MTrPs, as a core pathological feature, are prone to occur in the neck, shoulders, and back. During its active period, a vicious cycle of "pain - limited movement- new trigger point" can be formed. Ultrasonic technology has become a promising diagnostic tool due to its advantages, such as real-time, non-invasive imaging, but is constrained by factors including operator experience and equipment performance. In recent years, deep learning, especially transfer learning and convolutional neural network models, has demonstrated outstanding performance in medical image analysis, capable of automatically extracting features from ultrasound images and providing objective decision support.

To explore the application effect of a musculoskeletal ultrasound image analysis based on a deep convolutional neural network (DCNN) in the intelligent diagnosis of MPS.

Five hundred patients with neck, shoulder, and back pain who visited our hospital from May 2021 to June 2025 were selected as the research subjects, and their musculoskeletal ultrasound image data were collected. After eliminating unqualified images, 1,692 ultrasound images from 481 patients were included to construct an image dataset, and three attending physicians labeled them. The dataset was split into a training set and a test set, each containing 1 383 and 309 musculoskeletal ultrasound images, respectively. Deep learning algorithms were used to construct diagnostic models (DCNN1, DCNN2, DCNN3) to assess their accuracy in identifying active and latent MTrPs. In addition, 200 musculoskeletal ultrasound images of patients with neck, shoulder, and back pain, independent of the database, were selected and evaluated by three chief ultrasound physicians. The recording took a long time and was compared with the model diagnosis results. Another 130 independent musculoskeletal ultrasound examination images of patients with neck, shoulder, and back pain were selected from the database. Six ultrasound resident physicians evaluated the images before and after model assistance, respectively, and the diagnostic results were compared.

The accuracy of the DCNN3 model in diagnosing active and latent MTrPs was 0.983 and 0.876, respectively. The model's accuracy in identifying active and latent MTrPs was comparable to the diagnostic level of three chief physicians, and the time the model took to evaluate images was significantly shorter than that of the chief physicians (P < 0.05). With the model's assistance, the accuracy of image diagnosis by resident physicians was significantly improved, and the time required to diagnose images was significantly reduced (P < 0.013) .

This intelligent assessment model has high accuracy in diagnosing active and latent MTrPs and can significantly improve diagnostic efficiency. It can effectively assist clinicians in correctly evaluating MPS in the neck, shoulders, and back.