What is the clinical evidence from this study?

Study design: Other. Population: Upper crossed syndrome. Intervention: CNN-LSTM deep learning risk prediction model vs. Single-LSTM model. Primary outcome: Model accuracy.

What question did this study set out to answer?

The study aims to develop a deep learning-based model for early risk prediction and prevention of upper crossed syndrome.

June 4, 2026Open Access

Deep Learning-Driven Risk Prediction and Early Warning Model for Upper Crossed Syndrome

Key Result

A CNN-LSTM deep learning model achieved 92.3% accuracy in predicting upper crossed syndrome, outperforming the best single-LSTM model by 4.5 percentage points.

Key Points

The study aims to develop a deep learning-based model for early risk prediction and prevention of upper crossed syndrome.
Review of influencing factors and current assessment methods for upper crossed syndrome.
Development of a CNN-LSTM based model using multimodal data preprocessing.
Comparative experiments conducted on clinical and simulated datasets.
The fusion model achieved the highest accuracy of 92.3%, outperforming the best single-LSTM model by 4.5 percentage points.
Precision and recall rates were 91.8 and 93.1, respectively, effectively balancing false negatives and positives.
The model demonstrated reliable support for early intervention strategies in upper crossed syndrome.

Structured PICO

Does a deep learning-driven CNN-LSTM model improve risk prediction accuracy for upper crossed syndrome compared to single-LSTM models?

Population

Clinical datasets and simulated datasets for upper crossed syndrome

Intervention

Deep learning-driven risk prediction and early warning model using a convolutional neural network-long short-term memory network (CNN-LSTM) with multimodal data preprocessing

Comparator

Single-LSTM model and other baseline models

Outcome

Model accuracy, precision, and recallsurrogate

A novel CNN-LSTM deep learning model improves the accuracy of risk prediction and early warning for upper crossed syndrome compared to traditional single-LSTM models.

Main Result

Absolute Event Rate: 92.3% vs 87.8%

Abstract

As a result of unhealthy lifestyles that include spending increased time sitting at office and staring at their computer screens, cases of upper crossed syndrome continue to increase year after year with serious consequences on the muscle performance, physical appearance, and quality of life of individuals. The importance of early risk prediction and early warning is of much value in disease prevention and control. The existing risk evaluation of the upper crossed syndrome is largely based on the conventional scales or manual observation of physical symptoms that are characterized by high subjectivity, low efficiency, and delayed early warning and therefore, it is hard to obtain accurate and early prevention and control. The structure of this paper is as follows: First, it will conduct a review of the factors that influence the upper crossed syndrome, and some of the current assessment methods to establish the essential requirements to construct a model. Second, it builds a risk prediction and early warning model on the basis of deep learning, enhances the quality of data with a multimodal data (postural parameters, lifestyle habits, electromyographic signals, etc.) preprocessing module, and fuses multi-dimensional features with a better convolutional neural network-long short-term memory network (CNN-LSTM) to establish an adaptive threshold early warning mechanism. Lastly, it performs comparative experiments on clinical datasets and simulated datasets to check the performance of the model. Experiments have demonstrated that the proposed fusion model is the most effective in terms of all the evaluation metrics with the highest accuracy of 92.3% which is 4.5 percentage points high in comparison to the best single-LSTM model. The precision and recall are 91.8 and 93.1 respectively as well balanced between the problems of "overlooking high-risk cases" and "false identification of healthy cases" respectively, and have reliable technical support in early intervention in the upper crossed syndrome.