The aim of this study was to design a modified ResNet 50 algorithm to achieve automatic classification model for pain expressions by elderly patients with hip fractures. We built a dataset by combining the advantages of deep learning in image recognition, using a hybrid of the Multi-Task Cascaded Convolutional Neural Networks (MTCNN). ResNet 50-based network framework utilized transfer learning to implement model function. We performed Bayesian optimization on the hyperparameters in the learning process. We calculated intraclass correlation between visual analog scale scores provided by nurses independently and those provided by pain expression evaluation assistant.The automatic pain expression recognition model for elderly patients with hip fractures, constructed using the algorithm, achieved an accuracy of 99.6% on the training set, 98.7% on the validation set, and 98.2% on the test set. The substantial kappa coefficient of 0.683 confirmed the PEEA's efficacy in pain assessment.This study demonstrates that the modified ResNet 50 algorithm with Bayesian optimization can be used to construct an automatic pain expression recognition model for elderly patients with hip fractures with higher accuracy.
Research Article
Automated Pain Assessment Increases Nurses’ Working Efficiency in elderly with hip fractures
https://doi.org/10.21203/rs.3.rs-3493454/v1
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Expression recognition
ResNet 50 network
MTCNN face detection
Bayesian optimization
pain assessment
Pain is often accompanied by changes in behavior, including facial expressions that have received significant attention[1]. Similar to expressions of emotion[2], facial expressions during pain play a critical role in communicating information about the experience[3]. Hip fracture is a common orthopedic injury that exponentially increases in incidence with age[4]. Intense preoperative pain after hip fractures is common and can lead to long-term effects, such as residual pain and poor joint function [5]. Accurate detection and estimation of pain is essential for pain management, such as whether and at what level analgesics are administered to the patient. Sometimes the pain on the patient's face is much worse than it really is[6], they are less accurately detected than the six basic emotions and are often mistaken for negative emotions characterized by similar facial muscle movements, such as disgust [7].
Facial expression of pain is one potential method for improving the detection and estimation of pain. It is the most salient form of pain behavior and communicates information about the sensory and affective components of the multidimensional experience of pain[8]. Evidence suggests that the core expression of pain is characterized by four facial muscle movements, including brow-lowering, orbit tightening, levator-contraction, and to a lesser extent, eye closure[9]. Facial expressions of pain are associated with different processes than self-report and provide complementary information that may be more valid in some circumstances, such as among individuals with cognitive or expressive impairments[10].
Face recognition is the process by which a computer determines an individual's identification based on their facial features. Traditional face recognition technology first calculates the feature description factors describing each individual's identity based on the distribution of each pixel in the face image[11]. The field of face recognition has advanced significantly with the development of deep learning, which has increased the efficiency and precision of face recognition compared to traditional techniques. The primary benefit of deep learning is its ability to train on enormous amounts of data, gradually adapt to various scenarios, and discover the optimal features to represent the data[12]. Convolutional neural networks (CNN) and their variant networks have excellent effects on image processing, laying the foundation for the application of deep learning in the recognition of pain expression images of elderly individuals with hip fractures[13].
To improve the efficiency and accuracy of pain assessment, we intend to construct a facial recognition model of pain expression for elderly patients with hip fractures through deep learning technology. To address the issues of determining network structure, excessive number of training runs, and excessive time for processing facial expression classification in convolutional neural networks, we will design a modified ResNet 50 algorithm based on Bayesian optimization, and use this algorithm to construct an automatic classification model of pain expressions in elderly patients with hip fractures.
Technical Route
The first phase proposes an automatic image classification model for pain expression based on the ResNet50 network. This approach addresses the shortcomings of traditional machine learning in facial expression image recognition, the high complexity of manual feature extraction, and the limitations of conventional deep learning, such as network degradation with increasing network depth. The model flow chart is depicted in Fig. 1. Face detection is achieved by video using Multi-Task Cascaded Convolutional Neural Networks (MTCNN). Detected pre-processed images are then inputted into the ResNet50 network, which is trained and optimized for pain level classification using Bayesian optimization. Once the requirements are met, voice playback and data recording functions are implemented. The Pain Expression Evaluation Assistant (PEEA) is an automated software system that analyzes facial pain expressions for the detection and classification of features relevant to pain assessment.
Multi-Task Cascaded Convolutional Neural Networks (MTCNN)
In engineering practice, the MTCNN algorithm is renowned for its high detection speed and accuracy. MTCNN employs a three-layer cascade architecture to accomplish face detection and key point localization in images[14]. MTCNN comprises three networks: P-Net, R-Net, and O-Net.
The three stages of MTCNN can be simply explained as follows: The first network layer, P-Net, scales the image at multiple levels and performs sliding detection on each image using a 12×12 sliding window with a step size of 2 for each scale. Small pictures can detect large faces, while large pictures can detect small faces. All detected face frames undergo NMS (Non-Maximum Suppression) to obtain the face frames, which are then converted to the original size, and the short side is filled to convert them into squares of 24×24.The second network layer, R-Net, processes multiple 24×24 faces from the previous step using the CNN network to obtain more precise face frames, which then undergo NMS and are ultimately resized to a 48×48 square. The third network layer, O-Net, takes multiple 48×48 faces from the previous step as input to obtain multiple more accurate boxes, five face position points, and confidence scores. NMS is then applied to the face frames to obtain the final required face frame.
ResNet 50 Convolutional Neural Network
A transfer learning approach utilizing ResNet50 is employed in this study. The method involves pre-training a convolutional neural network (CNN) on a large existing dataset, and then transferring the pre-trained CNN to a target dataset for fine-tuning. The proposed model is initially pre-trained on the ImageNet dataset at the TB level[15]. All layers, except the Softmax layer, are initialized with the pre-trained model parameters, as opposed to traditional random initialization[16]. The Softmax layer is added to process the dataset used in this study. This transfer learning method offers several advantages, including superior model generalization performance, significant depth, high accuracy, and good convergence[17].
Bayesian optimization
Bayesian optimization is an algorithm that uses Bayesian probability to search for the optimal value of an objective function, with the probabilistic surrogate model and acquisition function being the key components [18]. The most widely used probabilistic surrogate model is the Gaussian process, and the acquisition function is based on the posterior probability of the objective function. The goal of the Bayesian optimization algorithm is to minimize the total loss r, and this is achieved by selecting the evaluation point xi using the acquisition function, which is formulated as follows:
where X is the decision space, λ(x,D1:i) is the acquisition function, and y* is the optimal solution [19].
The implementation of Bayesian optimization followed the procedure outlined below [20]:
Determining the maximum number of iterations N.
Using the acquisition function to obtain the evaluation point xi.
Evaluating the acquisition function yi at the evaluation point yi.
Updating the probabilistic agent model by integrating the data Dt.
If the previous number of iterations n is less than the maximum number of iterations N, output xi; otherwise, return to step
(2) and continue iterating, else output xi.
Experiment
Database Construction
In order to construct the pain expression database, this study followed the existing methods and schemes. Firstly, the researchers recorded videos of elderly patients expressing different degrees of pain. Next, key frames capturing the required expressions were extracted from the videos. Experienced orthopedic specialist nurses and doctors evaluated and classified these key frames. Finally, consistent images were selected and included in the database.
Video collection
Patients with hip fractures attending the inpatient facility of a hospital’s hip joint trauma department participated in the video collection. The Hospital Research Ethics Committee approved the study protocol, and patients provided informed consent before participation. The study aim, subjects’ rights, and investigators’ obligations were explained to the respondents using uniform language. The privacy of the participants was ensured throughout the study.
For image or video detection, a clinical nurse used a camera to capture the patient's facial expressions. The camera was positioned 1-1.5 meters away from the patient's face, and the video acquisition time was 20–25 seconds. During this period, the patient did not need to hide their state.
The inclusion criteria for the study were: (1) radiological diagnostic criteria[21] of hip fracture in the elderly; (2) consciousness and understanding; (3) age > 65; (4) written informed consent.
The exclusion criteria were: (1) neurological diseases, such as Alzheimer's disease; (2) deafness, aphasia, and other symptoms that hindered communication.
Data Acquisition
The method involved sequentially recording videos of elderly individuals expressing different degrees of pain. The required key expression frames were extracted from these videos, and experienced nurses and doctors evaluated and classified them. The acquired images were labeled according to the criteria of mild, obvious, severe, and intense pain. To ensure the reliability of the database, images with high consistency in multiple evaluators' scores were mainly selected and included in the database, which summarized in the following aspects: turning over and patting the back, moving from flat car to bed,lower limb flexion and extension training ,straight leg raising training ,bedside standing and walking training.
Key Frame Capture
To focus on facial expression recognition algorithm research and minimize the influence of other factors, the original images were normalized. This involved three steps: rotation correction, image cropping, and scale normalization. The goal was to correct for background interference and angle offsets caused by the shooting environment and changes in face pose. This ensured that the face of the patient in the image was upright with both eyes in a horizontal state. The method removed redundant background information as much as possible, retaining only the effective facial region containing expressions. The center points of the two eyes were manually marked, and the image was rotated using the axis connecting the center points of the eyes as a reference. The center points of the eyes were then adjusted to the same horizontal line to eliminate angle deviation. Next, the patient's facial region was manually cropped from the corrected image. After evaluating and selecting from the video key frames, a database of facial pain expressions of elderly patients was established, consisting of 4,538 images, including 2,247 images of mild pain (VAS: 1–3), 735 images of obvious pain (VAS: 4–6), 729 images of severe pain (VAS: 7–8), and 827 images of intense pain (VAS: 9–10), in accordance with guidelines for pain management[1].
Data Enhancement
After randomizing the entire dataset, it was partitioned into three sets: the training set (60%), validation set (20%), and test set (20%). The training dataset underwent rotation, cropping, and translation to increase the number of training samples and improve the model's robustness and generalization performance.
In the field of computer vision, a model's performance depends not only on the data and the quality of its structure but also on the optimizer, loss function, and data enhancement methods. The effectiveness of a model's training strategy, such as the optimizer, data augmentation, and regularization technique, is highly important and can significantly impact its performance. This study utilized data enhancement techniques (Table 1) to improve the accuracy of model training.
|
Accuracy/% |
|
|---|---|
|
Before data enhancement |
67.62 |
|
After data enhancement |
73.99 |
BO-ResNet 50 network
Furthermore, Bayesian optimization was employed to determine the optimal hyperparameters, including the learning rate, with a maximum of 60 iterations, which was expected to enhance the acquisition function. Table 2 shows the decision space for the optimization process.
|
Hyperparameter |
Minimum value |
Maximum value |
|---|---|---|
|
Initial Learn Rate |
1×10− 2 |
1 |
|
Momentum |
0.8 |
0.98 |
|
L2 Regularization |
1×10− 10 |
1×10− 2 |
|
Optimizer |
RMSprop, Adam,SGDM |
|
Model evaluation criteria
In this study, the classification and generalization ability of the model for recognizing pain expressions in elderly patients with hip fracture were assessed based on the prediction accuracy and model training time [22].The formula used for the prediction accuracy P1 is
where nT is the number of test sets used to validate the model, and mTA is the number of accurately classified samples in the sample test set.
Pain Level Prediction
Input the normalized image into the trained BO-Resnet50 model, use the Softmax classifier to receive the feature matrix input by the fully connected layer, and output the probability value of each category corresponding to the input object, assuming that there are \(N\) input objects \({\left\{{X}_{\text{i}}{Y}_{\text{i}}\right\}}_{i-1}^{k}\), the label for each object is \({\text{y}}_{i}\{\text{1,2},\dots ,k\}\), \(k\) is the number of the model output categories (\(k\ge 2\))[22], 4-classification (1, 2, 3, 4) is performed for the pain expression, and \(k\) is 4. Input \({X}_{\text{i}}\), use the assumption function \({\text{f}}_{}\left({X}_{\text{i}}\right)\) to estimate the probability value of its corresponding class j: \(\text{P}\left({y=j/X}_{\text{i}}\right)\), and its function is:
The pain level of the pain expression is determined by taking the label category with the highest probability of the Softmax output. To avoid false detections and improve system stability, the detected label must reach a stable and continuous number of frames before it can be used as the output result of the pain level determination. The collected video data is organized into a folder and transmitted to a dedicated computer where the automatic pain expression classification system is accessed in video detection mode. The report is generated based on the pain expression with the longest duration in the video (Fig. 2,3).
Measure
The study involved 15 nurses, each with more than five years of clinical pain assessment experience, who underwent a training session. The session covered the structure of the Pain Evaluation and Entitlement Act (PEEA) report and included three videos to demonstrate the process. The trainer, who was familiar with the outputs of PEEA, provided guidance on where to find relevant information in the graphical outputs, but did not interpret any videos or images to allow the nurses to utilize their medical expertise.
During the session, the nurses were instructed to rate the pain level based solely on their visual inspection of the patients' condition. To ensure accurate assessment and avoid reader fatigue, the nurses were allowed to take an unlimited amount of time to complete the assessments at their convenience. The researchers then used the same videos to assess pain levels using the PEEA system.
Statistical Analysis
Agreement Rates
Agreement rates between nurses and PEEA were assessed by intraclass correlation (ICC),15 assuming random effects for the nurses. 95% confidence intervals were calculated according to the original derivations by Shrout and Fleiss [23].Standard errors of the mean for ICCs were estimated by resampling the observations with replacement (bootstrap) 1000 times. We selected the two-way of the same raters for all subjects in this study.
PEEA performance
Following Bayesian hyperparameter optimization, the learning rate, momentum, and L2 regularization were determined to be 0.1679, 0.8437, and 2.456×10− 5, respectively. The optimizer utilized the SGDM optimization algorithm, which updates model parameters using a momentum factor while selecting training data samples, resulting in superior training speed and prediction accuracy compared to the RMSProp and Adam optimizers (Fig. 4).
The trained model was tested in a clinical setting, and the results of the statistical data analysis are presented in Table 3. The experimental analysis of our algorithm demonstrated high accuracy in both the training and validation sets, with better performance than the ResNet 50 network for classification and recognition of pain expressions in elderly patients with hip fractures and higher training efficiency.
| Network model | Alexnet | ResNet 15 | ResNet 50 | Algorithm of present study |
|---|---|---|---|---|
| Training set accuracy /(%) | 77.4 | 85.6 | 98.4 | 99.6 |
| Validation set accuracy /(%) | 74.5 | 83.4 | 97.5 | 98.7 |
| Number of iterations | 50 | 50 | 50 | 30 |
| Total training time / (min) | 52 | 36 | 46 | 24 |
To assess the effectiveness of the MTCNN model for face detection in this study, we evaluated the detection precision and speed of the detection algorithm using 32 patients for face testing. As shown in Fig. 5, the detection precision was 0.97, and the detection speed was 0.023 sec/image when using a processor of Core (TM) i5-12400, indicating a high detection speed and precision for the proposed face detection algorithm. The model converged at 1063 iterations, and the final validation set results were stable at approximately 98.7%.
Agreement Rate
For the validation analysis, we selected 300 pain videos of elderly patients with hip fractures, with 28 excluded due to dim lighting, resulting in 272 videos for analysis. The pain scores between nurses and PEEA were compared, and the results demonstrated good reliability in pain assessment by PEEA, with an average intraclass correlation measure of 0.9257 (Table 4,5) and a substantial kappa coefficient of 0.683 (Table 6, Fig. 6).
| VAS | 1–3 | 4–6 | 7–8 | 9–10 |
|---|---|---|---|---|
| Nurse | 68 | 90 | 91 | 23 |
| PEEA | 69 | 94 | 88 | 21 |
| Intraclass correlation a | 95% Confidence Interval | |
|---|---|---|
| Single measures b | 0.8616 | 0.8276 to 0.8894 |
| Average measures c | 0.9257 | 0.9056 to 0.9414 |
| a The degree of consistency among measurements. | ||
| b Estimates the reliability of single ratings. | ||
| c Estimates the reliability of averages of k ratings. | ||
| Value | Asymptotic standard error a | Approximation Tb | P | ||
|---|---|---|---|---|---|
| Coherence measure | Kappa | 0.683 | 0.036 | 17.976 | 0.000 |
| Effective cases | N | 272 | |||
| a No assumption of zero | |||||
| b Assuming zero hypothesis using asymptotic standard error | |||||
The effects of data enhancement on model performance were evaluated by training the original and enhanced datasets under the same parameters and network structure. The results showed that the testing accuracy of the enhanced dataset was higher than that of the unexpanded original dataset, indicating that data enhancement can increase data diversity and prevent overfitting during training. The increased training data also led to more detailed feature types, resulting in more stable training accuracy and reduced fluctuations, which effectively improved the model's robustness.
The method for recognizing pain expressions in elderly patients with hip fractures involved acquiring a face image, using MTCNN for face detection and pre-processing, building and training a Resnet50 network model, and inputting images for pain level prediction. By integrating MTCNN and Resnet50 network and performing transfer learning on a self-built dataset, the proposed model achieved accurate classification of pain expressions in elderly patients with hip fractures.
In this study, we constructed a deep learning network model for recognizing and classifying pain expressions in elderly individuals with hip fractures. The residual module in the model effectively addressed the problem of network degradation, ensuring that performance did not decline with deepened network depth. To tackle the problem of small publicly labeled image datasets on pain expression, transfer learning was used to avoid over-fitting.
The results showed that PEEA had high accuracy and efficiency, indicating that they tend to decrease nondeterminacy. This is important because nondeterminacy can lead to unnecessary interventions or examinations, costing time, money, and causing discomfort and anxiety for patients. The technology could allow nurses to recognize pain levels more quickly. However, because our study only included 15 nurses, we used the type of ICC that considers the nurse as a random effect to quantify agreement rates. This allows us to generalize the results to a broader population.
Artificial intelligence has the potential to revolutionize nursing. Our study highlighted that these software systems are meant to support and enhance nurses' performance in clinical practice rather than replace them. Automated assessment systems, such as PEEA, could be used to quickly assess and grade a large number of pain videos or images, and to improve the reliability of measurements by decreasing interobserver variability.
The limitations of this study are that (1) the results may lack effective persuasion because the number of samples is limited, especially in model validation; (2) the evidence of criterion-related validity was limited and sensitivity has not been measured. Although the positioning accuracy of model must be sufficient, but this model has targeted clearly, so the number of samples can be reduced.
The conventional convolutional neural network (CNN) used for recognizing and classifying pain expression in elderly patients with hip fractures suffers from long training times and poor accuracy. In this study, we improved the hyperparameters of the ResNet 50 network using Bayesian optimization and used this algorithm to construct an automatic pain expression recognition and classification model for elderly patients with hip fractures. Our comparative experimental analysis led to the following conclusions: The automatic pain expression recognition model for elderly patients with hip fractures constructed based on the algorithm described in this study does not require manual extraction of facial features, reducing reliance on experience and expertise. The automatic pain expression recognition model for elderly patients with hip fractures constructed based on the algorithm described in this study has higher accuracy, requires fewer training runs, and has shorter training time.
In conclusion, our study suggests that computer-assisted detection systems, such as PEEA, can improve both the speed and efficiency of pain level assessments in elderly patients.
Ethics Approval and Consent from Participants
The present study adheres to ethical standards set forth by the Institutional Ethical Review Board of Tianjin Hospital (TJYY-2021-YLS-167), and the Ethical Guidelines for Epidemiological Research by the Chinese Government, as well as the 1964 Helsinki declaration and its subsequent revisions. Written informed consent was obtained from all study participants who completed and submitted the survey.
Funding disclosure
There is no funding to be disclosed.
Conflict of interest
The authors have no conflicts of interest to be declared.
Data Availability
The raw/processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.The project has signed an informed consent form with the research object, and the patient's privacy shall not be disclosed.
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No competing interests reported.