Ignite Creation Date:
2024-10-26 @ 3:39 PM
Last Modification Date:
2024-10-26 @ 3:39 PM
Study NCT ID:
NCT06589583
Status:
COMPLETED
Last Update Posted:
None
First Post:
2024-09-02
Brief Title:
Artificial Intelligence and Scoliosis
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Rrol of Deep Learning Algorithm in Assessment and Management of Scoliosis
Status:
COMPLETED
Status Verified Date:
2024-09
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
The aim of the study to use artificial intelligence technology in assessment of scoliosis degree of severity and to personalize for treatment plan for each pa tient
Detailed Description:
Although attention in AI related to healthcare is expanding there hast been much progress in translating or implementing these technologies for clinical usage Therefore as we conduct our research we will open a new field of study for the integration of artificial intelligence AI in medical assessment tools and physical therapy field This will save physiotherapists time and effort for developing Proper evaluations and conduction of treatment plans benefit patients and the country overall by lowering the financial burden associated with making accurate evaluations and management for these cases and provide clear objective evaluations for the majority of spinal scoliosis deformities as well as appropriate personalization for treatment plans
Martial and Methods
1 Experiment martials This section presents the findings of the research detailing the methodologies employed and the outcomes obtained The analysis begins with an overview of the dataset used followed by the preprocessing steps undertaken to ensure data quality and suitability for model training then delve into the various approaches and models selected for this study including both custom Convolutional Neural Networks CNNs and pre-trained models utilized through transfer learning Each model is assessed based on its performance and discuss the experimental setup and evaluation metrics used to measure effectiveness Finally presenting the experimental results providing a comprehensive analysis of model accuracy precision recall and other relevant metrics
21 Dataset collection The data of the subjects were divided for 2 groups Group A normal spine total X-ray consisted of 664 image and group B scoliotic patients x-ray consisted of 4307 images All spinal X-ray involved in this study were retrospectively compiled from manifold sources including BUU Datasets Kaggle Mendeley Data Huggingface Dropbox and Roboflow ensuring a diverse and comprehensive collection of scoliosis-related images Patient with scoliosis were 1 diagnosed with scoliosis for different etiology 2 Clear X- Ray for spine for all spinal curvatures including cervical thoracic and lumber 3 C shaped and double C shape scoliosis 4 mild moderate and sever scoliotic degrees 5 Adolescents with mean age 176 Both gendermale and female
2 Purposed methodology 21 Data Augmentation To address the issue of limited data we employed data augmentation to artificially expand our dataset This technique involves generating new training samples by applying various transformations to the existing images such as flipping scaling and rotating These augmentations not only increase the dataset size but also play a crucial role in enhancing the model ability to generalize by exposing it to a wider variety of image variations By simulating different viewing conditions and distortions data augmentation helps the model become more robust to changes in image orientation scale and other variations that it may encounter in real-world scenarios This process is particularly important in medical image analysis where acquiring large diverse datasets can be challenging Data augmentation ensures that the model does not overfit to the limited original dataset and instead learns to recognize the underlying patterns that are indicative of scoliosis ultimately improving its performance and reliability
Original images Flipped image Rotated image Scaled image
Examples of Data Augmentation Techniques Applied Images 22 Preprocessing Effective preprocessing is a critical step in preparing our dataset for model training as it enhances the quality and consistency of the images making them more suitable for analysis One of the key techniques was employed histogram equalization which is particularly important in medical imaging where subtle variations in pixel intensity can significantly impact the accuracy of classification Histogram equalization improves the contrast of images by redistributing their intensity values ensuring that the histogram-or distribution of pixel intensities-is more evenly spread out This process enhances important features in the images making them more informative and easier for the model to interpret
23 Approaches and Model selection This study investigating various machine learning and deep learning models to tackle the complex task of classifying Normal and Scoliosis images The research approach was meticulously designed to strike an optimal balance between achieving high classification accuracy and maintaining computational efficiency which is crucial for real-world medical applications A careful selection for range of models each offering unique strengths in handling image data and configured them to maximize their performance on the inserted dataset The following paragraphs provide a comprehensive overview of the models employed the rationale behind their selection their specific configurations and a detailed analysis of the results obtained from the experiments This thorough examination not only highlights the effectiveness of the chosen models but also offers insights into their potential applicability in clinical settings
231 Convolutional Neural Networks CNNs utilized Convolutional Neural Networks CNNs for exceptional capability in image classification tasks CNNs excel at capturing spatial hierarchies and features within images through a combination of convolutional layers pooling layers and dense layers Convolutional Layers These layers are fundamental to CNNs as they apply various filters to the input images to detect patterns such as edges textures and shapes By sliding these filters across the image convolutional layers generate feature maps that represent different aspects of the image We experimented with various filter sizes and numbers of filters to optimize the feature extraction process
Pooling Layers Pooling layers typically using operations such as max pooling or average pooling down-sample the feature maps produced by convolutional layers This reduces the spatial dimensions of the feature maps thereby decreasing the computational load and helping the model generalize better by focusing on the most salient features We employed pooling layers to effectively manage the dimensionality and improve the model efficiency
Dense Layers After the convolutional and pooling operations dense layers fully connected layers used to interpret the features extracted by the convolutional layers Dense layers aggregate the high-level features and perform the final classification by mapping the learned representations to the output labels We tuned the number of dense layers and their units to enhance the model ability to make accurate predictions
This combination of convolutional pooling and dense layers allowed us to build a robust CNN architecture that effectively distinguishes between Normal and Scoliosis images
232 Transfer Learning
Given the limited size of our dataset we leveraged transfer learning to enhance model performance without extensive computational resources Transfer learning involves using pre-trained models-developed on large diverse datasets-and adapting them to our specific task several advanced pre-trained models to benefit from their learned features
XceptionNet XceptionNet is an architecture based on depth-wise separable convolutions which decomposes convolution operations into smaller more efficient operations This design helps capture complex features while reducing computational complexity We fine-tuned XceptionNet to adapt its learned features for our scoliosis classification task
DenseNet201 DenseNet201 is characterized by its dense connectivity pattern where each layer receives input from all preceding layers This approach promotes feature reuse and improves gradient flow during training DenseNet201 utilized to leverage its strong feature extraction capabilities and enhance our model accuracy
EfficientNetB0 EfficientNetB0 employs a compound scaling method that uniformly scales the depth width and resolution of the network This model is known for its balance between accuracy and efficiency EfficientNetB0 adapted to the dataset to benefit from its optimized architecture and high performance
InceptionV3 InceptionV3 features a complex architecture with multiple parallel convolutional operations of varying filter sizes enabling it to capture a wide range of features InceptionV3 incorporated to take advantage of its robust feature extraction and multi-scale processing abilities
MobileNetV2 MobileNetV2 is designed for mobile and edge devices with efficiency and speed in mind It uses depth-wise separable convolutions to reduce model size and computational requirements MobileNetV2 used to achieve a balance between model efficiency and classification accuracy
By fine-tuning these pre-trained models makes the classifiers able to leverage their advanced feature extraction capabilities and adapt them to selected specific task of classifying Normal and Scoliosis images resulting in improved performance and reduced training time
Martial and Methods
1 Experiment martials This section presents the findings of the research detailing the methodologies employed and the outcomes obtained The analysis begins with an overview of the dataset used followed by the preprocessing steps undertaken to ensure data quality and suitability for model training then delve into the various approaches and models selected for this study including both custom Convolutional Neural Networks CNNs and pre-trained models utilized through transfer learning Each model is assessed based on its performance and discuss the experimental setup and evaluation metrics used to measure effectiveness Finally presenting the experimental results providing a comprehensive analysis of model accuracy precision recall and other relevant metrics
21 Dataset collection The data of the subjects were divided for 2 groups Group A normal spine total X-ray consisted of 664 image and group B scoliotic patients x-ray consisted of 4307 images All spinal X-ray involved in this study were retrospectively compiled from manifold sources including BUU Datasets Kaggle Mendeley Data Huggingface Dropbox and Roboflow ensuring a diverse and comprehensive collection of scoliosis-related images Patient with scoliosis were 1 diagnosed with scoliosis for different etiology 2 Clear X- Ray for spine for all spinal curvatures including cervical thoracic and lumber 3 C shaped and double C shape scoliosis 4 mild moderate and sever scoliotic degrees 5 Adolescents with mean age 176 Both gendermale and female
2 Purposed methodology 21 Data Augmentation To address the issue of limited data we employed data augmentation to artificially expand our dataset This technique involves generating new training samples by applying various transformations to the existing images such as flipping scaling and rotating These augmentations not only increase the dataset size but also play a crucial role in enhancing the model ability to generalize by exposing it to a wider variety of image variations By simulating different viewing conditions and distortions data augmentation helps the model become more robust to changes in image orientation scale and other variations that it may encounter in real-world scenarios This process is particularly important in medical image analysis where acquiring large diverse datasets can be challenging Data augmentation ensures that the model does not overfit to the limited original dataset and instead learns to recognize the underlying patterns that are indicative of scoliosis ultimately improving its performance and reliability
Original images Flipped image Rotated image Scaled image
Examples of Data Augmentation Techniques Applied Images 22 Preprocessing Effective preprocessing is a critical step in preparing our dataset for model training as it enhances the quality and consistency of the images making them more suitable for analysis One of the key techniques was employed histogram equalization which is particularly important in medical imaging where subtle variations in pixel intensity can significantly impact the accuracy of classification Histogram equalization improves the contrast of images by redistributing their intensity values ensuring that the histogram-or distribution of pixel intensities-is more evenly spread out This process enhances important features in the images making them more informative and easier for the model to interpret
23 Approaches and Model selection This study investigating various machine learning and deep learning models to tackle the complex task of classifying Normal and Scoliosis images The research approach was meticulously designed to strike an optimal balance between achieving high classification accuracy and maintaining computational efficiency which is crucial for real-world medical applications A careful selection for range of models each offering unique strengths in handling image data and configured them to maximize their performance on the inserted dataset The following paragraphs provide a comprehensive overview of the models employed the rationale behind their selection their specific configurations and a detailed analysis of the results obtained from the experiments This thorough examination not only highlights the effectiveness of the chosen models but also offers insights into their potential applicability in clinical settings
231 Convolutional Neural Networks CNNs utilized Convolutional Neural Networks CNNs for exceptional capability in image classification tasks CNNs excel at capturing spatial hierarchies and features within images through a combination of convolutional layers pooling layers and dense layers Convolutional Layers These layers are fundamental to CNNs as they apply various filters to the input images to detect patterns such as edges textures and shapes By sliding these filters across the image convolutional layers generate feature maps that represent different aspects of the image We experimented with various filter sizes and numbers of filters to optimize the feature extraction process
Pooling Layers Pooling layers typically using operations such as max pooling or average pooling down-sample the feature maps produced by convolutional layers This reduces the spatial dimensions of the feature maps thereby decreasing the computational load and helping the model generalize better by focusing on the most salient features We employed pooling layers to effectively manage the dimensionality and improve the model efficiency
Dense Layers After the convolutional and pooling operations dense layers fully connected layers used to interpret the features extracted by the convolutional layers Dense layers aggregate the high-level features and perform the final classification by mapping the learned representations to the output labels We tuned the number of dense layers and their units to enhance the model ability to make accurate predictions
This combination of convolutional pooling and dense layers allowed us to build a robust CNN architecture that effectively distinguishes between Normal and Scoliosis images
232 Transfer Learning
Given the limited size of our dataset we leveraged transfer learning to enhance model performance without extensive computational resources Transfer learning involves using pre-trained models-developed on large diverse datasets-and adapting them to our specific task several advanced pre-trained models to benefit from their learned features
XceptionNet XceptionNet is an architecture based on depth-wise separable convolutions which decomposes convolution operations into smaller more efficient operations This design helps capture complex features while reducing computational complexity We fine-tuned XceptionNet to adapt its learned features for our scoliosis classification task
DenseNet201 DenseNet201 is characterized by its dense connectivity pattern where each layer receives input from all preceding layers This approach promotes feature reuse and improves gradient flow during training DenseNet201 utilized to leverage its strong feature extraction capabilities and enhance our model accuracy
EfficientNetB0 EfficientNetB0 employs a compound scaling method that uniformly scales the depth width and resolution of the network This model is known for its balance between accuracy and efficiency EfficientNetB0 adapted to the dataset to benefit from its optimized architecture and high performance
InceptionV3 InceptionV3 features a complex architecture with multiple parallel convolutional operations of varying filter sizes enabling it to capture a wide range of features InceptionV3 incorporated to take advantage of its robust feature extraction and multi-scale processing abilities
MobileNetV2 MobileNetV2 is designed for mobile and edge devices with efficiency and speed in mind It uses depth-wise separable convolutions to reduce model size and computational requirements MobileNetV2 used to achieve a balance between model efficiency and classification accuracy
By fine-tuning these pre-trained models makes the classifiers able to leverage their advanced feature extraction capabilities and adapt them to selected specific task of classifying Normal and Scoliosis images resulting in improved performance and reduced training time
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None