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ASNET: A Novel AI Framework for Accurate Ankylosing Spondylitis Diagnosis from MRI

Background: Ankylosing spondylitis (AS) is a chronic, painful, progressive disease usually seen in the spine. Traditional diagnostic methods have limitations in detecting the early stages of AS. The early diagnosis of AS can improve patients’ quality of life. This study aims to diagnose AS with a pr...

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Autores principales: Tas, Nevsun Pihtili, Kaya, Oguz, Macin, Gulay, Tasci, Burak, Dogan, Sengul, Tuncer, Turker
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10525210/
https://www.ncbi.nlm.nih.gov/pubmed/37760882
http://dx.doi.org/10.3390/biomedicines11092441
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author Tas, Nevsun Pihtili
Kaya, Oguz
Macin, Gulay
Tasci, Burak
Dogan, Sengul
Tuncer, Turker
author_facet Tas, Nevsun Pihtili
Kaya, Oguz
Macin, Gulay
Tasci, Burak
Dogan, Sengul
Tuncer, Turker
author_sort Tas, Nevsun Pihtili
collection PubMed
description Background: Ankylosing spondylitis (AS) is a chronic, painful, progressive disease usually seen in the spine. Traditional diagnostic methods have limitations in detecting the early stages of AS. The early diagnosis of AS can improve patients’ quality of life. This study aims to diagnose AS with a pre-trained hybrid model using magnetic resonance imaging (MRI). Materials and Methods: In this research, we collected a new MRI dataset comprising three cases. Furthermore, we introduced a novel deep feature engineering model. Within this model, we utilized three renowned pretrained convolutional neural networks (CNNs): DenseNet201, ResNet50, and ShuffleNet. Through these pretrained CNNs, deep features were generated using the transfer learning approach. For each pretrained network, two feature vectors were generated from an MRI. Three feature selectors were employed during the feature selection phase, amplifying the number of features from 6 to 18 (calculated as 6 × 3). The k-nearest neighbors (kNN) classifier was utilized in the classification phase to determine classification results. During the information phase, the iterative majority voting (IMV) algorithm was applied to secure voted results, and our model selected the output with the highest classification accuracy. In this manner, we have introduced a self-organized deep feature engineering model. Results: We have applied the presented model to the collected dataset. The proposed method yielded 99.80%, 99.60%, 100%, and 99.80% results for accuracy, recall, precision, and F1-score for the collected axial images dataset. The collected coronal image dataset yielded 99.45%, 99.20%, 99.70%, and 99.45% results for accuracy, recall, precision, and F1-score, respectively. As for contrast-enhanced images, accuracy of 95.62%, recall of 80.72%, precision of 94.24%, and an F1-score of 86.96% were attained. Conclusions: Based on the results, the proposed method for classifying AS disease has demonstrated successful outcomes using MRI. The model has been tested on three cases, and its consistently high classification performance across all cases underscores the model’s general robustness. Furthermore, the ability to diagnose AS disease using only axial images, without the need for contrast-enhanced MRI, represents a significant advancement in both healthcare and economic terms.
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spelling pubmed-105252102023-09-28 ASNET: A Novel AI Framework for Accurate Ankylosing Spondylitis Diagnosis from MRI Tas, Nevsun Pihtili Kaya, Oguz Macin, Gulay Tasci, Burak Dogan, Sengul Tuncer, Turker Biomedicines Article Background: Ankylosing spondylitis (AS) is a chronic, painful, progressive disease usually seen in the spine. Traditional diagnostic methods have limitations in detecting the early stages of AS. The early diagnosis of AS can improve patients’ quality of life. This study aims to diagnose AS with a pre-trained hybrid model using magnetic resonance imaging (MRI). Materials and Methods: In this research, we collected a new MRI dataset comprising three cases. Furthermore, we introduced a novel deep feature engineering model. Within this model, we utilized three renowned pretrained convolutional neural networks (CNNs): DenseNet201, ResNet50, and ShuffleNet. Through these pretrained CNNs, deep features were generated using the transfer learning approach. For each pretrained network, two feature vectors were generated from an MRI. Three feature selectors were employed during the feature selection phase, amplifying the number of features from 6 to 18 (calculated as 6 × 3). The k-nearest neighbors (kNN) classifier was utilized in the classification phase to determine classification results. During the information phase, the iterative majority voting (IMV) algorithm was applied to secure voted results, and our model selected the output with the highest classification accuracy. In this manner, we have introduced a self-organized deep feature engineering model. Results: We have applied the presented model to the collected dataset. The proposed method yielded 99.80%, 99.60%, 100%, and 99.80% results for accuracy, recall, precision, and F1-score for the collected axial images dataset. The collected coronal image dataset yielded 99.45%, 99.20%, 99.70%, and 99.45% results for accuracy, recall, precision, and F1-score, respectively. As for contrast-enhanced images, accuracy of 95.62%, recall of 80.72%, precision of 94.24%, and an F1-score of 86.96% were attained. Conclusions: Based on the results, the proposed method for classifying AS disease has demonstrated successful outcomes using MRI. The model has been tested on three cases, and its consistently high classification performance across all cases underscores the model’s general robustness. Furthermore, the ability to diagnose AS disease using only axial images, without the need for contrast-enhanced MRI, represents a significant advancement in both healthcare and economic terms. MDPI 2023-09-01 /pmc/articles/PMC10525210/ /pubmed/37760882 http://dx.doi.org/10.3390/biomedicines11092441 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Tas, Nevsun Pihtili
Kaya, Oguz
Macin, Gulay
Tasci, Burak
Dogan, Sengul
Tuncer, Turker
ASNET: A Novel AI Framework for Accurate Ankylosing Spondylitis Diagnosis from MRI
title ASNET: A Novel AI Framework for Accurate Ankylosing Spondylitis Diagnosis from MRI
title_full ASNET: A Novel AI Framework for Accurate Ankylosing Spondylitis Diagnosis from MRI
title_fullStr ASNET: A Novel AI Framework for Accurate Ankylosing Spondylitis Diagnosis from MRI
title_full_unstemmed ASNET: A Novel AI Framework for Accurate Ankylosing Spondylitis Diagnosis from MRI
title_short ASNET: A Novel AI Framework for Accurate Ankylosing Spondylitis Diagnosis from MRI
title_sort asnet: a novel ai framework for accurate ankylosing spondylitis diagnosis from mri
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10525210/
https://www.ncbi.nlm.nih.gov/pubmed/37760882
http://dx.doi.org/10.3390/biomedicines11092441
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