Pub Date : 2025-02-14DOI: 10.1016/j.artmed.2025.103077
Hwa-Ah-Ni Lee , Geun-Hyeong Kim , Seung Park , In Ah Choi , Hyun Woo Kwon , Hansol Moon , Jae Hyun Jung , Chulhan Kim
Arthritis is an inflammatory condition associated with joint damage, the incidence of which is increasing worldwide. In severe cases, arthritis can result in the restriction of joint movement, thereby affecting daily activities; as such, early and accurate diagnosis crucial to ensure effective treatment and management. Advances in imaging technologies used for arthritis diagnosis, particularly Single Photon Emission Computed Tomography/Computed Tomography (SPECT/CT), have enabled the quantitative measurement of joint inflammation using . To the best of our knowledge, this is the first study to apply deep learning to to predict the development of hand arthritis. We developed a transformer-based Finger-aware Artificial Neural Network (FANN) to predict arthritis in patients experiencing hand pain, including finger embedding, and to share unique finger-specific information between hands. Compared to conventional machine learning models, the FANN model demonstrated superior performance, achieving an area under the receiver operating characteristic curve of 0.85, accuracy of 0.79, precision of 0.87, recall of 0.79, and F1-score of 0.83. Furthermore, analysis using the SHapley Additive exPlanations (SHAP) algorithm revealed that the FANN predictions were most significantly influenced by the proximal interphalangeal joints of the right hand, in which arthritis is the most clinically prevalent. These findings indicate that the FANN significantly enhances arthritis prediction, representing a promising tool for clinical decision-making in arthritis diagnosis.
{"title":"Finger-aware Artificial Neural Network for predicting arthritis in Patients with hand pain","authors":"Hwa-Ah-Ni Lee , Geun-Hyeong Kim , Seung Park , In Ah Choi , Hyun Woo Kwon , Hansol Moon , Jae Hyun Jung , Chulhan Kim","doi":"10.1016/j.artmed.2025.103077","DOIUrl":"10.1016/j.artmed.2025.103077","url":null,"abstract":"<div><div>Arthritis is an inflammatory condition associated with joint damage, the incidence of which is increasing worldwide. In severe cases, arthritis can result in the restriction of joint movement, thereby affecting daily activities; as such, early and accurate diagnosis crucial to ensure effective treatment and management. Advances in imaging technologies used for arthritis diagnosis, particularly Single Photon Emission Computed Tomography/Computed Tomography (SPECT/CT), have enabled the quantitative measurement of joint inflammation using <span><math><msub><mrow><mtext>SUV</mtext></mrow><mrow><mtext>max</mtext></mrow></msub></math></span>. To the best of our knowledge, this is the first study to apply deep learning to <span><math><msub><mrow><mtext>SUV</mtext></mrow><mrow><mtext>max</mtext></mrow></msub></math></span> to predict the development of hand arthritis. We developed a transformer-based Finger-aware Artificial Neural Network (FANN) to predict arthritis in patients experiencing hand pain, including finger embedding, and to share unique finger-specific information between hands. Compared to conventional machine learning models, the FANN model demonstrated superior performance, achieving an area under the receiver operating characteristic curve of 0.85, accuracy of 0.79, precision of 0.87, recall of 0.79, and F1-score of 0.83. Furthermore, analysis using the SHapley Additive exPlanations (SHAP) algorithm revealed that the FANN predictions were most significantly influenced by the proximal interphalangeal joints of the right hand, in which arthritis is the most clinically prevalent. These findings indicate that the FANN significantly enhances arthritis prediction, representing a promising tool for clinical decision-making in arthritis diagnosis.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"162 ","pages":"Article 103077"},"PeriodicalIF":6.1,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Understanding and extracting valuable information from electronic health records (EHRs) is important for improving healthcare delivery and health outcomes. Large language models (LLMs) have demonstrated significant proficiency in natural language understanding and processing, offering promises for automating the typically labor-intensive and time-consuming analytical tasks with EHRs. Despite the active application of LLMs in the healthcare setting, many foundation models lack real-world healthcare relevance. Applying LLMs to EHRs is still in its early stage. To advance this field, in this study, we pioneer a generation-augmented prompting paradigm “GAPrompt” to empower generic LLMs for automated clinical assessment, in particular, quantitative stroke severity assessment, using data extracted from EHRs.
Methods:
The GAPrompt paradigm comprises five components: (i) prompt-driven selection of LLMs, (ii) generation-augmented construction of a knowledge base, (iii) summary-based generation-augmented retrieval (SGAR); (iv) inferencing with a hierarchical chain-of-thought (HCoT), and (v) ensembling of multiple generations.
Results:
GAPrompt addresses the limitations of generic LLMs in clinical applications in a progressive manner. It efficiently evaluates the applicability of LLMs in specific tasks through LLM selection prompting, enhances their understanding of task-specific knowledge from the constructed knowledge base, improves the accuracy of knowledge and demonstration retrieval via SGAR, elevates LLM inference precision through HCoT, enhances generation robustness, and reduces hallucinations of LLM via ensembling. Experiment results demonstrate the capability of our method to empower LLMs to automatically assess EHRs and generate quantitative clinical assessment results.
Conclusion:
Our study highlights the applicability of enhancing the capabilities of foundation LLMs in medical domain-specific tasks, i.e., automated quantitative analysis of EHRs, addressing the challenges of labor-intensive and often manually conducted quantitative assessment of stroke in clinical practice and research. This approach offers a practical and accessible GAPrompt paradigm for researchers and industry practitioners seeking to leverage the power of LLMs in domain-specific applications. Its utility extends beyond the medical domain, applicable to a wide range of fields.
{"title":"Empowering large language models for automated clinical assessment with generation-augmented retrieval and hierarchical chain-of-thought","authors":"Zhanzhong Gu , Wenjing Jia , Massimo Piccardi , Ping Yu","doi":"10.1016/j.artmed.2025.103078","DOIUrl":"10.1016/j.artmed.2025.103078","url":null,"abstract":"<div><h3>Background:</h3><div>Understanding and extracting valuable information from electronic health records (EHRs) is important for improving healthcare delivery and health outcomes. Large language models (LLMs) have demonstrated significant proficiency in natural language understanding and processing, offering promises for automating the typically labor-intensive and time-consuming analytical tasks with EHRs. Despite the active application of LLMs in the healthcare setting, many foundation models lack real-world healthcare relevance. Applying LLMs to EHRs is still in its early stage. To advance this field, in this study, we pioneer a generation-augmented prompting paradigm “GAPrompt” to empower generic LLMs for automated clinical assessment, in particular, quantitative stroke severity assessment, using data extracted from EHRs.</div></div><div><h3>Methods:</h3><div>The GAPrompt paradigm comprises five components: (i) prompt-driven selection of LLMs, (ii) generation-augmented construction of a knowledge base, (iii) summary-based generation-augmented retrieval (SGAR); (iv) inferencing with a hierarchical chain-of-thought (HCoT), and (v) ensembling of multiple generations.</div></div><div><h3>Results:</h3><div>GAPrompt addresses the limitations of generic LLMs in clinical applications in a progressive manner. It efficiently evaluates the applicability of LLMs in specific tasks through LLM selection prompting, enhances their understanding of task-specific knowledge from the constructed knowledge base, improves the accuracy of knowledge and demonstration retrieval via SGAR, elevates LLM inference precision through HCoT, enhances generation robustness, and reduces hallucinations of LLM via ensembling. Experiment results demonstrate the capability of our method to empower LLMs to automatically assess EHRs and generate quantitative clinical assessment results.</div></div><div><h3>Conclusion:</h3><div>Our study highlights the applicability of enhancing the capabilities of foundation LLMs in medical domain-specific tasks, <em>i.e.</em>, automated quantitative analysis of EHRs, addressing the challenges of labor-intensive and often manually conducted quantitative assessment of stroke in clinical practice and research. This approach offers a practical and accessible GAPrompt paradigm for researchers and industry practitioners seeking to leverage the power of LLMs in domain-specific applications. Its utility extends beyond the medical domain, applicable to a wide range of fields.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"162 ","pages":"Article 103078"},"PeriodicalIF":6.1,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Antimicrobial stewardship programs (ASPs) are essential in optimizing the use of antibiotics to address the global concern of antimicrobial resistance (AMR). Artificial intelligence (AI) and machine learning (ML) have emerged as promising tools for enhancing ASPs efficiency by improving antibiotic prescription accuracy, resistance prediction, and dosage optimization. This systematic review evaluated the application of AI-driven ASPs, focusing on their methodologies, outcomes, and challenges. We searched all of the databases in PubMed, Scopus, Web of Science, and Embase using keywords related to “AI” and “antibiotic.” We only included studies that used AI and ML algorithms in ASPs, with the main criteria being empirical antibiotic selection, dose adjustment, and ASP adherence. There were no limits on time, setting, or language. Two authors independently screened studies for inclusion and assessed their risk of bias using the Newcastle Ottawa Scale (NOS) Assessment tool for observational studies. Implementation studies underscored AI's potential for improving antimicrobial stewardship programs. Two studies showed that logistic regression, boosted-tree models, and gradient-boosting machines could effectively describe the difference between patients who needed to change their antibiotic regimen and those who did not. Twenty-four studies have confirmed the role of machine learning in optimizing empirical antibiotic selection, predicting resistance, and enhancing therapy appropriateness, all of which have the potential to reduce mortality rates. Additionally, machine learning algorithms showed promise in optimizing antibiotic dosing, particularly for vancomycin. This systematic review aimed to highlight various AI models, their applications in ASPs, and the resulting impact on healthcare outcomes. Machine learning and AI models effectively enhance antibiotic stewardship by optimizing patient interventions, empirical antibiotic selection, resistance prediction, and dosing. However, it subtly draws attention to the differences between high-income countries (HICs) and low- and middle-income countries (LMICs), highlighting the structural difficulties that LMICs confront while simultaneously highlighting the progress made in HICs.
{"title":"Artificial intelligence-driven approaches in antibiotic stewardship programs and optimizing prescription practices: A systematic review","authors":"Hamid Harandi , Maryam Shafaati , Mohammadreza Salehi , Mohammad Mahdi Roozbahani , Keyhan Mohammadi , Samaneh Akbarpour , Ramin Rahimnia , Gholamreza Hassanpour , Yasin Rahmani , Arash Seifi","doi":"10.1016/j.artmed.2025.103089","DOIUrl":"10.1016/j.artmed.2025.103089","url":null,"abstract":"<div><div>Antimicrobial stewardship programs (ASPs) are essential in optimizing the use of antibiotics to address the global concern of antimicrobial resistance (AMR). Artificial intelligence (AI) and machine learning (ML) have emerged as promising tools for enhancing ASPs efficiency by improving antibiotic prescription accuracy, resistance prediction, and dosage optimization. This systematic review evaluated the application of AI-driven ASPs, focusing on their methodologies, outcomes, and challenges. We searched all of the databases in PubMed, Scopus, Web of Science, and Embase using keywords related to “AI” and “antibiotic.” We only included studies that used AI and ML algorithms in ASPs, with the main criteria being empirical antibiotic selection, dose adjustment, and ASP adherence. There were no limits on time, setting, or language. Two authors independently screened studies for inclusion and assessed their risk of bias using the Newcastle Ottawa Scale (NOS) Assessment tool for observational studies. Implementation studies underscored AI's potential for improving antimicrobial stewardship programs. Two studies showed that logistic regression, boosted-tree models, and gradient-boosting machines could effectively describe the difference between patients who needed to change their antibiotic regimen and those who did not. Twenty-four studies have confirmed the role of machine learning in optimizing empirical antibiotic selection, predicting resistance, and enhancing therapy appropriateness, all of which have the potential to reduce mortality rates. Additionally, machine learning algorithms showed promise in optimizing antibiotic dosing, particularly for vancomycin. This systematic review aimed to highlight various AI models, their applications in ASPs, and the resulting impact on healthcare outcomes. Machine learning and AI models effectively enhance antibiotic stewardship by optimizing patient interventions, empirical antibiotic selection, resistance prediction, and dosing. However, it subtly draws attention to the differences between high-income countries (HICs) and low- and middle-income countries (LMICs), highlighting the structural difficulties that LMICs confront while simultaneously highlighting the progress made in HICs.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"162 ","pages":"Article 103089"},"PeriodicalIF":6.1,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-07DOI: 10.1016/j.artmed.2025.103080
Arif Badrou , Arnaud Duval , Jérôme Szewczyk , Raphaël Blanc , Nicolas Tardif , Nahiène Hamila , Anthony Gravouil , Aline Bel-Brunon
Endovascular therapies enable minimally invasive treatment of vascular pathologies by guiding long tools towards the target area. However, certain pathways, such as the Supra-Aortic Trunks (SATs), present complex trajectories that make navigation challenging. To improve catheterization access to these challenging targets, an active guidewire composed of Shape Memory Alloy has been developed. Our study focuses on navigating this device and associated catheters to reach neurovascular targets via the left carotid artery. In previous work, a finite element model was used to simulate the navigation of the active guidewire and catheters from the aortic arch to the branching of the left carotid artery in patient-specific aortas. However, these numerical simulations are computationally intensive, limiting their feasibility for real-time navigation assistance. To address this, we present the development of numerical charts that enable real-time computation based on high-fidelity FE simulations. These charts predict: (1) the behavior of the active guidewire, and (2) the navigation of the guidewire and catheters within specific anatomical configurations, based on guidewire and navigation parameters. Using the High Order Proper Generalized Decomposition (HOPGD) method, these charts achieve accurate real-time predictions with errors below 5 % and a response time of seconds, based on a limited number of preliminary high-fidelity computations. These findings could significantly contribute to the development of clinically applicable methods to enhance endovascular procedures and the advance the broader field of neurovascular interventions.
{"title":"Development of decision support tools by model order reduction for active endovascular navigation","authors":"Arif Badrou , Arnaud Duval , Jérôme Szewczyk , Raphaël Blanc , Nicolas Tardif , Nahiène Hamila , Anthony Gravouil , Aline Bel-Brunon","doi":"10.1016/j.artmed.2025.103080","DOIUrl":"10.1016/j.artmed.2025.103080","url":null,"abstract":"<div><div>Endovascular therapies enable minimally invasive treatment of vascular pathologies by guiding long tools towards the target area. However, certain pathways, such as the Supra-Aortic Trunks (SATs), present complex trajectories that make navigation challenging. To improve catheterization access to these challenging targets, an active guidewire composed of Shape Memory Alloy has been developed. Our study focuses on navigating this device and associated catheters to reach neurovascular targets via the left carotid artery. In previous work, a finite element model was used to simulate the navigation of the active guidewire and catheters from the aortic arch to the branching of the left carotid artery in patient-specific aortas. However, these numerical simulations are computationally intensive, limiting their feasibility for real-time navigation assistance. To address this, we present the development of numerical charts that enable real-time computation based on high-fidelity FE simulations. These charts predict: (1) the behavior of the active guidewire, and (2) the navigation of the guidewire and catheters within specific anatomical configurations, based on guidewire and navigation parameters. Using the High Order Proper Generalized Decomposition (HOPGD) method, these charts achieve accurate real-time predictions with errors below 5 % and a response time of <span><math><msup><mn>10</mn><mrow><mo>−</mo><mn>3</mn></mrow></msup></math></span> seconds, based on a limited number of preliminary high-fidelity computations. These findings could significantly contribute to the development of clinically applicable methods to enhance endovascular procedures and the advance the broader field of neurovascular interventions.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"161 ","pages":"Article 103080"},"PeriodicalIF":6.1,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Modeling Optical Coherence Tomography (OCT) images is crucial for numerous image processing applications and aids ophthalmologists in the early detection of macular abnormalities. Sparse representation-based models, particularly dictionary learning (DL), play a pivotal role in image modeling. Traditional DL methods often transform higher-order tensors into vectors and then aggregate them into a matrix, which overlooks the inherent multi-dimensional structure of the data. To address this limitation, tensor-based DL approaches have been introduced. In this study, we present a novel tensor-based DL algorithm, CircWaveDL, for OCT classification, where both the training data and the dictionary are modeled as higher-order tensors. We named our approach CircWaveDL to reflect the use of CircWave atoms for dictionary initialization, rather than random initialization. CircWave has previously shown effectiveness in OCT classification, making it a fitting basis function for our DL method. The algorithm employs CANDECOMP/PARAFAC (CP) decomposition to factorize each tensor into lower dimensions. We then learn a sub-dictionary for each class using its respective training tensor. For testing, a test tensor is reconstructed with each sub-dictionary, and each test B-scan is assigned to the class that yields the minimal residual error. To evaluate the model's generalizability, we tested it across three distinct databases. Additionally, we introduce a new heatmap generation technique based on averaging the most significant atoms of the learned sub-dictionaries. This approach highlights that selecting an appropriate sub-dictionary for reconstructing test B-scans improves reconstructions, emphasizing the distinctive features of different classes. CircWaveDL demonstrated strong generalizability across external validation datasets, outperforming previous classification methods. It achieved accuracies of 92.5 %, 86.1 %, and 89.3 % on datasets 1, 2, and 3, respectively, showcasing its efficacy in OCT image classification.
{"title":"CircWaveDL: Modeling of optical coherence tomography images based on a new supervised tensor-based dictionary learning for classification of macular abnormalities","authors":"Roya Arian , Alireza Vard , Rahele Kafieh , Gerlind Plonka , Hossein Rabbani","doi":"10.1016/j.artmed.2024.103060","DOIUrl":"10.1016/j.artmed.2024.103060","url":null,"abstract":"<div><div>Modeling Optical Coherence Tomography (OCT) images is crucial for numerous image processing applications and aids ophthalmologists in the early detection of macular abnormalities. Sparse representation-based models, particularly dictionary learning (DL), play a pivotal role in image modeling. Traditional DL methods often transform higher-order tensors into vectors and then aggregate them into a matrix, which overlooks the inherent multi-dimensional structure of the data. To address this limitation, tensor-based DL approaches have been introduced. In this study, we present a novel tensor-based DL algorithm, CircWaveDL, for OCT classification, where both the training data and the dictionary are modeled as higher-order tensors. We named our approach CircWaveDL to reflect the use of CircWave atoms for dictionary initialization, rather than random initialization. CircWave has previously shown effectiveness in OCT classification, making it a fitting basis function for our DL method. The algorithm employs CANDECOMP/PARAFAC (CP) decomposition to factorize each tensor into lower dimensions. We then learn a sub-dictionary for each class using its respective training tensor. For testing, a test tensor is reconstructed with each sub-dictionary, and each test B-scan is assigned to the class that yields the minimal residual error. To evaluate the model's generalizability, we tested it across three distinct databases. Additionally, we introduce a new heatmap generation technique based on averaging the most significant atoms of the learned sub-dictionaries. This approach highlights that selecting an appropriate sub-dictionary for reconstructing test B-scans improves reconstructions, emphasizing the distinctive features of different classes. CircWaveDL demonstrated strong generalizability across external validation datasets, outperforming previous classification methods. It achieved accuracies of 92.5 %, 86.1 %, and 89.3 % on datasets 1, 2, and 3, respectively, showcasing its efficacy in OCT image classification.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"160 ","pages":"Article 103060"},"PeriodicalIF":6.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142973365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A single Raman spectrum reflects limited molecular information. Effective fusion of the Raman spectra of serum and urine source domains helps to obtain richer feature information. However, most of the current studies on immunoglobulin A nephropathy (IgAN) based on Raman spectroscopy are based on small sample data and low signal-to-noise ratio. If a multi-source data fusion strategy is directly adopted, it may even reduce the accuracy of disease diagnosis. To this end, this paper proposes a data enhancement and spectral optimization method based on variational autoencoders to obtain reconstructed Raman spectra with doubled sample size and improved signal-to-noise ratio. In the diagnosis of IgAN in multi-source domain Raman spectra, this paper builds a global and local feature decoupled variational autoencoder (DMSGL-VAE) model based on multi-source data. First, the statistical features after spectral segmentation are extracted, and the latent variables obtained by the variational encoder are decoupled through the decoupling module. The global representation and local representation obtained represent the global shared information and local unique information of the serum and urine source domains, respectively. Then, the cross-source reconstruction loss and decoupling loss are used to constrain the decoupling, and the effectiveness of the decoupling is proved quantitatively and qualitatively. Finally, the features of different source domains were integrated to diagnose IgAN, and the results were analyzed for important features using the SHapley Additive exPlanations algorithm. The experimental results showed that the AUC value of the DMSGL-VAE model for diagnosing IgAN on the test set was as high as 0.9958. The SHAP algorithm was used to further prove that proteins, hydroxybutyrate, and guanine are likely to be common biological fingerprint substances for the diagnosis of IgAN by serum and urine Raman spectroscopy. In summary, the DMSGL-VAE model designed based on Raman spectroscopy in this paper can achieve rapid, non-invasive, and accurate screening of IgAN in terms of classification performance. And interpretable analysis may help doctors further understand IgAN and make more efficient diagnostic measures in the future.
{"title":"Disentangled global and local features of multi-source data variational autoencoder: An interpretable model for diagnosing IgAN via multi-source Raman spectral fusion techniques","authors":"Wei Shuai , Xuecong Tian , Enguang Zuo , Xueqin Zhang , Chen Lu , Jin Gu , Chen Chen , Xiaoyi Lv , Cheng Chen","doi":"10.1016/j.artmed.2024.103053","DOIUrl":"10.1016/j.artmed.2024.103053","url":null,"abstract":"<div><div>A single Raman spectrum reflects limited molecular information. Effective fusion of the Raman spectra of serum and urine source domains helps to obtain richer feature information. However, most of the current studies on immunoglobulin A nephropathy (IgAN) based on Raman spectroscopy are based on small sample data and low signal-to-noise ratio. If a multi-source data fusion strategy is directly adopted, it may even reduce the accuracy of disease diagnosis. To this end, this paper proposes a data enhancement and spectral optimization method based on variational autoencoders to obtain reconstructed Raman spectra with doubled sample size and improved signal-to-noise ratio. In the diagnosis of IgAN in multi-source domain Raman spectra, this paper builds a global and local feature decoupled variational autoencoder (DMSGL-VAE) model based on multi-source data. First, the statistical features after spectral segmentation are extracted, and the latent variables obtained by the variational encoder are decoupled through the decoupling module. The global representation and local representation obtained represent the global shared information and local unique information of the serum and urine source domains, respectively. Then, the cross-source reconstruction loss and decoupling loss are used to constrain the decoupling, and the effectiveness of the decoupling is proved quantitatively and qualitatively. Finally, the features of different source domains were integrated to diagnose IgAN, and the results were analyzed for important features using the SHapley Additive exPlanations algorithm. The experimental results showed that the AUC value of the DMSGL-VAE model for diagnosing IgAN on the test set was as high as 0.9958. The SHAP algorithm was used to further prove that proteins, hydroxybutyrate, and guanine are likely to be common biological fingerprint substances for the diagnosis of IgAN by serum and urine Raman spectroscopy. In summary, the DMSGL-VAE model designed based on Raman spectroscopy in this paper can achieve rapid, non-invasive, and accurate screening of IgAN in terms of classification performance. And interpretable analysis may help doctors further understand IgAN and make more efficient diagnostic measures in the future.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"160 ","pages":"Article 103053"},"PeriodicalIF":6.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142866489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.artmed.2025.103076
Z. Movahedi Nia , L. Seyyed-Kalantari , M. Goitom , B. Mellado , A. Ahmadi , A. Asgary , J. Orbinski , J. Wu , J.D. Kong
Background
Controlling re-emerging outbreaks such as COVID-19 is a critical concern to global health. Disease forecasting solutions are extremely beneficial to public health emergency management. This work aims to design and deploy a framework for real-time surveillance, prediction, forecasting, and early warning of respiratory disease. To this end, we selected southern African countries and Canadian provinces, along with COVID-19 and influenza as our case studies.
Methodology
Six different datasets were collected for different provinces of Canada: number of influenza cases, number of COVID-19 cases, Google Trends, Reddit posts, satellite air quality data, and weather data. Moreover, five different data sources were collected for southern African countries whose COVID-19 number of cases were significantly correlated with each other: number of COVID-19 infections, Google Trends, Wiki Trends, Google News, and satellite air quality data. For each infectious disease, i.e. COVID-19 and Influenza for Canada and COVID-19 for southern African countries, data was processed, scaled, and fed into the deep learning model which included four layers, namely, a Convolutional Neural Network (CNN), a Graph Neural Network (GNN), a Gated Recurrent Unit (GRU), and a linear Neural Network (NN). Hyperparameters were optimized to provide an accurate 56-day-ahead prediction of the number of cases.
Result
The accuracy of our models in real-time surveillance, prediction, forecasting, and early warning of respiratory diseases are evaluated against state-of-the-art models, through Root Mean Square Error (RMSE), coefficient of determination (R2-score), and correlation coefficient. Our model improves R2-score, RMSE, and correlation by up to 55.98 %, 39.71 %, and 44.47 % for 56 days-ahead COVID-19 prediction in Ontario, 34.87 %, 25.52 %, 50.91 % for 8 weeks-ahead influenza prediction in Quebec, and 51.04 %, 32.04 %, and 28.74 % for 56 days-ahead COVID-19 prediction in South Africa, respectively.
Conclusion
This work presents a framework that automatically collects data from unconventional sources, and builds an early warning system for COVID-19 and influenza outbreaks. The result is extremely helpful to policy-makers and health officials for preparedness and rapid response against future outbreaks.
{"title":"Leveraging deep-learning and unconventional data for real-time surveillance, forecasting, and early warning of respiratory pathogens outbreak","authors":"Z. Movahedi Nia , L. Seyyed-Kalantari , M. Goitom , B. Mellado , A. Ahmadi , A. Asgary , J. Orbinski , J. Wu , J.D. Kong","doi":"10.1016/j.artmed.2025.103076","DOIUrl":"10.1016/j.artmed.2025.103076","url":null,"abstract":"<div><h3>Background</h3><div>Controlling re-emerging outbreaks such as COVID-19 is a critical concern to global health. Disease forecasting solutions are extremely beneficial to public health emergency management. This work aims to design and deploy a framework for real-time surveillance, prediction, forecasting, and early warning of respiratory disease. To this end, we selected southern African countries and Canadian provinces, along with COVID-19 and influenza as our case studies.</div></div><div><h3>Methodology</h3><div>Six different datasets were collected for different provinces of Canada: number of influenza cases, number of COVID-19 cases, Google Trends, Reddit posts, satellite air quality data, and weather data. Moreover, five different data sources were collected for southern African countries whose COVID-19 number of cases were significantly correlated with each other: number of COVID-19 infections, Google Trends, Wiki Trends, Google News, and satellite air quality data. For each infectious disease, i.e. COVID-19 and Influenza for Canada and COVID-19 for southern African countries, data was processed, scaled, and fed into the deep learning model which included four layers, namely, a Convolutional Neural Network (CNN), a Graph Neural Network (GNN), a Gated Recurrent Unit (GRU), and a linear Neural Network (NN). Hyperparameters were optimized to provide an accurate 56-day-ahead prediction of the number of cases.</div></div><div><h3>Result</h3><div>The accuracy of our models in real-time surveillance, prediction, forecasting, and early warning of respiratory diseases are evaluated against state-of-the-art models, through Root Mean Square Error (RMSE), coefficient of determination (R2-score), and correlation coefficient. Our model improves R2-score, RMSE, and correlation by up to 55.98 %, 39.71 %, and 44.47 % for 56 days-ahead COVID-19 prediction in Ontario, 34.87 %, 25.52 %, 50.91 % for 8 weeks-ahead influenza prediction in Quebec, and 51.04 %, 32.04 %, and 28.74 % for 56 days-ahead COVID-19 prediction in South Africa, respectively.</div></div><div><h3>Conclusion</h3><div>This work presents a framework that automatically collects data from unconventional sources, and builds an early warning system for COVID-19 and influenza outbreaks. The result is extremely helpful to policy-makers and health officials for preparedness and rapid response against future outbreaks.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"161 ","pages":"Article 103076"},"PeriodicalIF":6.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143167903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.artmed.2024.103054
Trong-Thang Pham , Jacob Brecheisen , Carol C. Wu , Hien Nguyen , Zhigang Deng , Donald Adjeroh , Gianfranco Doretto , Arabinda Choudhary , Ngan Le
Using Deep Learning in computer-aided diagnosis systems has been of great interest due to its impressive performance in the general domain and medical domain. However, a notable challenge is the lack of explainability of many advanced models, which poses risks in critical applications such as diagnosing findings in CXR. To address this problem, we propose ItpCtrl-AI, a novel end-to-end interpretable and controllable framework that mirrors the decision-making process of the radiologist. By emulating the eye gaze patterns of radiologists, our framework initially determines the focal areas and assesses the significance of each pixel within those regions. As a result, the model generates an attention heatmap representing radiologists’ attention, which is then used to extract attended visual information to diagnose the findings. By allowing the directional input, our framework is controllable by the user. Furthermore, by displaying the eye gaze heatmap which guides the diagnostic conclusion, the underlying rationale behind the model’s decision is revealed, thereby making it interpretable.
In addition to developing an interpretable and controllable framework, our work includes the creation of a dataset, named Diagnosed-Gaze++, which aligns medical findings with eye gaze data. Our extensive experimentation validates the effectiveness of our approach in generating accurate attention heatmaps and diagnoses. The experimental results show that our model not only accurately identifies medical findings but also precisely produces the eye gaze attention of radiologists. The dataset, models, and source code will be made publicly available upon acceptance.
{"title":"ItpCtrl-AI: End-to-end interpretable and controllable artificial intelligence by modeling radiologists’ intentions","authors":"Trong-Thang Pham , Jacob Brecheisen , Carol C. Wu , Hien Nguyen , Zhigang Deng , Donald Adjeroh , Gianfranco Doretto , Arabinda Choudhary , Ngan Le","doi":"10.1016/j.artmed.2024.103054","DOIUrl":"10.1016/j.artmed.2024.103054","url":null,"abstract":"<div><div>Using Deep Learning in computer-aided diagnosis systems has been of great interest due to its impressive performance in the general domain and medical domain. However, a notable challenge is the lack of explainability of many advanced models, which poses risks in critical applications such as diagnosing findings in CXR. To address this problem, we propose ItpCtrl-AI, a novel <em>end-to-end interpretable and controllable framework that mirrors the decision-making process of the radiologist</em>. By emulating the eye gaze patterns of radiologists, our framework initially determines the focal areas and assesses the significance of each pixel within those regions. As a result, the model generates an attention heatmap representing radiologists’ attention, which is then used to extract attended visual information to diagnose the findings. By allowing the directional input, our framework is controllable by the user. Furthermore, by displaying the eye gaze heatmap which guides the diagnostic conclusion, the underlying rationale behind the model’s decision is revealed, thereby making it interpretable.</div><div>In addition to developing an interpretable and controllable framework, our work includes the creation of a dataset, named Diagnosed-Gaze++, which aligns medical findings with eye gaze data. Our extensive experimentation validates the effectiveness of our approach in generating accurate attention heatmaps and diagnoses. The experimental results show that our model not only accurately identifies medical findings but also precisely produces the eye gaze attention of radiologists. The dataset, models, and source code will be made publicly available upon acceptance.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"160 ","pages":"Article 103054"},"PeriodicalIF":6.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142848409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.artmed.2024.103052
Anne-Kathrin Kleine , Eesha Kokje , Pia Hummelsberger , Eva Lermer , Insa Schaffernak , Susanne Gaube
The review seeks to promote transparency in the availability of regulated AI-enabled Clinical Decision Support Systems (AI-CDSS) for mental healthcare. From 84 potential products, seven fulfilled the inclusion criteria. The products can be categorized into three major areas: diagnosis of autism spectrum disorder (ASD) based on clinical history, behavioral, and eye-tracking data; diagnosis of multiple disorders based on conversational data; and medication selection based on clinical history and genetic data. We found five scientific articles evaluating the devices' performance and external validity. The average completeness of reporting, indicated by 52 % adherence to the Consolidated Standards of Reporting Trials Artificial Intelligence (CONSORT-AI) checklist, was modest, signaling room for improvement in reporting quality. Our findings stress the importance of obtaining regulatory approval, adhering to scientific standards, and staying up-to-date with the latest changes in the regulatory landscape. Refining regulatory guidelines and implementing effective tracking systems for AI-CDSS could enhance transparency and oversight in the field.
{"title":"AI-enabled clinical decision support tools for mental healthcare: A product review","authors":"Anne-Kathrin Kleine , Eesha Kokje , Pia Hummelsberger , Eva Lermer , Insa Schaffernak , Susanne Gaube","doi":"10.1016/j.artmed.2024.103052","DOIUrl":"10.1016/j.artmed.2024.103052","url":null,"abstract":"<div><div>The review seeks to promote transparency in the availability of regulated AI-enabled Clinical Decision Support Systems (AI-CDSS) for mental healthcare. From 84 potential products, seven fulfilled the inclusion criteria. The products can be categorized into three major areas: diagnosis of autism spectrum disorder (ASD) based on clinical history, behavioral, and eye-tracking data; diagnosis of multiple disorders based on conversational data; and medication selection based on clinical history and genetic data. We found five scientific articles evaluating the devices' performance and external validity. The average completeness of reporting, indicated by 52 % adherence to the Consolidated Standards of Reporting Trials Artificial Intelligence (CONSORT-AI) checklist, was modest, signaling room for improvement in reporting quality. Our findings stress the importance of obtaining regulatory approval, adhering to scientific standards, and staying up-to-date with the latest changes in the regulatory landscape. Refining regulatory guidelines and implementing effective tracking systems for AI-CDSS could enhance transparency and oversight in the field.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"160 ","pages":"Article 103052"},"PeriodicalIF":6.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142815086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.artmed.2024.103058
Ugo Lomoio , Pierangelo Veltri , Pietro Hiram Guzzi , Pietro Liò
Electrocardiogram signals play a pivotal role in cardiovascular diagnostics, providing essential information on electrical hearth activity. However, inherent noise and limited resolution can hinder an accurate interpretation of the recordings. In this paper an advanced Denoising Convolutional Autoencoder designed to process electrocardiogram signals, generating super-resolution reconstructions is proposed; this is followed by in-depth analysis of the enhanced signals. The autoencoder receives a signal window (of 5 s) sampled at 50 Hz (low resolution) as input and reconstructs a denoised super-resolution signal at 500 Hz. The proposed autoencoder is applied to publicly available datasets, demonstrating optimal performance in reconstructing high-resolution signals from very low-resolution inputs sampled at 50 Hz. The results were then compared with current state-of-the-art for electrocardiogram super-resolution, demonstrating the effectiveness of the proposed method. The method achieves a signal-to-noise ratio of 12.20 dB, a mean squared error of 0.0044, and a root mean squared error of 4.86%, which significantly outperforms current state-of-the-art alternatives. This framework can effectively enhance hidden information within signals, aiding in the detection of heart-related diseases.
{"title":"Design and use of a Denoising Convolutional Autoencoder for reconstructing electrocardiogram signals at super resolution","authors":"Ugo Lomoio , Pierangelo Veltri , Pietro Hiram Guzzi , Pietro Liò","doi":"10.1016/j.artmed.2024.103058","DOIUrl":"10.1016/j.artmed.2024.103058","url":null,"abstract":"<div><div>Electrocardiogram signals play a pivotal role in cardiovascular diagnostics, providing essential information on electrical hearth activity. However, inherent noise and limited resolution can hinder an accurate interpretation of the recordings. In this paper an advanced Denoising Convolutional Autoencoder designed to process electrocardiogram signals, generating super-resolution reconstructions is proposed; this is followed by in-depth analysis of the enhanced signals. The autoencoder receives a signal window (of 5 s) sampled at 50 Hz (low resolution) as input and reconstructs a denoised super-resolution signal at 500 Hz. The proposed autoencoder is applied to publicly available datasets, demonstrating optimal performance in reconstructing high-resolution signals from very low-resolution inputs sampled at 50 Hz. The results were then compared with current state-of-the-art for electrocardiogram super-resolution, demonstrating the effectiveness of the proposed method. The method achieves a signal-to-noise ratio of 12.20 dB, a mean squared error of 0.0044, and a root mean squared error of 4.86%, which significantly outperforms current state-of-the-art alternatives. This framework can effectively enhance hidden information within signals, aiding in the detection of heart-related diseases.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"160 ","pages":"Article 103058"},"PeriodicalIF":6.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142915779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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