Ebraham Alskaf, Cian M Scannell, Avan Suinesiaputra, Richard Crawley, PierGiorgio Masci, Alistair Young, Divaka Perera, Amedeo Chiribiri
Background: The prognostic value of late gadolinium enhancement (LGE) in cardiac magnetic resonance (CMR) imaging is well-established. However, the direct relationship between image pixels and outcomes remains poorly understood. We hypothesised that leveraging artificial intelligence (AI) to analyse qualitative LGE images based on American Heart Association (AHA) guidelines could elucidate this relationship.
Methods: We collected retrospective CMR cases from a stress perfusion database, selecting LGE images comprising three long-axis views and 10 short-axis views. Clinical CMR reports served for annotation. We trained a multi-label convolutional neural network (CNN) to predict each AHA segment. Additionally, we transformed LGE image pixels into features, combined them with clinical data features, and trained a hybrid neural network (HNN) to predict mortality and ventricular arrhythmia. The dataset was divided into training (70%), validation (15%), and test (15%) sets. Evaluation metrics included the area under the curve (AUC).
Results: The total number of cases included was 2,740, with 218 patients experiencing positive mortality events (8%). The total number of cases with at least one AHA segment positive for LGE was 823 (30%), among which 111 (13%) experienced mortality events, and 84 (10%) had ventricular arrhythmia events. When assessing all segments combined, the most common cases were those classified as normal studies, with each AHA segment having a score of 0 (1,661 cases, 60.6%). The multi-label classifier demonstrated fair performance (AUC: 64%), whereas the cluster classifier did not yield any predictions (AUC: 53%, P<0.001). The mortality HNN achieved a satisfactory performance with an AUC of 77%, as did the ventricular arrhythmia HNN with an AUC of 75%.
Conclusions: Our study demonstrates the feasibility of generating qualitative AHA LGE maps using AI. Furthermore, the prediction of mortality and ventricular arrhythmia using HNN represents a potent new approach for risk stratification in patients with known or suspected coronary artery disease (CAD).
{"title":"Qualitative American Heart Association plot of late gadolinium enhancement with mortality and ventricular arrhythmia prediction using artificial intelligence.","authors":"Ebraham Alskaf, Cian M Scannell, Avan Suinesiaputra, Richard Crawley, PierGiorgio Masci, Alistair Young, Divaka Perera, Amedeo Chiribiri","doi":"10.21037/jmai-24-94","DOIUrl":"10.21037/jmai-24-94","url":null,"abstract":"<p><strong>Background: </strong>The prognostic value of late gadolinium enhancement (LGE) in cardiac magnetic resonance (CMR) imaging is well-established. However, the direct relationship between image pixels and outcomes remains poorly understood. We hypothesised that leveraging artificial intelligence (AI) to analyse qualitative LGE images based on American Heart Association (AHA) guidelines could elucidate this relationship.</p><p><strong>Methods: </strong>We collected retrospective CMR cases from a stress perfusion database, selecting LGE images comprising three long-axis views and 10 short-axis views. Clinical CMR reports served for annotation. We trained a multi-label convolutional neural network (CNN) to predict each AHA segment. Additionally, we transformed LGE image pixels into features, combined them with clinical data features, and trained a hybrid neural network (HNN) to predict mortality and ventricular arrhythmia. The dataset was divided into training (70%), validation (15%), and test (15%) sets. Evaluation metrics included the area under the curve (AUC).</p><p><strong>Results: </strong>The total number of cases included was 2,740, with 218 patients experiencing positive mortality events (8%). The total number of cases with at least one AHA segment positive for LGE was 823 (30%), among which 111 (13%) experienced mortality events, and 84 (10%) had ventricular arrhythmia events. When assessing all segments combined, the most common cases were those classified as normal studies, with each AHA segment having a score of 0 (1,661 cases, 60.6%). The multi-label classifier demonstrated fair performance (AUC: 64%), whereas the cluster classifier did not yield any predictions (AUC: 53%, P<0.001). The mortality HNN achieved a satisfactory performance with an AUC of 77%, as did the ventricular arrhythmia HNN with an AUC of 75%.</p><p><strong>Conclusions: </strong>Our study demonstrates the feasibility of generating qualitative AHA LGE maps using AI. Furthermore, the prediction of mortality and ventricular arrhythmia using HNN represents a potent new approach for risk stratification in patients with known or suspected coronary artery disease (CAD).</p>","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":"8 ","pages":"2"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7617223/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142815170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Exploring the capabilities and limitations of large language models in nuclear medicine knowledge with primary focus on GPT-3.5, GPT-4 and Google Bard","authors":"Sira Vachatimanont, K. Kingpetch","doi":"10.21037/jmai-23-180","DOIUrl":"https://doi.org/10.21037/jmai-23-180","url":null,"abstract":"","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":"60 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140399918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E. Alskaf, R. Crawley, C. Scannell, Avan Suinesiaputra, Alistair Young, Pier-Giorgio Masci, D. Perera, A. Chiribiri
{"title":"Hybrid artificial intelligence outcome prediction using features extraction from stress perfusion cardiac magnetic resonance images and electronic health records","authors":"E. Alskaf, R. Crawley, C. Scannell, Avan Suinesiaputra, Alistair Young, Pier-Giorgio Masci, D. Perera, A. Chiribiri","doi":"10.21037/jmai-24-1","DOIUrl":"https://doi.org/10.21037/jmai-24-1","url":null,"abstract":"","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":"56 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140400144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Efficient glioma grade prediction using learned features extracted from convolutional neural networks","authors":"Shyam Yathirajam, Sreedevi Gutta","doi":"10.21037/jmai-23-161","DOIUrl":"https://doi.org/10.21037/jmai-23-161","url":null,"abstract":"","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":"127 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140404942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Devil’s advocate: exploring the potential negative impacts of artificial intelligence on the field of surgery","authors":"Mina Sarofim","doi":"10.21037/jmai-23-158","DOIUrl":"https://doi.org/10.21037/jmai-23-158","url":null,"abstract":"","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":"69 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140406436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Khan, Abubakar Siddique, Asim Mustafa Khan, Bhavya Shetty, Ibrahim Fazal
{"title":"Artificial intelligence in periodontology and implantology—a narrative review","authors":"S. Khan, Abubakar Siddique, Asim Mustafa Khan, Bhavya Shetty, Ibrahim Fazal","doi":"10.21037/jmai-23-186","DOIUrl":"https://doi.org/10.21037/jmai-23-186","url":null,"abstract":"","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":"66 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140399423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lucy Bennett, Mohamed Mostafa, R. Hammersley, H. Purssell, Manish Patel, Oliver Street, V. Athwal, Karen Piper Hanley, The ID-LIVER Consortium, Neil A. Hanley, J. Morling, Indra Neil Guha
{"title":"Using a machine learning model to risk stratify for the presence of significant liver disease in a primary care population","authors":"Lucy Bennett, Mohamed Mostafa, R. Hammersley, H. Purssell, Manish Patel, Oliver Street, V. Athwal, Karen Piper Hanley, The ID-LIVER Consortium, Neil A. Hanley, J. Morling, Indra Neil Guha","doi":"10.21037/jmai-23-35","DOIUrl":"https://doi.org/10.21037/jmai-23-35","url":null,"abstract":"","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139297638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Skin Cancer is on the rise and Melanoma is the most threatening type among the skin cancers. Early detection of skin cancer is vital in order to prevent the cancer to be spread to other parts. In this paper a transfer-learning based system is proposed for Melanoma lesions detection. In the proposed system first, the images are preprocessed for removing the noise and illumination effect. In the next step a convolutional neural network is trained based on transfer learning using the weights of ImageNet data set. In the third step the network is fine-tuned to become more specialized for detecting the Melanoma versus other types of benign cancers. The proposed system uses the information from the image in 3 stages. In each stage the focus will be more concentrate on the center on the image where the suspicious part is. The results from these parts are combined and applied to a fully connected neural network. Results shows the superiority of the proposed methods compare to other state-of-the arts methods.
{"title":"Skin cancer detection using multi-scale deep learning and transfer learning","authors":"Mohammadreza Hajiarbabi","doi":"10.21037/jmai-23-67","DOIUrl":"https://doi.org/10.21037/jmai-23-67","url":null,"abstract":"Skin Cancer is on the rise and Melanoma is the most threatening type among the skin cancers. Early detection of skin cancer is vital in order to prevent the cancer to be spread to other parts. In this paper a transfer-learning based system is proposed for Melanoma lesions detection. In the proposed system first, the images are preprocessed for removing the noise and illumination effect. In the next step a convolutional neural network is trained based on transfer learning using the weights of ImageNet data set. In the third step the network is fine-tuned to become more specialized for detecting the Melanoma versus other types of benign cancers. The proposed system uses the information from the image in 3 stages. In each stage the focus will be more concentrate on the center on the image where the suspicious part is. The results from these parts are combined and applied to a fully connected neural network. Results shows the superiority of the proposed methods compare to other state-of-the arts methods.","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":"100 3-4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135714331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alaa Aljiffry, Yanbo Xu, Shenda Hong, Justin B. Long, Jimeng Sun, Kevin O. Maher
{"title":"Artificial intelligence and clinical stability after the Norwood operation","authors":"Alaa Aljiffry, Yanbo Xu, Shenda Hong, Justin B. Long, Jimeng Sun, Kevin O. Maher","doi":"10.21037/jmai-22-35","DOIUrl":"https://doi.org/10.21037/jmai-22-35","url":null,"abstract":"","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139302480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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