Pub Date : 2025-01-30DOI: 10.1016/j.mcpdig.2025.100197
Ethan L. Williams MD , Daniel Huynh MD , Mohamed Estai MBBS, PhD , Toshi Sinha PhD , Matthew Summerscales MBBS , Yogesan Kanagasingam PhD
This study aimed to evaluate the quality of evidence for using machine learning models to predict inpatient admissions from emergency department triage data, ultimately aiming to improve patient flow management. A comprehensive literature search was conducted according to the PRISMA guidelines across 5 databases, PubMed, Embase, Web of Science, Scopus, and CINAHL, on August 1, 2024, for English-language studies published between August 1, 2014, and August 1, 2024. This yielded 700 articles, of which 66 were screened in full, and 31 met the inclusion and exclusion criteria. Model quality was assessed using the PROBAST appraisal tool and a modified TRIPOD+AI framework, alongside reported model performance metrics. Seven studies demonstrated rigorous methodology and promising in silico performance, with an area under the receiver operating characteristic ranging from 0.81 to 0.93. However, further performance analysis was limited by heterogeneity in model development and an unclear-to-high risk of bias and applicability concerns in the remaining 24 articles, as evaluated by the PROBAST tool. The current literature demonstrates a good degree of in silico accuracy in predicting inpatient admission from triage data alone. Future research should emphasize transparent model development and reporting, temporal validation, concept drift analysis, exploration of emerging artificial intelligence techniques, and analysis of real-world patient flow metrics to comprehensively assess the usefulness of these models.
{"title":"Predicting Inpatient Admissions From Emergency Department Triage Using Machine Learning: A Systematic Review","authors":"Ethan L. Williams MD , Daniel Huynh MD , Mohamed Estai MBBS, PhD , Toshi Sinha PhD , Matthew Summerscales MBBS , Yogesan Kanagasingam PhD","doi":"10.1016/j.mcpdig.2025.100197","DOIUrl":"10.1016/j.mcpdig.2025.100197","url":null,"abstract":"<div><div>This study aimed to evaluate the quality of evidence for using machine learning models to predict inpatient admissions from emergency department triage data, ultimately aiming to improve patient flow management. A comprehensive literature search was conducted according to the PRISMA guidelines across 5 databases, PubMed, Embase, Web of Science, Scopus, and CINAHL, on August 1, 2024, for English-language studies published between August 1, 2014, and August 1, 2024. This yielded 700 articles, of which 66 were screened in full, and 31 met the inclusion and exclusion criteria. Model quality was assessed using the PROBAST appraisal tool and a modified TRIPOD+AI framework, alongside reported model performance metrics. Seven studies demonstrated rigorous methodology and promising in silico performance, with an area under the receiver operating characteristic ranging from 0.81 to 0.93. However, further performance analysis was limited by heterogeneity in model development and an unclear-to-high risk of bias and applicability concerns in the remaining 24 articles, as evaluated by the PROBAST tool. The current literature demonstrates a good degree of in silico accuracy in predicting inpatient admission from triage data alone. Future research should emphasize transparent model development and reporting, temporal validation, concept drift analysis, exploration of emerging artificial intelligence techniques, and analysis of real-world patient flow metrics to comprehensively assess the usefulness of these models.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 1","pages":"Article 100197"},"PeriodicalIF":0.0,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143444616","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}
Pub Date : 2025-01-21DOI: 10.1016/j.mcpdig.2025.100196
Oscar Freyer , Isabella C. Wiest Dr. med , Stephen Gilbert PhD
{"title":"Policing the Boundary Between Responsible and Irresponsible Placing on the Market of Large Language Model Health Applications","authors":"Oscar Freyer , Isabella C. Wiest Dr. med , Stephen Gilbert PhD","doi":"10.1016/j.mcpdig.2025.100196","DOIUrl":"10.1016/j.mcpdig.2025.100196","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 1","pages":"Article 100196"},"PeriodicalIF":0.0,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143402575","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}
Pub Date : 2025-01-10DOI: 10.1016/j.mcpdig.2025.100194
D.M. Anisuzzaman PhD, Jeffrey G. Malins PhD, John I. Jackson PhD, Eunjung Lee PhD, Jwan A. Naser MBBS, Behrouz Rostami PhD, Grace Greason BA, Jared G. Bird MD, Paul A. Friedman MD, Jae K. Oh MD, Patricia A. Pellikka MD, Jeremy J. Thaden MD, Francisco Lopez-Jimenez MD, MSc, MBA, Zachi I. Attia PhD, Sorin V. Pislaru MD, PhD, Garvan C. Kane MD, PhD
Objective
To develop a fully end-to-end deep learning framework capable of estimating left ventricular ejection fraction (LVEF), estimating patient age, and classifying patient sex from echocardiographic videos, including videos collected using handheld cardiac ultrasound (HCU).
Patients and Methods
Deep learning models were trained using retrospective transthoracic echocardiography (TTE) data collected in Mayo Clinic Rochester and surrounding Mayo Clinic Health System sites (training: 6432 studies and internal validation: 1369 studies). Models were then evaluated using retrospective TTE data from the 3 Mayo Clinic sites (Rochester, n=1970; Arizona, n=1367; Florida, n=1562) before being applied to a prospective dataset of handheld ultrasound and TTE videos collected from 625 patients. Study data were collected between January 1, 2018 and February 29, 2024.
Results
Models showed strong performance on the retrospective TTE datasets (LVEF regression: root mean squared error (RMSE)=6.83%, 6.53%, and 6.95% for Rochester, Arizona, and Florida cohorts, respectively; classification of LVEF ≤40% versus LVEF > 40%: area under curve (AUC)=0.962, 0.967, and 0.980 for Rochester, Arizona, and Florida, respectively; age: RMSE=9.44% for Rochester; sex: AUC=0.882 for Rochester), and performed comparably for prospective HCU versus TTE data (LVEF regression: RMSE=6.37% for HCU vs 5.57% for TTE; LVEF classification: AUC=0.974 vs 0.981; age: RMSE=10.35% vs 9.32%; sex: AUC=0.896 vs 0.933).
Conclusion
Robust TTE datasets can be used to effectively power HCU deep learning models, which in turn demonstrates focused diagnostic images can be obtained with handheld devices.
{"title":"Leveraging Comprehensive Echo Data to Power Artificial Intelligence Models for Handheld Cardiac Ultrasound","authors":"D.M. Anisuzzaman PhD, Jeffrey G. Malins PhD, John I. Jackson PhD, Eunjung Lee PhD, Jwan A. Naser MBBS, Behrouz Rostami PhD, Grace Greason BA, Jared G. Bird MD, Paul A. Friedman MD, Jae K. Oh MD, Patricia A. Pellikka MD, Jeremy J. Thaden MD, Francisco Lopez-Jimenez MD, MSc, MBA, Zachi I. Attia PhD, Sorin V. Pislaru MD, PhD, Garvan C. Kane MD, PhD","doi":"10.1016/j.mcpdig.2025.100194","DOIUrl":"10.1016/j.mcpdig.2025.100194","url":null,"abstract":"<div><h3>Objective</h3><div>To develop a fully end-to-end deep learning framework capable of estimating left ventricular ejection fraction (LVEF), estimating patient age, and classifying patient sex from echocardiographic videos, including videos collected using handheld cardiac ultrasound (HCU).</div></div><div><h3>Patients and Methods</h3><div>Deep learning models were trained using retrospective transthoracic echocardiography (TTE) data collected in Mayo Clinic Rochester and surrounding Mayo Clinic Health System sites (training: 6432 studies and internal validation: 1369 studies). Models were then evaluated using retrospective TTE data from the 3 Mayo Clinic sites (Rochester, n=1970; Arizona, n=1367; Florida, n=1562) before being applied to a prospective dataset of handheld ultrasound and TTE videos collected from 625 patients. Study data were collected between January 1, 2018 and February 29, 2024.</div></div><div><h3>Results</h3><div>Models showed strong performance on the retrospective TTE datasets (LVEF regression: root mean squared error (RMSE)=6.83%, 6.53%, and 6.95% for Rochester, Arizona, and Florida cohorts, respectively; classification of LVEF ≤40% versus LVEF > 40%: area under curve (AUC)=0.962, 0.967, and 0.980 for Rochester, Arizona, and Florida, respectively; age: RMSE=9.44% for Rochester; sex: AUC=0.882 for Rochester), and performed comparably for prospective HCU versus TTE data (LVEF regression: RMSE=6.37% for HCU vs 5.57% for TTE; LVEF classification: AUC=0.974 vs 0.981; age: RMSE=10.35% vs 9.32%; sex: AUC=0.896 vs 0.933).</div></div><div><h3>Conclusion</h3><div>Robust TTE datasets can be used to effectively power HCU deep learning models, which in turn demonstrates focused diagnostic images can be obtained with handheld devices.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 1","pages":"Article 100194"},"PeriodicalIF":0.0,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349121","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}
Pub Date : 2025-01-02DOI: 10.1016/j.mcpdig.2024.100193
Taina J. Hudson DO , Michael Albrecht MD , Timothy R. Smith MD , Gregory A. Ator MD , Jeffrey A. Thompson PhD , Tina Shah MD, MPH , Denton Shanks DO, MPH
{"title":"Impact of Ambient Artificial Intelligence Documentation on Cognitive Load","authors":"Taina J. Hudson DO , Michael Albrecht MD , Timothy R. Smith MD , Gregory A. Ator MD , Jeffrey A. Thompson PhD , Tina Shah MD, MPH , Denton Shanks DO, MPH","doi":"10.1016/j.mcpdig.2024.100193","DOIUrl":"10.1016/j.mcpdig.2024.100193","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 1","pages":"Article 100193"},"PeriodicalIF":0.0,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143149189","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}
Pub Date : 2024-12-24DOI: 10.1016/j.mcpdig.2024.100191
Sandeep Reddy MBBS, MSc, PhD
The growing incorporation of artificial intelligence (AI) into medical device software offers substantial prospects and regulatory hurdles. As AI software as a medical device (AI-SaMD) continues to advance, ensuring its safety, effectiveness, and security is paramount. Nevertheless, the regulatory environment needs more cohesion, with various regions implementing diverse strategies. This paper underscores the necessity for globally harmonized AI-SaMD regulations by examining key regulatory frameworks from the United States, the European Union, China, and Australia. The article also explores crucial elements for harmonization, including algorithm transparency, risk management, data security, and clinical evaluation. Furthermore, the paper advocates for implementing international standards and global data security protocols, emphasizing the significance of cross-border cooperation to ensure the worldwide safety and efficacy of AI-SaMD.
{"title":"Global Harmonization of Artificial Intelligence-Enabled Software as a Medical Device Regulation: Addressing Challenges and Unifying Standards","authors":"Sandeep Reddy MBBS, MSc, PhD","doi":"10.1016/j.mcpdig.2024.100191","DOIUrl":"10.1016/j.mcpdig.2024.100191","url":null,"abstract":"<div><div>The growing incorporation of artificial intelligence (AI) into medical device software offers substantial prospects and regulatory hurdles. As AI software as a medical device (AI-SaMD) continues to advance, ensuring its safety, effectiveness, and security is paramount. Nevertheless, the regulatory environment needs more cohesion, with various regions implementing diverse strategies. This paper underscores the necessity for globally harmonized AI-SaMD regulations by examining key regulatory frameworks from the United States, the European Union, China, and Australia. The article also explores crucial elements for harmonization, including algorithm transparency, risk management, data security, and clinical evaluation. Furthermore, the paper advocates for implementing international standards and global data security protocols, emphasizing the significance of cross-border cooperation to ensure the worldwide safety and efficacy of AI-SaMD.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 1","pages":"Article 100191"},"PeriodicalIF":0.0,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143149190","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}
To convert a generic paper-based patient decision aid (PtDA) into digital format and assess its usability through α and β testing, recognizing the growing role of digital health technologies in empowering patients in shared decision-making.
Patients and Methods
After a systematic PtDA development process in the period 2020-2022, the conversion process included scoping, prototyping, design, and testing phases. An α test evaluated internal usability, whereas 2 β tests explored the feasibility for breast and colorectal cancer patients preconsultation and postconsultation on adjuvant therapy using the preparation for decision-making scale.
Results
Seven PtDA experts gave positive feedback on the quality of the digital PtDA in the α test. The 6 patients who participated in the preconsultation β test were positive about the purpose and ease of use of the digital PtDA and rated decision preparation on a scale of 0-100 with a mean score of 81.3, whereas the postconsultation β test with 10 patients reported an overall mean score of 72.0. The conversion involved several iterative design processes, showing potential for high adoption and uptake due to its convenience and accessibility before and after the consultation.
Conclusion
The digital PtDA provides a user-friendly solution for patients. Overall, the conversion of a paper-based PtDA to a digital format proved successful, and the test results were promising. Further research is recommended to test the digital version on a large scale.
{"title":"From Paper to Pixels: Digital Transition of a Patient Decision Aid—A Pilot Study","authors":"Bettina Mølri Knudsen MA , Karina Olling BSN , Lisbeth Høilund Gamst BSN , Charlotte Hald Fausbøll BSN , Karina Dahl Steffensen MD, PhD","doi":"10.1016/j.mcpdig.2024.100190","DOIUrl":"10.1016/j.mcpdig.2024.100190","url":null,"abstract":"<div><h3>Objective</h3><div>To convert a generic paper-based patient decision aid (PtDA) into digital format and assess its usability through α and β testing, recognizing the growing role of digital health technologies in empowering patients in shared decision-making.</div></div><div><h3>Patients and Methods</h3><div>After a systematic PtDA development process in the period 2020-2022, the conversion process included scoping, prototyping, design, and testing phases. An α test evaluated internal usability, whereas 2 β tests explored the feasibility for breast and colorectal cancer patients preconsultation and postconsultation on adjuvant therapy using the preparation for decision-making scale.</div></div><div><h3>Results</h3><div>Seven PtDA experts gave positive feedback on the quality of the digital PtDA in the α test. The 6 patients who participated in the preconsultation β test were positive about the purpose and ease of use of the digital PtDA and rated decision preparation on a scale of 0-100 with a mean score of 81.3, whereas the postconsultation β test with 10 patients reported an overall mean score of 72.0. The conversion involved several iterative design processes, showing potential for high adoption and uptake due to its convenience and accessibility before and after the consultation.</div></div><div><h3>Conclusion</h3><div>The digital PtDA provides a user-friendly solution for patients. Overall, the conversion of a paper-based PtDA to a digital format proved successful, and the test results were promising. Further research is recommended to test the digital version on a large scale.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 1","pages":"Article 100190"},"PeriodicalIF":0.0,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143149194","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}
Pub Date : 2024-12-20DOI: 10.1016/j.mcpdig.2024.100189
Austin M. Stroud MA , Michele D. Anzabi MBE , Journey L. Wise BA , Barbara A. Barry PhD , Momin M. Malik PhD , Michelle L. McGowan PhD , Richard R. Sharp PhD
Claims abound that advances in artificial intelligence (AI) will permeate virtually every aspect of medicine and transform clinical practice. Simultaneously, concerns about the safety and equity of health care AI have prompted ethical and regulatory scrutiny from multiple oversight bodies. Positioned at the intersection of these perspectives, academic medical centers (AMCs) are charged with navigating the safe and responsible implementation of health care AI. Decisions about the use of AI at AMCs are complicated by uncertainties regarding the risks posed by these technologies and a lack of consensus on best practices for managing these risks. In this article, we highlight several potential harms that may arise in the adoption of health care AI, with a focus on risks to patients, clinicians, and medical practice. In addition, we describe several strategies that AMCs might adopt now to address concerns about the safety and ethical uses of health care AI. Our analysis aims to support AMCs as they seek to balance AI innovation with proactive oversight.
{"title":"Toward Safe and Ethical Implementation of Health Care Artificial Intelligence: Insights From an Academic Medical Center","authors":"Austin M. Stroud MA , Michele D. Anzabi MBE , Journey L. Wise BA , Barbara A. Barry PhD , Momin M. Malik PhD , Michelle L. McGowan PhD , Richard R. Sharp PhD","doi":"10.1016/j.mcpdig.2024.100189","DOIUrl":"10.1016/j.mcpdig.2024.100189","url":null,"abstract":"<div><div>Claims abound that advances in artificial intelligence (AI) will permeate virtually every aspect of medicine and transform clinical practice. Simultaneously, concerns about the safety and equity of health care AI have prompted ethical and regulatory scrutiny from multiple oversight bodies. Positioned at the intersection of these perspectives, academic medical centers (AMCs) are charged with navigating the safe and responsible implementation of health care AI. Decisions about the use of AI at AMCs are complicated by uncertainties regarding the risks posed by these technologies and a lack of consensus on best practices for managing these risks. In this article, we highlight several potential harms that may arise in the adoption of health care AI, with a focus on risks to patients, clinicians, and medical practice. In addition, we describe several strategies that AMCs might adopt now to address concerns about the safety and ethical uses of health care AI. Our analysis aims to support AMCs as they seek to balance AI innovation with proactive oversight.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 1","pages":"Article 100189"},"PeriodicalIF":0.0,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143149187","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}
Pub Date : 2024-12-18DOI: 10.1016/j.mcpdig.2024.100188
Panagiotis Korfiatis PhD , Timothy L. Kline PhD , Holly M. Meyer MS , Sana Khalid MS , Timothy Leiner MD , Brenna T. Loufek MS , Daniel Blezek PhD , David E. Vidal JD , Robert P. Hartman MD , Lori J. Joppa MBA , Andrew D. Missert PhD , Theodora A. Potretzke MD , Jerome P. Taubel , Jason A. Tjelta BS , Matthew R. Callstrom MD , Eric E. Williamson MD
Integration of AI-enabled algorithms into the radiology workflow presents a complex array of challenges that span operational, technical, clinical, and regulatory domains. Successfully overcoming these hurdles requires a multifaceted approach, including strategic planning, educational initiatives, and careful consideration of the practical implications for radiologists' workloads. Institutions must navigate these challenges with a clear understanding of the potential benefits and limitations of both vended and in-house developed AI tools.
{"title":"Implementing Artificial Intelligence Algorithms in the Radiology Workflow: Challenges and Considerations","authors":"Panagiotis Korfiatis PhD , Timothy L. Kline PhD , Holly M. Meyer MS , Sana Khalid MS , Timothy Leiner MD , Brenna T. Loufek MS , Daniel Blezek PhD , David E. Vidal JD , Robert P. Hartman MD , Lori J. Joppa MBA , Andrew D. Missert PhD , Theodora A. Potretzke MD , Jerome P. Taubel , Jason A. Tjelta BS , Matthew R. Callstrom MD , Eric E. Williamson MD","doi":"10.1016/j.mcpdig.2024.100188","DOIUrl":"10.1016/j.mcpdig.2024.100188","url":null,"abstract":"<div><div>Integration of AI-enabled algorithms into the radiology workflow presents a complex array of challenges that span operational, technical, clinical, and regulatory domains. Successfully overcoming these hurdles requires a multifaceted approach, including strategic planning, educational initiatives, and careful consideration of the practical implications for radiologists' workloads. Institutions must navigate these challenges with a clear understanding of the potential benefits and limitations of both vended and in-house developed AI tools.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 1","pages":"Article 100188"},"PeriodicalIF":0.0,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143149188","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}
Pub Date : 2024-12-14DOI: 10.1016/j.mcpdig.2024.100187
Cappi Chan MSc , Min Wang PhD , Luoyi Kong MSc , Leanne Li MSc , Lawrence Wing Chi Chan PhD
Computer tomography–derived fractional flow reserve (CT-FFR) represents a significant advancement in noninvasive cardiac functional assessment. This technology uses computer simulation and anatomical information from computer tomography of coronary angiogram to calculate the CT-FFR value at each point within the coronary vasculature. These values serve as a critical reference for cardiologists in making informed treatment decisions and planning. Emerging evidence suggests that CT-FFR has the potential to enhance the specificity of computer tomography of coronary angiogram, thereby reducing the need for additional diagnostic examinations such as invasive coronary angiography and cardiac magnetic resonance imaging. This could result in savings in financial cost, time, and resources for both patients and health care providers. However, it is important to note that although CT-FFR holds great promise, there are limitations to this technology. Users should be cautious of common pitfalls associated with its use. A comprehensive understanding of these limitations is essential for effectively applying CT-FFR in clinical practice.
{"title":"Clinical Applications of Fractional Flow Reserve Derived from Computed Tomography in Coronary Artery Disease","authors":"Cappi Chan MSc , Min Wang PhD , Luoyi Kong MSc , Leanne Li MSc , Lawrence Wing Chi Chan PhD","doi":"10.1016/j.mcpdig.2024.100187","DOIUrl":"10.1016/j.mcpdig.2024.100187","url":null,"abstract":"<div><div>Computer tomography–derived fractional flow reserve (CT-FFR) represents a significant advancement in noninvasive cardiac functional assessment. This technology uses computer simulation and anatomical information from computer tomography of coronary angiogram to calculate the CT-FFR value at each point within the coronary vasculature. These values serve as a critical reference for cardiologists in making informed treatment decisions and planning. Emerging evidence suggests that CT-FFR has the potential to enhance the specificity of computer tomography of coronary angiogram, thereby reducing the need for additional diagnostic examinations such as invasive coronary angiography and cardiac magnetic resonance imaging. This could result in savings in financial cost, time, and resources for both patients and health care providers. However, it is important to note that although CT-FFR holds great promise, there are limitations to this technology. Users should be cautious of common pitfalls associated with its use. A comprehensive understanding of these limitations is essential for effectively applying CT-FFR in clinical practice.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 1","pages":"Article 100187"},"PeriodicalIF":0.0,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143149192","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}
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