Artificial Intelligence Advances in the World of Cardiovascular Imaging

Patel, Bhakti; Makaryus, Amgad N.

doi:10.3390/healthcare10010154

Cited by 15 publications

(13 citation statements)

References 41 publications

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“…Of the 21 selected review studies [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ], a total of 14 studies specifically focused on the themes of the review [ 13 , 14 , 15 , 16 , 17 , 18 , 20 , 21 , 23 , 24 , 26 , 27 , 28 , 31 ], while the remaining [ 11 , 12 , 19 , 22 , 25 , 29 , 30 ] had a focus on different topics.…”

Section: Resultsmentioning

confidence: 99%

Reimagining Radiology: A Comprehensive Overview of Reviews at the Intersection of Mobile and Domiciliary Radiology over the Last Five Years

Lepri,

Oddi,

Gulino

et al. 2024

Bioengineering

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(Background) Domiciliary radiology, which originated in pioneering studies in 1958, has transformed healthcare, particularly during the COVID-19 pandemic, through advancements such as miniaturization and digitization. This evolution, driven by the synergy of advanced technologies and robust data networks, reshapes the intersection of domiciliary radiology and mobile technology in healthcare delivery. (Objective) The objective of this study is to overview the reviews in this field with reference to the last five years to face the state of development and integration of this practice in the health domain. (Methods) A review was conducted on PubMed and Scopus, applying a standard checklist and a qualification process. The outcome detected 21 studies. (Key Content and Findings) The exploration of mobile and domiciliary radiology unveils a compelling and optimistic perspective. Notable strides in this dynamic field include the integration of Artificial Intelligence (AI), revolutionary applications in telemedicine, and the educational potential of mobile devices. Post-COVID-19, telemedicine advances and the influential role of AI in pediatric radiology signify significant progress. Mobile mammography units emerge as a solution for underserved women, highlighting the crucial importance of early breast cancer detection. The investigation into domiciliary radiology, especially with mobile X-ray equipment, points toward a promising frontier, prompting in-depth research for comprehensive insights into its potential benefits for diverse populations. The study also identifies limitations and suggests future exploration in various domains of mobile and domiciliary radiology. A key recommendation stresses the strategic prioritization of multi-domain technology assessment initiatives, with scientific societies’ endorsement, emphasizing regulatory considerations for responsible and ethical technology integration in healthcare practices. The broader landscape of technology assessment should aim to be innovative, ethical, and aligned with societal needs and regulatory standards. (Conclusions) The dynamic state of the field is evident, with active exploration of new frontiers. This overview also provides a roadmap, urging scholars, industry players, and regulators to collectively contribute to the further integration of this technology in the health domain.

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Section: Resultsmentioning

confidence: 99%

Reimagining Radiology: A Comprehensive Overview of Reviews at the Intersection of Mobile and Domiciliary Radiology over the Last Five Years

Lepri,

Oddi,

Gulino

et al. 2024

Bioengineering

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“…In this context, various image-based mathematical models have been developed to assess hemodynamic significance and improve cost-effectiveness ( 27 ). In a single-center observational study, OCT-based computational analysis demonstrated a better correlation with conventional FFR than other imaging modalities, such as computational tomography or IVUS ( 6 , 28 ).…”

Section: Discussionmentioning

confidence: 99%

Assessment of fractional flow reserve in intermediate coronary stenosis using optical coherence tomography-based machine learning

Cha

Nguyen

Tran³

et al. 2023

Front. Cardiovasc. Med.

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ObjectivesThis study aimed to evaluate and compare the diagnostic accuracy of machine learning (ML)- fractional flow reserve (FFR) based on optical coherence tomography (OCT) with wire-based FFR irrespective of the coronary territory.BackgroundML techniques for assessing hemodynamics features including FFR in coronary artery disease have been developed based on various imaging modalities. However, there is no study using OCT-based ML models for all coronary artery territories.MethodsOCT and FFR data were obtained for 356 individual coronary lesions in 130 patients. The training and testing groups were divided in a ratio of 4:1. The ML-FFR was derived for the testing group and compared with the wire-based FFR in terms of the diagnosis of ischemia (FFR ≤ 0.80).ResultsThe mean age of the subjects was 62.6 years. The numbers of the left anterior descending, left circumflex, and right coronary arteries were 130 (36.5%), 110 (30.9%), and 116 (32.6%), respectively. Using seven major features, the ML-FFR showed strong correlation (r = 0.8782, P < 0.001) with the wire-based FFR. The ML-FFR predicted wire-based FFR ≤ 0.80 in the test set with sensitivity of 98.3%, specificity of 61.5%, and overall accuracy of 91.7% (area under the curve: 0.948). External validation showed good correlation (r = 0.7884, P < 0.001) and accuracy of 83.2% (area under the curve: 0.912).ConclusionOCT-based ML-FFR showed good diagnostic performance in predicting FFR irrespective of the coronary territory. Because the study was a small-size study, the results should be warranted the performance in further large-scale research.

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“…Technology enabled care, including digital health technology with “AI inside,” expands the reach of a limited number of clinicians to the communities where people live and, if thoughtfully deployed, improves health equity through greater access to standardized high-quality care 17 . The management of chronic disease using AI-enabled digital screening mechanisms supports earlier identification of disease progression, optimizing clinical workflows to serve patients in a timely manner with the appropriate resources 18 . Those individuals who need chronic management can be managed at home using many of the systems just discussed.…”

Section: Continuous Care and Optimized Resource Allocationmentioning

confidence: 99%

Collaborative Intelligence to catalyze the digital transformation of healthcare

Bhatt,

Bae

2023

npj Digit. Med.

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Collaborative intelligence reflects the promise and limits of leveraging artificial intelligence (AI) technologies in clinical care. It involves the use of advanced analytics and computing power with an understanding that humans bear responsibility for the accuracy, completeness and any inherent bias found in the training data. Clinicians benefit from using this technology to address increased complexity and information overload, support continuous care and optimized resource allocation, and to enact efforts to eradicate disparities in health care access and quality. This requires active clinician engagement with the technology, a general understanding of how the machine produced its insight, the limitations of the algorithms, and the need to screen datasets for bias. Importantly, by interacting, the clinician and the analytics will create trust based on the clinician’s critical thinking skills leveraged to discern value of machine outputs within clinical context. Utilization of collaborative intelligence should be staged with the level of understanding and evidence. It is particularly well suited to low-complexity non-urgent care and to identifying individuals at rising risk within a population. Clinician involvement in algorithm development and the amassing of evidence to support safety and efficacy will propel adoption. Utilization of collaborative intelligence represents the natural progression of health care innovation, and if thoughtfully constructed and equitably deployed, holds the promise to decrease clinician burden and improve access to care.

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Artificial Intelligence Advances in the World of Cardiovascular Imaging

Cited by 15 publications

References 41 publications

Reimagining Radiology: A Comprehensive Overview of Reviews at the Intersection of Mobile and Domiciliary Radiology over the Last Five Years

Reimagining Radiology: A Comprehensive Overview of Reviews at the Intersection of Mobile and Domiciliary Radiology over the Last Five Years

Assessment of fractional flow reserve in intermediate coronary stenosis using optical coherence tomography-based machine learning

Collaborative Intelligence to catalyze the digital transformation of healthcare

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