Diagnostics

Research

15 pages, 2255 KiB

Open AccessArticle

Pulmonary Hypertension Detection Non-Invasively at Point-of-Care Using a Machine-Learned Algorithm

by Navid Nemati, Timothy Burton, Farhad Fathieh, Horace R. Gillins, Ian Shadforth, Shyam Ramchandani and Charles R. Bridges

Diagnostics 2024, 14(9), 897; https://doi.org/10.3390/diagnostics14090897 - 25 Apr 2024

Viewed by 285

Abstract

Artificial intelligence, particularly machine learning, has gained prominence in medical research due to its potential to develop non-invasive diagnostics. Pulmonary hypertension presents a diagnostic challenge due to its heterogeneous nature and similarity in symptoms to other cardiovascular conditions. Here, we describe the development [...] Read more.

Artificial intelligence, particularly machine learning, has gained prominence in medical research due to its potential to develop non-invasive diagnostics. Pulmonary hypertension presents a diagnostic challenge due to its heterogeneous nature and similarity in symptoms to other cardiovascular conditions. Here, we describe the development of a supervised machine learning model using non-invasive signals (orthogonal voltage gradient and photoplethysmographic) and a hand-crafted library of 3298 features. The developed model achieved a sensitivity of 87% and a specificity of 83%, with an overall Area Under the Receiver Operator Characteristic Curve (AUC-ROC) of 0.93. Subgroup analysis showed consistent performance across genders, age groups and classes of PH. Feature importance analysis revealed changes in metrics that measure conduction, repolarization and respiration as significant contributors to the model. The model demonstrates promising performance in identifying pulmonary hypertension, offering potential for early detection and intervention when embedded in a point-of-care diagnostic system. Full article

(This article belongs to the Special Issue Artificial Intelligence in Cardiology Diagnosis )

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13 pages, 2033 KiB

Open AccessArticle

Development of a Non-Invasive Machine-Learned Point-of-Care Rule-Out Test for Coronary Artery Disease

by Timothy Burton, Farhad Fathieh, Navid Nemati, Horace R. Gillins, Ian P. Shadforth, Shyam Ramchandani and Charles R. Bridges

Diagnostics 2024, 14(7), 719; https://doi.org/10.3390/diagnostics14070719 - 28 Mar 2024

Cited by 1 | Viewed by 628

Abstract

The current standard of care for coronary artery disease (CAD) requires an intake of radioactive or contrast enhancement dyes, radiation exposure, and stress and may take days to weeks for referral to gold-standard cardiac catheterization. The CAD diagnostic pathway would greatly benefit from [...] Read more.

The current standard of care for coronary artery disease (CAD) requires an intake of radioactive or contrast enhancement dyes, radiation exposure, and stress and may take days to weeks for referral to gold-standard cardiac catheterization. The CAD diagnostic pathway would greatly benefit from a test to assess for CAD that enables the physician to rule it out at the point of care, thereby enabling the exploration of other diagnoses more rapidly. We sought to develop a test using machine learning to assess for CAD with a rule-out profile, using an easy-to-acquire signal (without stress/radiation) at the point of care. Given the historic disparate outcomes between sexes and urban/rural geographies in cardiology, we targeted equal performance across sexes in a geographically accessible test. Noninvasive photoplethysmogram and orthogonal voltage gradient signals were simultaneously acquired in a representative clinical population of subjects before invasive catheterization for those with CAD (gold-standard for the confirmation of CAD) and coronary computed tomographic angiography for those without CAD (excellent negative predictive value). Features were measured from the signal and used in machine learning to predict CAD status. The machine-learned algorithm achieved a sensitivity of 90% and specificity of 59%. The rule-out profile was maintained across both sexes, as well as all other relevant subgroups. A test to assess for CAD using machine learning on a noninvasive signal has been successfully developed, showing high performance and rule-out ability. Confirmation of the performance on a large clinical, blinded, enrollment-gated dataset is required before implementation of the test in clinical practice. Full article

(This article belongs to the Special Issue Artificial Intelligence in Cardiology Diagnosis )

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16 pages, 3281 KiB

Open AccessArticle

TAVI-PREP: A Deep Learning-Based Tool for Automated Measurements Extraction in TAVI Planning

by Marcel Santaló-Corcoy, Denis Corbin, Olivier Tastet, Frédéric Lesage, Thomas Modine, Anita Asgar and Walid Ben Ali

Diagnostics 2023, 13(20), 3181; https://doi.org/10.3390/diagnostics13203181 - 11 Oct 2023

Cited by 3 | Viewed by 1058

Abstract

Background: Transcatheter aortic valve implantation (TAVI) is a less invasive alternative to open-heart surgery for treating severe aortic stenosis. Despite its benefits, the risk of procedural complications necessitates careful preoperative planning. Methods: This study proposes a fully automated deep learning-based method, TAVI-PREP, for [...] Read more.

Background: Transcatheter aortic valve implantation (TAVI) is a less invasive alternative to open-heart surgery for treating severe aortic stenosis. Despite its benefits, the risk of procedural complications necessitates careful preoperative planning. Methods: This study proposes a fully automated deep learning-based method, TAVI-PREP, for pre-TAVI planning, focusing on measurements extracted from computed tomography (CT) scans. The algorithm was trained on the public MM-WHS dataset and a small subset of private data. It uses MeshDeformNet for 3D surface mesh generation and a 3D Residual U-Net for landmark detection. TAVI-PREP is designed to extract 22 different measurements from the aortic valvular complex. A total of 200 CT-scans were analyzed, and automatic measurements were compared to the ones made manually by an expert cardiologist. A second cardiologist analyzed 115 scans to evaluate inter-operator variability. Results: High Pearson correlation coefficients between the expert and the algorithm were obtained for most parameters (0.90–0.97), except for left and right coronary height (0.8 and 0.72, respectively). Similarly, the mean absolute relative error was within 5% for most measurements, except for left and right coronary height (11.6% and 16.5%, respectively). A greater consensus was observed among experts than when compared to the automatic approach, with TAVI-PREP showing no discernable bias towards either the lower or higher ends of the measurement spectrum. Conclusions: TAVI-PREP provides reliable and time-efficient measurements of the aortic valvular complex that could aid clinicians in the preprocedural planning of TAVI procedures. Full article

(This article belongs to the Special Issue Artificial Intelligence in Cardiology Diagnosis )

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13 pages, 3082 KiB

Open AccessArticle

Synthetic Attenuation Correction Maps for SPECT Imaging Using Deep Learning: A Study on Myocardial Perfusion Imaging

by Mariana Andrea Prieto Canalejo, Aley Palau San Pedro, Ricardo Geronazzo, Daniel Mauricio Minsky, Luis Eduardo Juárez-Orozco and Mauro Namías

Diagnostics 2023, 13(13), 2214; https://doi.org/10.3390/diagnostics13132214 - 29 Jun 2023

Cited by 1 | Viewed by 1501

Abstract

(1) Background: The CT-based attenuation correction of SPECT images is essential for obtaining accurate quantitative images in cardiovascular imaging. However, there are still many SPECT cameras without associated CT scanners throughout the world, especially in developing countries. Performing additional CT scans implies troublesome [...] Read more.

(1) Background: The CT-based attenuation correction of SPECT images is essential for obtaining accurate quantitative images in cardiovascular imaging. However, there are still many SPECT cameras without associated CT scanners throughout the world, especially in developing countries. Performing additional CT scans implies troublesome planning logistics and larger radiation doses for patients, making it a suboptimal solution. Deep learning (DL) offers a revolutionary way to generate complementary images for individual patients at a large scale. Hence, we aimed to generate linear attenuation coefficient maps from SPECT emission images reconstructed without attenuation correction using deep learning. (2) Methods: A total of 384 SPECT myocardial perfusion studies that used ^99mTc-sestamibi were included. A DL model based on a 2D U-Net architecture was trained using information from 312 patients. The quality of the generated synthetic attenuation correction maps (ACMs) and reconstructed emission values were evaluated using three metrics and compared to standard-of-care data using Bland–Altman plots. Finally, a quantitative evaluation of myocardial uptake was performed, followed by a semi-quantitative evaluation of myocardial perfusion. (3) Results: In a test set of 66 test patients, the ACM quality metrics were MSSIM = 0.97 ± 0.001 and NMAE = 3.08 ± 1.26 (%), and the reconstructed emission quality metrics were MSSIM = 0.99 ± 0.003 and NMAE = 0.23 ± 0.13 (%). The 95% limits of agreement (LoAs) at the voxel level for reconstructed SPECT images were: [−9.04; 9.00]%, and for the segment level, they were [−11; 10]%. The 95% LoAs for the Summed Stress Score values between the images reconstructed were [−2.8, 3.0]. When global perfusion scores were assessed, only 2 out of 66 patients showed changes in perfusion categories. (4) Conclusion: Deep learning can generate accurate attenuation correction maps from non-attenuation-corrected cardiac SPECT images. These high-quality attenuation maps are suitable for attenuation correction in myocardial perfusion SPECT imaging and could obviate the need for additional imaging in standalone SPECT scanners. Full article

(This article belongs to the Special Issue Artificial Intelligence in Cardiology Diagnosis )

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20 pages, 2211 KiB

Open AccessArticle

Heart Failure Emergency Readmission Prediction Using Stacking Machine Learning Model

by Md. Sohanur Rahman, Hasib Ryan Rahman, Johayra Prithula, Muhammad E. H. Chowdhury, Mosabber Uddin Ahmed, Jaya Kumar, M. Murugappan and Muhammad Salman Khan

Diagnostics 2023, 13(11), 1948; https://doi.org/10.3390/diagnostics13111948 - 02 Jun 2023

Cited by 2 | Viewed by 2211

Abstract

Heart failure is a devastating disease that has high mortality rates and a negative impact on quality of life. Heart failure patients often experience emergency readmission after an initial episode, often due to inadequate management. A timely diagnosis and treatment of underlying issues [...] Read more.

Heart failure is a devastating disease that has high mortality rates and a negative impact on quality of life. Heart failure patients often experience emergency readmission after an initial episode, often due to inadequate management. A timely diagnosis and treatment of underlying issues can significantly reduce the risk of emergency readmissions. The purpose of this project was to predict emergency readmissions of discharged heart failure patients using classical machine learning (ML) models based on Electronic Health Record (EHR) data. The dataset used for this study consisted of 166 clinical biomarkers from 2008 patient records. Three feature selection techniques were studied along with 13 classical ML models using five-fold cross-validation. A stacking ML model was trained using the predictions of the three best-performing models for final classification. The stacking ML model provided an accuracy, precision, recall, specificity, F1-score, and area under the curve (AUC) of 89.41%, 90.10%, 89.41%, 87.83%, 89.28%, and 0.881, respectively. This indicates the effectiveness of the proposed model in predicting emergency readmissions. The healthcare providers can intervene pro-actively to reduce emergency hospital readmission risk and improve patient outcomes and decrease healthcare costs using the proposed model. Full article

(This article belongs to the Special Issue Artificial Intelligence in Cardiology Diagnosis )

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Review

Jump to: Research, Other

15 pages, 6949 KiB

Open AccessReview

Artificial Intelligence in Transcatheter Aortic Valve Replacement: Its Current Role and Ongoing Challenges

by Mina M. Benjamin and Mark G. Rabbat

Diagnostics 2024, 14(3), 261; https://doi.org/10.3390/diagnostics14030261 - 25 Jan 2024

Viewed by 1169

Abstract

Transcatheter aortic valve replacement (TAVR) has emerged as a viable alternative to surgical aortic valve replacement, as accumulating clinical evidence has demonstrated its safety and efficacy. TAVR indications have expanded beyond high-risk or inoperable patients to include intermediate and low-risk patients with severe [...] Read more.

Transcatheter aortic valve replacement (TAVR) has emerged as a viable alternative to surgical aortic valve replacement, as accumulating clinical evidence has demonstrated its safety and efficacy. TAVR indications have expanded beyond high-risk or inoperable patients to include intermediate and low-risk patients with severe aortic stenosis. Artificial intelligence (AI) is revolutionizing the field of cardiology, aiding in the interpretation of medical imaging and developing risk models for at-risk individuals and those with cardiac disease. This article explores the growing role of AI in TAVR procedures and assesses its potential impact, with particular focus on its ability to improve patient selection, procedural planning, post-implantation monitoring and contribute to optimized patient outcomes. In addition, current challenges and future directions in AI implementation are highlighted. Full article

(This article belongs to the Special Issue Artificial Intelligence in Cardiology Diagnosis )

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13 pages, 1901 KiB

Open AccessEditor’s ChoiceReview

Artificial Intelligence in Coronary Artery Calcium Scoring Detection and Quantification

by Khaled Abdelrahman, Arthur Shiyovich, Daniel M. Huck, Adam N. Berman, Brittany Weber, Sumit Gupta, Rhanderson Cardoso and Ron Blankstein

Diagnostics 2024, 14(2), 125; https://doi.org/10.3390/diagnostics14020125 - 05 Jan 2024

Cited by 1 | Viewed by 1517

Abstract

Coronary artery calcium (CAC) is a marker of coronary atherosclerosis, and the presence and severity of CAC have been shown to be powerful predictors of future cardiovascular events. Due to its value in risk discrimination and reclassification beyond traditional risk factors, CAC has [...] Read more.

Coronary artery calcium (CAC) is a marker of coronary atherosclerosis, and the presence and severity of CAC have been shown to be powerful predictors of future cardiovascular events. Due to its value in risk discrimination and reclassification beyond traditional risk factors, CAC has been supported by recent guidelines, particularly for the purposes of informing shared decision-making regarding the use of preventive therapies. In addition to dedicated ECG-gated CAC scans, the presence and severity of CAC can also be accurately estimated on non-contrast chest computed tomography scans performed for other clinical indications. However, the presence of such “incidental” CAC is rarely reported. Advances in artificial intelligence have now enabled automatic CAC scoring for both cardiac and non-cardiac CT scans. Various AI approaches, from rule-based models to machine learning algorithms and deep learning, have been applied to automate CAC scoring. Convolutional neural networks, a deep learning technique, have had the most successful approach, with high agreement with manual scoring demonstrated in multiple studies. Such automated CAC measurements may enable wider and more accurate detection of CAC from non-gated CT studies, thus improving the efficiency of healthcare systems to identify and treat previously undiagnosed coronary artery disease. Full article

(This article belongs to the Special Issue Artificial Intelligence in Cardiology Diagnosis )

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19 pages, 1536 KiB

Open AccessReview

Artificial Intelligence, Wearables and Remote Monitoring for Heart Failure: Current and Future Applications

by Nitesh Gautam, Sai Nikhila Ghanta, Joshua Mueller, Munthir Mansour, Zhongning Chen, Clara Puente, Yu Mi Ha, Tushar Tarun, Gaurav Dhar, Kalai Sivakumar, Yiye Zhang, Ahmed Abu Halimeh, Ukash Nakarmi, Sadeer Al-Kindi, Deeptankar DeMazumder and Subhi J. Al’Aref

Diagnostics 2022, 12(12), 2964; https://doi.org/10.3390/diagnostics12122964 - 26 Nov 2022

Cited by 10 | Viewed by 3498

Abstract

Substantial milestones have been attained in the field of heart failure (HF) diagnostics and therapeutics in the past several years that have translated into decreased mortality but a paradoxical increase in HF-related hospitalizations. With increasing data digitalization and access, remote monitoring via wearables [...] Read more.

Substantial milestones have been attained in the field of heart failure (HF) diagnostics and therapeutics in the past several years that have translated into decreased mortality but a paradoxical increase in HF-related hospitalizations. With increasing data digitalization and access, remote monitoring via wearables and implantables have the potential to transform ambulatory care workflow, with a particular focus on reducing HF hospitalizations. Additionally, artificial intelligence and machine learning (AI/ML) have been increasingly employed at multiple stages of healthcare due to their power in assimilating and integrating multidimensional multimodal data and the creation of accurate prediction models. With the ever-increasing troves of data, the implementation of AI/ML algorithms could help improve workflow and outcomes of HF patients, especially time series data collected via remote monitoring. In this review, we sought to describe the basics of AI/ML algorithms with a focus on time series forecasting and the current state of AI/ML within the context of wearable technology in HF, followed by a discussion of the present limitations, including data integration, privacy, and challenges specific to AI/ML application within healthcare. Full article

(This article belongs to the Special Issue Artificial Intelligence in Cardiology Diagnosis )

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Other

Jump to: Research, Review

10 pages, 256 KiB

Open AccessPerspective

Embracing AI: The Imperative Tool for Echo Labs to Stay Ahead of the Curve

by Corina Maria Vasile and Xavier Iriart

Diagnostics 2023, 13(19), 3137; https://doi.org/10.3390/diagnostics13193137 - 06 Oct 2023

Cited by 3 | Viewed by 1092

Abstract

Advancements in artificial intelligence (AI) have rapidly transformed various sectors, and the field of echocardiography is no exception. AI-driven technologies hold immense potential to revolutionize echo labs’ diagnostic capabilities and improve patient care. This paper explores the importance for echo labs to embrace [...] Read more.

Advancements in artificial intelligence (AI) have rapidly transformed various sectors, and the field of echocardiography is no exception. AI-driven technologies hold immense potential to revolutionize echo labs’ diagnostic capabilities and improve patient care. This paper explores the importance for echo labs to embrace AI and stay ahead of the curve in harnessing its power. Our manuscript provides an overview of the growing impact of AI on medical imaging, specifically echocardiography. It highlights how AI-driven algorithms can enhance image quality, automate measurements, and accurately diagnose cardiovascular diseases. Additionally, we emphasize the importance of training echo lab professionals in AI implementation to optimize its integration into routine clinical practice. By embracing AI, echo labs can overcome challenges such as workload burden and diagnostic accuracy variability, improving efficiency and patient outcomes. This paper highlights the need for collaboration between echocardiography laboratory experts, AI researchers, and industry stakeholders to drive innovation and establish standardized protocols for implementing AI in echocardiography. In conclusion, this article emphasizes the importance of AI adoption in echocardiography labs, urging practitioners to proactively integrate AI technologies into their workflow and take advantage of their present opportunities. Embracing AI is not just a choice but an imperative for echo labs to maintain their leadership and excel in delivering state-of-the-art cardiac care in the era of advanced medical technologies. Full article

(This article belongs to the Special Issue Artificial Intelligence in Cardiology Diagnosis )

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