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Example of CT myocardial perfusion image postprocessing analysis showing a transmural perfusion defect in the LAD (left image) with decreased myocardial blood flow in the interventricular septum and apex of the left ventricle (right image) as displayed by the shown colormap
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Cardiac CT using non-enhanced coronary artery calcium scoring (CACS) and coronary CT angiography (cCTA) has been proven to provide excellent evaluation of coronary artery disease (CAD) combining anatomical and morphological assessment of CAD for cardiovascular risk stratification and therapeutic decision-making, in addition to providing prognostic...
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... reported a diagnostic accuracy, sensitivity, and specificity of 68%, 53%, and 85% of CTP added to cCTA stenosis > 70% for predicting ischemia. The addition of resting CTP to the evaluation with cCTA resulted in an increased AUC of 0.75 vs. 0.68 for hemodynamically significant CAD. A case example of CTP post-processing image analysis is shown in Fig. ...
Citations
... Certain algorithms [9] based on recurrent CNNs have been applied to image and component analysis of coronary atherosclerotic plaques in order to facilitate clinical assessment and prognostic evaluation of artery stenosis [9]. The rapidly growing amount of relevant imaging data has triggered an interest in automated diagnostic tools [10]. ...
... The data emerging from those diagnostic and prognostic tools are multiplying at a rapid rate, often rendering preventive medical action insufficient or inaccurate. Automated segmentation and quantification of enhanced postprocessing medical images, using DL, combined with algorithms that take into consideration risk factors such as age, gender, class of obesity and smoking habits, are about to become a strong predictor of cardiovascular events [10]. ...
The evolving role of artificial intelligence (AI) in healthcare can shift the route of automated, supervised and computer-aided diagnostic radiology. An extensive literature review was conducted to consider the potential of designing a fully automated, complete diagnostic platform capable of integrating the current medical imaging technologies. Adjuvant, targeted, non-systematic research was regarded as necessary, especially to the end-user medical expert, for the completeness, understanding and terminological clarity of this discussion article that focuses on giving a representative and inclusive idea of the evolutional strides that have taken place, not including an AI architecture technical evaluation. Recent developments in AI applications for assessing various organ systems, as well as enhancing oncology and histopathology, show significant impact on medical practice. Published research outcomes of AI picture segmentation and classification algorithms exhibit promising accuracy, sensitivity and specificity. Progress in this field has led to the introduction of the concept of explainable AI, which ensures transparency of deep learning architectures, enabling human involvement in clinical decision making, especially in critical healthcare scenarios. Structure and language standardization of medical reports, along with interdisciplinary collaboration between medical and technical experts, are crucial for research coordination. Patient personal data should always be handled with confidentiality and dignity, while ensuring legality in the attribution of responsibility, particularly in view of machines lacking empathy and self-awareness. The results of our literature research demonstrate the strong potential of utilizing AI architectures, mainly convolutional neural networks, in medical imaging diagnostics, even though a complete automated diagnostic platform, enabling full body scanning, has not yet been presented.
... (дата обращения: 19.02.2022). 15 Колесников Ю. С. Методика проведения фокус-групп. Psyfactor.org. ...
... Был составлен список медицинских учреждений для проведения опроса и сформирована анкета, которая была выслана интервьюируемым заранее. Фокус-группы созавались на основе индикаторов, определяющих положение участников, и показателей, характеризующих их квалификацию, опыт работы, возраст и другие виды поведения 15 . При проведении опросов обеспечивалась гомогенность фокус-групп. ...
... Большой интерес представляет оценка перспективности основных направлений развития ИИ в медицинской практике. С учетом областей своей деятельности респонденты выделили следующие направления: Социологические аспекты направлений внедрения СОЦИОЛОГИЯ УПРАВЛЕНИЯ 57,6 % высказались в пользу мониторинга состояния тяжелобольных с целью выявления критических ситуаций и назначения реанимационных мероприятий; 36,4 % перспективными направлениями считают нейропротезирование и биопротезирование [15]; 30,3 % -врачейассистентов с ИИ, а 24,2 % и 15,2 % -компьютерное зрение и речевую диагностику пациентов соответственно. При этом 30,3 % отметили, что на данном этапе ни один продукт ИИ не сможет сравниться со знаниями и опытом практикующего врача. ...
The article analyzes the structure of morbidity in the region and identifies the main directions for artificial intelligence implementation in Russia. In order to identify the attitude of clinical physicians towards the artificial intelligence products, the authors performed a sociological survey. To develop the artificial intelligence in the Kemerovo Region it is necessary to use artificial intelligence products and build competence centers for implementing these products in regional healthcare. The main ways of development are strategic programs; creative teams within scientific and educational centers; introduction of automated workplaces for doctors. The authors’ proposals can improve the accuracy of diagnosis, simplify the treatment of patients with various diseases, and rise the healthcare of the Kemerovo region – Kuzbass to a new level.
... Coronary CT angiography (CCTA) has evolved to the standard of care for the noninvasive evaluation of coronary artery disease (CAD) and plaque quantification [1][2][3]. Previous studies have shown the predictive ability of coronary plaque measurements (i.e., plaque burden, composition, and high-risk features) for improved risk stratification [4][5][6]. ...
Background: To investigate gender differences in epicardial adipose tissue (EAT) and plaque composition by coronary CT angiography (CCTA) and the association with cardiovascular outcome. Methods: Data of 352 patients (64.2 ± 10.3 years, 38% female) with suspected coronary artery disease (CAD) who underwent CCTA were retrospectively analyzed. EAT volume and plaque composition from CCTA were compared between men and women. Major adverse cardiovascular events (MACE) were recorded from follow-up. Results: Men were more likely to have obstructive CAD, higher Agatston scores, and a larger total and non-calcified plaque burden. In addition, men displayed more adverse plaque characteristics and EAT volume compared to women (all p < 0.05). After a median follow-up of 5.1 years, MACE occurred in 8 women (6%) and 22 men (10%). In multivariable analysis, Agatston calcium score (HR 1.0008, p = 0.014), EAT volume (HR 1.067, p = 0.049), and low-attenuation plaque (HR 3.82, p = 0.036) were independent predictors for MACE in men, whereas only low-attenuation plaque (HR 2.42, p = 0.041) showed predictive value for events in women. Conclusion: Women demonstrated less overall plaque burden, fewer adverse plaque characteristics, and a smaller EAT volume compared to men. However, low-attenuation plaque is a predictor for MACE in both genders. Thus, a differentiated plaque analysis is warranted to understand gender differences of atherosclerosis to guide medical therapy and prevention strategies.
... Key Words: spiral computed tomography, coronary artery disease, outcome measures, diabetes mellitus (J Thorac Imaging 2021;00:000-000) C oronary computed tomography angiography (cCTA) is a well-established modality for the assessment of coronary artery disease (CAD) and noninvasive plaque quantification. [1][2][3] Recent studies have demonstrated the predictive value of coronary plaque measures (ie, extent, composition, location) for improved risk stratification. [4][5][6] Diabetes mellitus (DM) is a well-known cardiovascular risk factor for CAD with increased rates in morbidity and mortality. ...
Purpose:
To investigate the long-term prognostic value of coronary computed tomography angiography (cCTA)-derived plaque information on major adverse cardiac events (MACE) in patients with and without diabetes mellitus.
Materials and methods:
In all, 64 patients with diabetes (63.3±10.1 y, 66% male) and suspected coronary artery disease who underwent cCTA were matched with 297 patients without diabetes according to age, sex, cardiovascular risk factors, and statin and antithrombotic therapy. MACE were recorded. cCTA-derived risk scores and plaque measures were assessed. The discriminatory power to identify MACE was evaluated using multivariable regression analysis and concordance indices.
Results:
After a median follow-up of 5.4 years, MACE occurred in 31 patients (8.6%). In patients with diabetes, cCTA risk scores and plaque measures were significantly higher compared with nondiabetic patients (all P<0.05). The following plaque measures were predictors of MACE using multivariable Cox regression analysis (hazard ratio [HR]) in patients with diabetes: segment stenosis score (HR=1.20, P<0.001), low-attenuation plaque (HR=3.47, P=0.05), and in nondiabetic patients: segment stenosis score (HR=1.92, P<0.001), Agatston score (HR=1.0009, P=0.04), and low-attenuation plaque (HR=4.15, P=0.04). A multivariable model showed a significantly improved C-index of 0.96 (95% confidence interval: 0.94-0.0.97) for MACE prediction, when compared with single measures alone.
Conclusion:
Diabetes is associated with a significantly higher extent of coronary artery disease and plaque features, which have independent predictive values for MACE. cCTA-derived plaque information portends improved risk stratification of patients with diabetes beyond the assessment of obstructive stenosis on cCTA alone with subsequent impact on individualized treatment decision-making.
... The application of artificial intelligence (AI) technology in cardiovascular imaging has become a research hotspot in recent years, as it may reduce treatment cost and help avoid unnecessary testing [1]. AI technology has been progressively applied for processing multiple modal images, such as auxiliary electrocardiograph diagnosis [2], cardiac computerized tomography (CT) detection [3], and radionuclide myocardial perfusion imaging [4]. In the diagnosis and treatment of heart diseases, AI techniques have been applied to electrocardiography, vectorcardiography, echocardiography, and electronic health records [5]. ...
Ultrasound is one of the most important examinations for clinical diagnosis of cardiovascular diseases. The speed of image movements driven by the frequency of the beating heart is faster than that of other organs. This particularity of echocardiography poses a challenge for sonographers to diagnose accurately. However, artificial intelligence for detection, functional evaluation, and disease diagnosis has gradually become an alternative for accurate diagnosis and treatment using echocardiography. This work discusses the current application of artificial intelligence in echocardiography technology, its limitations, and future development directions.
... Coronary CT angiography (cCTA) is a well-established modality for the assessment of coronary artery disease (CAD) and non-invasive plaque quanti cation [1][2][3]. Recent studies have demonstrated the predictive value of coronary plaque measures (i.e. extent, composition, location) for improved risk strati cation [4,5]. ...
PurposeTo investigate the long-term prognostic value of coronary CT angiography (cCTA)-derived plaque information on major adverse cardiac events (MACE) in patients with and without diabetes mellitus. Methods64 patients with diabetes (63.3±10.1 years, 66% male) and suspected coronary artery disease (CAD) who underwent cCTA were matched with 297 patients without diabetes according to age, sex, cardiovascular risk factors, statin and antithrombotic therapy. Major adverse cardiac events (MACE) were recorded. cCTA-derived risk scores and plaque measures were assessed. The discriminatory power to identify MACE was evaluated using multivariable regression analysis and concordance indices (CIs).ResultsAfter a median follow-up of 5.4 years, MACE occurred in 31 patients (8.6%). In patients with diabetes, cCTA risk scores and plaque measures were significantly higher compared to non-diabetic patients (all p <0.05). The following plaque measures were predictors of MACE using multivariable Cox regression analysis (hazard ratio [HR]) in patients with diabetes: segment stenosis score (HR 1.20, p <0.001), low-attenuation plaque (HR 3.47, p =0.05), and in non-diabetic patients: segment stenosis score (HR 1.92, p <0.001), Agatston score (HR 1.0009, p =0.04), and low-attenuation plaque (HR 4.15, p =0.04). A multivariable model showed significantly improved C-index of 0.96 (95% CI 0.94-0.0.97) for MACE prediction, when compared to single measures alone.Conclusion
Diabetes is associated with a significantly higher extent of CAD and plaque features, which have independent predictive values for MACE. cCTA-derived plaque information portends improved risk stratification of patients with diabetes beyond assessment of obstructive stenosis on cCTA alone.
... Machine learning (ML) has been receiving vigorous attention for its promise to improve diagnostic accuracy, risk stratification, and outcome prediction in cardiovascular CT imaging (4)(5)(6). However, the inherent complexity of ML algorithms, and inconsistencies in the model performance and interpretation of results remain major challenges in the clinical applicability of these algorithms. ...
... In the next paper by Dr Verena Brandt of the Medical University of South Carolina under the leadership of Dr Joseph Schoepf and together with co-authors from multiple centers in Germany [11]. The goal of this review paper is to provide an overview about the contemporary state of Machine Learning based algorithms in cardiac CT, and to provide clinicians with currently available scientific data on the clinical validation and implementation of these algorithms for the prediction of ischemia-specific CAD and cardiovascular outcome. ...
