Recent advances in artificial intelligence for cardiac CT: Enhancing diagnosis and prognosis prediction

Coronary artery disease evaluation during transcatheter aortic valve replacement work-up using photon-counting CT and artificial intelligence

PURPOSE

The purpose of this study was to evaluate the capabilities of photon-counting (PC) CT combined with artificial intelligence-derived coronary computed tomography angiography (PC-CCTA) stenosis quantification and fractional flow reserve prediction (FFRai) for the assessment of coronary artery disease (CAD) in transcatheter aortic valve replacement (TAVR) work-up.

MATERIALS AND METHODS

Consecutive patients with severe symptomatic aortic valve stenosis referred for pre-TAVR work-up between October 2021 and June 2023 were included in this retrospective tertiary single-center study. All patients underwent both PC-CCTA and ICA within three months for reference standard diagnosis. PC-CCTA stenosis quantification (at 50% level) and FFRai (at 0.8 level) were predicted using two deep learning models (CorEx, Spimed-AI). Diagnostic performance for global CAD evaluation (at least one significant stenosis ≥ 50% or FFRai ≤ 0.8) was assessed.

RESULTS

A total of 260 patients (138 men, 122 women) with a mean age of 78.7 ± 8.1 (standard deviation) years (age range: 51-93 years) were evaluated. Significant CAD on ICA was present in 126/260 patients (48.5%). Per-patient sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were 96.0% (95% confidence interval [CI]: 91.0-98.7), 68.7% (95% CI: 60.1-76.4), 74.3 % (95% CI: 69.1-78.8), 94.8% (95% CI: 88.5-97.8), and 81.9% (95% CI: 76.7-86.4) for PC-CCTA, and 96.8% (95% CI: 92.1-99.1), 87.3% (95% CI: 80.5-92.4), 87.8% (95% CI: 82.2-91.8), 96.7% (95% CI: 91.7-98.7), and 91.9% (95% CI: 87.9-94.9) for FFRai. Area under the curve of FFRai was 0.92 (95% CI: 0.88-0.95) compared to 0.82 for PC-CCTA (95% CI: 0.77-0.87) (P < 0.001). FFRai-guidance could have prevented the need for ICA in 121 out of 260 patients (46.5%) vs. 97 out of 260 (37.3%) using PC-CCTA alone (P < 0.001).

CONCLUSION

Deep learning-based photon-counting FFRai evaluation improves the accuracy of PC-CCTA ≥ 50% stenosis detection, reduces the need for ICA, and may be incorporated into the clinical TAVR work-up for the assessment of CAD.

Data set terminology of deep learning in medicine: a historical review and recommendation

Medicine and deep learning-based artificial intelligence (AI) engineering represent two distinct fields each with decades of published history. The current rapid convergence of deep learning and medicine has led to significant advancements, yet it has also introduced ambiguity regarding data set terms common to both fields, potentially leading to miscommunication and methodological discrepancies. This narrative review aims to give historical context for these terms, accentuate the importance of clarity when these terms are used in medical deep learning contexts, and offer solutions to mitigate misunderstandings by readers from either field. Through an examination of historical documents, including articles, writing guidelines, and textbooks, this review traces the divergent evolution of terms for data sets and their impact. Initially, the discordant interpretations of the word 'validation' in medical and AI contexts are explored. We then show that in the medical field as well, terms traditionally used in the deep learning domain are becoming more common, with the data for creating models referred to as the 'training set', the data for tuning of parameters referred to as the 'validation (or tuning) set', and the data for the evaluation of models as the 'test set'. Additionally, the test sets used for model evaluation are classified into internal (random splitting, cross-validation, and leave-one-out) sets and external (temporal and geographic) sets. This review then identifies often misunderstood terms and proposes pragmatic solutions to mitigate terminological confusion in the field of deep learning in medicine. We support the accurate and standardized description of these data sets and the explicit definition of data set splitting terminologies in each publication. These are crucial methods for demonstrating the robustness and generalizability of deep learning applications in medicine. This review aspires to enhance the precision of communication, thereby fostering more effective and transparent research methodologies in this interdisciplinary field.

Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years

BACKGROUND

Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology.

RESULTS

We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology.

CONCLUSIONS

The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology.

Comparison of two deep-learning image reconstruction algorithms on cardiac CT images: A phantom study

PURPOSE

The purpose of this study was to compare the performance of Precise IQ Engine (PIQE) and Advanced intelligent Clear-IQ Engine (AiCE) algorithms on image-quality according to the dose level in a cardiac computed tomography (CT) protocol.

MATERIALS AND METHODS

Acquisitions were performed using the CT ACR 464 phantom at three dose levels (volume CT dose indexes: 7.1/5.2/3.1 mGy) using a prospective cardiac CT protocol. Raw data were reconstructed using the three levels of AiCE and PIQE (Mild, Standard and Strong). The noise power spectrum (NPS) and task-based transfer function (TTF) for bone and acrylic inserts were computed. The detectability index (d') was computed to model the detectability of the coronary lumen (350 Hounsfield units and 4-mm diameter) and non-calcified plaque (40 Hounsfield units and 2-mm diameter).

RESULTS

Noise magnitude values were lower with PIQE than with AiCE (-13.4 ± 6.0 [standard deviation (SD)] % for Mild, -20.4 ± 4.0 [SD] % for Standard and -32.6 ± 2.6 [SD] % for Strong levels). The average NPS spatial frequencies shifted towards higher frequencies with PIQE than with AiCE (21.9 ± 3.5 [SD] % for Mild, 20.1 ± 3.0 [SD] % for Standard and 12.5 ± 3.5 [SD] % for Strong levels). The TTF values at fifty percent (f50) values shifted towards higher frequencies with PIQE than with AiCE for acrylic inserts but, for bone inserts, f50 values were found to be close. Whatever the dose and DLR level, d' values of both simulated cardiac lesions were higher with PIQE than with AiCE. For the simulated coronary lumen, d' values were better by 35.1 ± 9.3 (SD) % on average for all dose levels for Mild, 43.2 ± 5.0 (SD) % for Standard, and 62.6 ± 1.2 (SD) % for Strong levels.

CONCLUSION

Compared to AiCE, PIQE reduced noise, improved spatial resolution, noise texture and detectability of simulated cardiac lesions. PIQE seems to have a greater potential for dose reduction in cardiac CT acquisition.

Prevalence of major embolic findings and incidental findings on early cardiac CT in patients with suspected ischemic stroke

PURPOSE

The purpose of this study was to assess the type and prevalence of stroke and non-stroke-related findings diagnosed on early cardiac computed tomography (CT) in patients with suspected stroke. The secondary objective was to assess the clinical consequences on the management of patients with non-stroke-related conditions identified by early cardiac CT.

MATERIALS AND METHODS

This single-center, retrospective, observational study included 1111 consecutive patients with suspected ischemic stroke between November 2018 and March 2020 who underwent cardiac CT examination in addition to the usual brain CT protocol (i.e., non-enhanced brain CT, perfusion brain CT when needed, aortic arch and supra-aortic CT angiography, and post contrast brain CT). There were 562 women and 549 men with a median age of 74 years (range: 60-85 years). Of these, 415 (415/1111; 37.4%) patients had ischemic stroke and 692 (692/1111; 62.3%) had no stroke. Cardiac CT examinations were retrospectively reviewed for cardiac CT findings at high embolic risk and clinically significant extracardiac incidental findings.

RESULTS

Among 1111 included patients, 89 (89/1111; 8.0%) had a stroke-related condition identified on early cardiac CT. This was significantly more frequent in patients with ischemic stroke (66/415; 15.9%) by comparison with those without ischemic stroke (23/696; 3.3%) (P < 0.001), with 41 patients (41/415; 9.9%) diagnosed with left atrial thrombus. Cardiac CT revealed a clinically significant non-stroke-related finding in 173 patients (173/1111; 15.6%), including 17 pulmonary embolisms (1.5%), seven suspicious pulmonary lesions (0.6%), and three breast lesions suspected to be malignant (0.3%). Twenty out of 173 patients (20/173; 11.5%) with incidental findings on early cardiac CT had a change in their management.

CONCLUSION

This study shows that adding early cardiac CT to brain CT during the acute phase of an ischemic stroke leads to a higher rate of etiological diagnoses and highlights the major interest of looking at the bigger picture.