Deep Learning Algorithm to Detect Cardiac Sarcoidosis From Echocardiographic Movies

What does artificial intelligence mean in rheumatology?

Intelligence is the ability of humans to learn from experiences to ascribe conscious weights and unconscious biases to modulate their outputs from given inputs. Transferring this ability to computers is artificial intelligence (AI). The ability of computers to understand data in an intelligent manner is machine learning. When such learning is with images and videos, which involves deeper layers of artificial neural networks, it is described as deep learning. Large language models are the latest development in AI which incorporate self-learning into deep learning through transformers. AI in Rheumatology has immense potential to revolutionize healthcare and research. Machine learning could aid clinical diagnosis and decision-making, and deep learning could extend this to analyze images of radiology or positron emission tomography scans or histopathology images to aid a clinician's diagnosis. Analysis of routinely obtained patient data or continuously collected information from wearables could predict disease flares. Analysis of high-volume genomics, transcriptomics, proteomics, or metabolomics data from patients could help identify novel markers of disease prognosis. AI might identify newer therapeutic targets based on in-silico modelling of omics data. AI could help automate medical administrative work such as inputting information into electronic health records or transcribing clinic notes. AI could help automate patient education and counselling. Beyond the clinic, AI has the potential to aid medical education. The ever-expanding capabilities of AI models bring along with them considerable ethical challenges, particularly related to risks of misuse. Nevertheless, the widespread use of AI in Rheumatology is inevitable and a progress with great potential.

Current Applications of Artificial Intelligence in Sarcoidosis

PURPOSE

Sarcoidosis is a complex disease which can affect nearly every organ system with manifestations ranging from asymptomatic imaging findings to sudden cardiac death. As such, diagnosis and prognostication are topics of continued investigation. Recent technological advancements have introduced multiple modalities of artificial intelligence (AI) to the study of sarcoidosis. Machine learning, deep learning, and radiomics have predominantly been used to study sarcoidosis.

METHODS

Articles were collected by searching online databases using keywords such as sarcoid, machine learning, artificial intelligence, radiomics, and deep learning. Article titles and abstracts were reviewed for relevance by a single reviewer. Articles written in languages other than English were excluded.

CONCLUSIONS

Machine learning may be used to help diagnose pulmonary sarcoidosis and prognosticate in cardiac sarcoidosis. Deep learning is most comprehensively studied for diagnosis of pulmonary sarcoidosis and has less frequently been applied to prognostication in cardiac sarcoidosis. Radiomics has primarily been used to differentiate sarcoidosis from malignancy. To date, the use of AI in sarcoidosis is limited by the rarity of this disease, leading to small, suboptimal training sets. Nevertheless, there are applications of AI that have been used to study other systemic diseases, which may be adapted for use in sarcoidosis. These applications include discovery of new disease phenotypes, discovery of biomarkers of disease onset and activity, and treatment optimization.

The Role of Echocardiography in the Contemporary Diagnosis and Prognosis of Cardiac Sarcoidosis: A Comprehensive Review

Cardiac sarcoidosis (CS) is a rare inflammatory disorder characterised by the presence of non-caseating granulomas within the myocardium. Contemporary studies have revealed that 25-30% of patients with systemic sarcoidosis have cardiac involvement, with detection rates increasing in the era of advanced cardiac imaging. The use of late gadolinium enhancement cardiac magnetic resonance and 18fluorodeoxy glucose positron emission tomography (FDG-PET) imaging has superseded endomyocardial biopsy for the diagnosis of CS. Echocardiography has historically been used as a screening tool with abnormalities triggering the need for advanced imaging, and as a tool to assess cardiac function. Regional wall thinning or aneurysm formation in a noncoronary distribution may indicate granuloma infiltration. Thinning of the basal septum in the setting of extracardiac sarcoidosis carries a high specificity for cardiac involvement. Abnormal myocardial echotexture and eccentric hypertrophy may be suggestive of active myocardial inflammation. The presence of right-ventricular involvement as indicated by free-wall aneurysms can mimic arrhythmogenic right-ventricular cardiomyopathy. More recently, the use of myocardial strain has increased the sensitivity of echocardiography in diagnosing cardiac involvement. Echocardiography is limited in prognostication, with impaired left-ventricular (LV) ejection fraction and LV dilatation being the only established independent predictors of mortality. More research is required to explore how advanced echocardiographic technologies can increase both the diagnostic sensitivity and prognostic ability of this modality in CS.

Machine-Learning-Based Diagnostics of Cardiac Sarcoidosis Using Multi-Chamber Wall Motion Analyses

BACKGROUND

Hindered by its unspecific clinical and phenotypical presentation, cardiac sarcoidosis (CS) remains a challenging diagnosis.

OBJECTIVE

Utilizing cardiac magnetic resonance imaging (CMR), we acquired multi-chamber volumetrics and strain feature tracking for a support vector machine learning (SVM)-based diagnostic approach to CS.

METHOD

Forty-five CMR-negative (CMR(-), 56.5(53.0;63.0)years), eighteen CMR-positive (CMR(+), 64.0(57.8;67.0)years) sarcoidosis patients and forty-four controls (CTRL, 56.5(53.0;63.0)years)) underwent CMR examination. Cardiac parameters were processed using the classifiers of logistic regression, KNN(K-nearest-neighbor), DT (decision tree), RF (random forest), SVM, GBoost, XGBoost, Voting and feature selection.

RESULTS

In a three-cluster analysis of CTRL versus vs. CMR(+) vs. CMR(-), RF and Voting classifier yielded the highest prediction rates (81.82%). The two-cluster analysis of CTRL vs. all sarcoidosis (All Sarc.) yielded high prediction rates with the classifiers logistic regression, RF and SVM (96.97%), and low prediction rates for the analysis of CMR(+) vs. CMR(-), which were augmented using feature selection with logistic regression (89.47%).

CONCLUSION

Multi-chamber cardiac function and strain-based supervised machine learning provides a non-contrast approach to accurately differentiate between healthy individuals and sarcoidosis patients. Feature selection overcomes the algorithmically challenging discrimination between CMR(+) and CMR(-) patients, yielding high accuracy predictions. The study findings imply higher prevalence of cardiac involvement than previously anticipated, which may impact clinical disease management.

An explainable artificial intelligence-enabled electrocardiogram analysis model for the classification of reduced left ventricular function

Aims

The black box nature of artificial intelligence (AI) hinders the development of interpretable AI models that are applicable in clinical practice. We aimed to develop an AI model for classifying patients of reduced left ventricular ejection fraction (LVEF) from 12-lead electrocardiograms (ECG) with the decision-interpretability.

Methods and results

We acquired paired ECG and echocardiography datasets from the central and co-operative institutions. For the central institution dataset, a random forest model was trained to identify patients with reduced LVEF among 29 907 ECGs. Shapley additive explanations were applied to 7196 ECGs. To extract the model's decision criteria, the calculated Shapley additive explanations values were clustered for 192 non-paced rhythm patients in which reduced LVEF was predicted. Although the extracted criteria were different for each cluster, these criteria generally comprised a combination of six ECG findings: negative T-wave inversion in I/V5-6 leads, low voltage in I/II/V4-6 leads, Q wave in V3-6 leads, ventricular activation time prolongation in I/V5-6 leads, S-wave prolongation in V2-3 leads, and corrected QT interval prolongation. Similarly, for the co-operative institution dataset, the extracted criteria comprised a combination of the same six ECG findings. Furthermore, the accuracy of seven cardiologists' ECG readings improved significantly after watching a video explaining the interpretation of these criteria (before, 62.9% ± 3.9% vs. after, 73.9% ± 2.4%; P = 0.02).

Conclusion

We visually interpreted the model's decision criteria to evaluate its validity, thereby developing a model that provided the decision-interpretability required for clinical application.

The Role of Artificial Intelligence in Echocardiography

Echocardiography is an integral part of the diagnosis and management of cardiovascular disease. The use and application of artificial intelligence (AI) is a rapidly expanding field in medicine to improve consistency and reduce interobserver variability. AI can be successfully applied to echocardiography in addressing variance during image acquisition and interpretation. Furthermore, AI and machine learning can aid in the diagnosis and management of cardiovascular disease. In the realm of echocardiography, accurate interpretation is largely dependent on the subjective knowledge of the operator. Echocardiography is burdened by the high dependence on the level of experience of the operator, to a greater extent than other imaging modalities like computed tomography, nuclear imaging, and magnetic resonance imaging. AI technologies offer new opportunities for echocardiography to produce accurate, automated, and more consistent interpretations. This review discusses machine learning as a subfield within AI in relation to image interpretation and how machine learning can improve the diagnostic performance of echocardiography. This review also explores the published literature outlining the value of AI and its potential to improve patient care.

Atherosclerotic Cardiovascular Risk Stratification in the Rheumatic Diseases:: An Integrative, Multiparametric Approach

Cardiovascular disease (CVD) risk is increased in most inflammatory rheumatic diseases (IRDs), reiterating the role of inflammation in the initiation and progression of atherosclerosis. An inverse association of CVD risk with body weight and lipid levels has been described in IRDs. Coronary artery calcium scores, plaque burden and characteristics, and carotid plaques on ultrasound optimize CVD risk estimate in IRDs. Biomarkers of cardiac injury, autoantibodies, lipid biomarkers, and cytokines also improve risk assessment in IRDs. Machine learning and deep learning algorithms for phenotype and image analysis hold promise to improve CVD risk stratification in IRDs.

Diagnosis and Treatment of Cardiac Sarcoidosis

About 5% of sarcoidosis patients develop clinically manifest cardiac features. Cardiac sarcoidosis (CS) typically presents with conduction abnormalities, ventricular arrhythmias and heart failure. Its diagnosis is challenging and requires a substantial degree of clinical suspicion as well as expertise in advanced cardiac imaging. Adverse events, particularly malignant arrhythmias and development of heart failure, are common among CS patients. A timely diagnosis is paramount to ameliorating outcomes for these patients. Despite weak evidence, immunosuppression (primarily with corticosteroids) is generally recommended in the presence of active inflammation in the myocardium. The burden of malignant arrhythmias remains important regardless of treatment, thus leading to the recommended use of an implantable cardioverter defibrillator in most patients with clinically manifest CS.

Quantitative computed tomography and machine learning: recent data in fibrotic interstitial lung disease and potential role in pulmonary sarcoidosis

PURPOSE OF REVIEW

The aim of this study was to summarize quantitative computed tomography (CT) and machine learning data in fibrotic lung disease and to explore the potential application of these technologies in pulmonary sarcoidosis.

RECENT FINDINGS

Recent data in the use of quantitative CT in fibrotic interstitial lung disease (ILD) are covered. Machine learning includes deep learning, a branch of machine learning particularly suited to medical imaging analysis. Deep learning imaging biomarker research in ILD is currently undergoing accelerated development, driven by technological advances in image processing and analysis. Fundamental concepts and goals related to deep learning imaging research in ILD are discussed. Recent work highlighted in this review has been performed in patients with idiopathic pulmonary fibrosis (IPF). Quantitative CT and deep learning have not been applied to pulmonary sarcoidosis, although there are recent deep learning data in cardiac sarcoidosis.

SUMMARY

Pulmonary sarcoidosis presents unsolved problems for which quantitative CT and deep learning may provide unique solutions: in particular, the exploration of the long-standing question of whether sarcoidosis should be viewed as a single disease or as an umbrella term for disorders that might usefully be considered as separate diseases.

Artificial intelligence in echocardiography: Review and limitations including epistemological concerns

BACKGROUND AND PURPOSE

In this review we describe the use of artificial intelligence in the field of echocardiography. Various aspects and terminologies used in artificial intelligence are explained in an easy-to-understand manner and supplemented with illustrations related to echocardiography. Limitations of artificial intelligence, including epistemologic concerns from a philosophical standpoint, are also discussed.

METHODS

A narrative review of relevant papers was conducted.

CONCLUSION

We provide an overview of the usefulness of artificial intelligence in echocardiography and focus on how it can supplement current day-to-day clinical practice in the assessment of various cardiovascular disease entities. On the other hand, there are significant limitations, including epistemological concerns, which need to be kept in perspective.

Clinical Features, Histopathology and Differential Diagnosis of Sarcoidosis

Sarcoidosis is a chameleon disease of unknown etiology, characterized by the growth of non-necrotizing and non-caseating granulomas and manifesting with clinical pictures that vary on the basis of the organs that are mainly affected. Lungs and intrathoracic lymph nodes are the sites that are most often involved, but virtually no organ is spared from this disease. Histopathology is distinctive but not pathognomonic, since the findings can be found also in other granulomatous disorders. The knowledge of these findings is important because it could be helpful to differentiate sarcoidosis from the other granulomatous-related diseases. This review aims at illustrating the main clinical and histopathological findings that could help clinicians in their routine clinical practice.

Prospects for cardiovascular medicine using artificial intelligence

As the importance of artificial intelligence (AI) in the clinical setting increases, the need for clinicians to understand AI is also increasing. This review focuses on the fundamental principles of AI and the current state of cardiovascular AI. Various types of cardiovascular AI have been developed for evaluating examinations such as X-rays, electrocardiogram, echocardiography, computed tomography, and magnetic resonance imaging. Cardiovascular AI achieves high accuracy in diagnostic support and prognosis prediction. Furthermore, it can even detect abnormalities that were previously difficult for cardiologists to detect. Randomized controlled trials begin to be reported to verify the usefulness of cardiovascular AI. The day is approaching when cardiovascular AI will be commonly used in clinical practice. Various types of medical AI will be used for cardiovascular care; however, it will not replace medical doctors. We need to understand the strengths and weaknesses of medical AI so that cardiologists can effectively use AI to improve the medical care of patients.