Collaborative AI and Laboratory Medicine integration in precision cardiovascular medicine

Cardiovascular Imaging in the Era of Precision Medicine: Insights from Advanced Technologies - A Narrative Review

Background and Aims

Cardiovascular diseases are responsible for a high mortality rate globally. Precision medicine has emerged as an essential tool for improving cardiovascular disease outcomes. In this context, using advanced imaging exams is fundamental in cardiovascular precision medicine, enabling more accurate diagnoses and customized treatments. This review aims to provide a concise review on how advanced cardiovascular imaging supports precision medicine, highlighting its benefits, challenges, and future directions.

Methods

A literature review was carried out using the Pubmed and Google Scholar databases, using search strategies that combined terms such as precision medicine, cardiovascular diseases, and imaging tests.

Results

More advanced analysis aimed at diagnosing and describing cardiovascular diseases in greater detail is made possible by tests such as cardiac computed tomography, cardiac magnetic resonance imaging, and cardiac positron emission tomography. In addition, the aggregation of imaging data with other omics data allows for more personalized treatment and a better description of patient profiles.

Conclusion

The use of advanced imaging tests is essential in cardiovascular precision medicine. Although there are still technical and ethical obstacles, it is essential that there is collaboration between health professionals, as well as investments in technology and education to better disseminate cardiovascular precision medicine and consequently promote improved patient outcomes.

The multidimensional value of natriuretic peptides in heart failure, integrating laboratory and clinical aspects

Natriuretic peptides (NP) play an essential role in heart failure (HF) regulation, and their measurement has improved diagnostic and prognostic accuracy. Clinical symptoms and objective measurements, such as NP levels, should be included in the HF definition to render it more reliable and consistent among observers, hospitals, and healthcare systems. BNP and NT-proBNP are reasonable surrogates for cardiac disease, and their measurement is critical to early diagnosis and risk stratification of HF patients. NPs should be measured in all patients presenting with dyspnea or other symptoms suggestive of HF to facilitate early diagnosis and risk stratification. Both BNP and NT-proBNP are currently used for guided HF management and display comparable diagnostic and prognostic accuracy. Standardized cutoffs for each NP assay are essential for data comparison. The value of NP testing is recognized at various levels, including patient empowerment and education, analytical and operational issues, clinical HF management, and cost-effectiveness.

Transforming clinical cardiology through neural networks and deep learning: A guide for clinicians

The rapid evolution of neural networks and deep learning has revolutionized various fields, with clinical cardiology being no exception. As traditional methods in cardiology encounter limitations, the integration of advanced computational techniques offers unprecedented opportunities in diagnostics and patient care. This review explores the transformative role of neural networks and deep learning in clinical cardiology, particularly focusing on their applications in electrocardiogram (ECG) analysis, imaging technologies, and cardiac prediction models. Among others, Deep Neural Networks (DNNs) have significantly surpassed traditional approaches in accuracy and efficiency in automatic ECG diagnosis. Convolutional Neural Networks (CNNs) are successfully applied in PET/CT and PET/MR imaging, enhancing diagnostic capabilities. Furthermore, deep learning algorithms have shown potential in improving cardiac prediction models, although challenges in interpretability and clinical integration remain. The review also addresses the 'black box' nature of neural networks and the ethical considerations surrounding their use in clinical settings. Overall, this review underscores the significant impact of neural networks and deep learning in cardiology, providing insights into current applications and future directions in the field.

Artificial intelligence-based preventive, personalized and precision medicine for cardiovascular disease/stroke risk assessment in rheumatoid arthritis patients: a narrative review

The challenges associated with diagnosing and treating cardiovascular disease (CVD)/Stroke in Rheumatoid arthritis (RA) arise from the delayed onset of symptoms. Existing clinical risk scores are inadequate in predicting cardiac events, and conventional risk factors alone do not accurately classify many individuals at risk. Several CVD biomarkers consider the multiple pathways involved in the development of atherosclerosis, which is the primary cause of CVD/Stroke in RA. To enhance the accuracy of CVD/Stroke risk assessment in the RA framework, a proposed approach involves combining genomic-based biomarkers (GBBM) derived from plasma and/or serum samples with innovative non-invasive radiomic-based biomarkers (RBBM), such as measurements of synovial fluid, plaque area, and plaque burden. This review presents two hypotheses: (i) RBBM and GBBM biomarkers exhibit a significant correlation and can precisely detect the severity of CVD/Stroke in RA patients. (ii) Artificial Intelligence (AI)-based preventive, precision, and personalized (aiP3) CVD/Stroke risk AtheroEdge™ model (AtheroPoint™, CA, USA) that utilizes deep learning (DL) to accurately classify the risk of CVD/stroke in RA framework. The authors conducted a comprehensive search using the PRISMA technique, identifying 153 studies that assessed the features/biomarkers of RBBM and GBBM for CVD/Stroke. The study demonstrates how DL models can be integrated into the AtheroEdge™-aiP3 framework to determine the risk of CVD/Stroke in RA patients. The findings of this review suggest that the combination of RBBM with GBBM introduces a new dimension to the assessment of CVD/Stroke risk in the RA framework. Synovial fluid levels that are higher than normal lead to an increase in the plaque burden. Additionally, the review provides recommendations for novel, unbiased, and pruned DL algorithms that can predict CVD/Stroke risk within a RA framework that is preventive, precise, and personalized.

Prediction of fractional flow reserve with enhanced ant lion optimized support vector machine

The evaluation of coronary morphology provides important guidance for the treatment of coronary heart disease (CHD). A chaotic Gaussian mutation antlion optimizer algorithm (CGALO) is proposed in the paper, and it is combined with SVM to construct a classification prediction model for Fractional flow reserve (FFR). To overcome the limitations of the original antlion optimizer (ALO) algorithm, the chaotic Gaussian mutation strategy is introduced, which leads to an improvement in its convergence speed and accuracy. To evaluate the proposed algorithm's performance, comparative experiments were conducted on 23 benchmark functions alongside 12 other cutting-edge optimization algorithms. The experimental outcomes demonstrate that the proposed algorithm achieves superior convergence accuracy and speed compared to the alternative comparison algorithms. Additionally, it is combined with SVM and FS to construct a hierarchical FFR classification model, which is utilized to make effective predictions for 84 patients at the affiliated hospital of medical school, Ningbo university. The experimental results demonstrate that the proposed model achieves an average accuracy of 92%. Moreover, it concludes that smoking history, number of lesion vessels, lesion location, diffuse lesions and ST segment changes, and other factors are the most critical indicators for FFR. Therefore, the model that has been established is a new FFR intelligent classification prediction technology that can effectively assist doctors in making corresponding decisions and evaluation plans.

Potentials and pitfalls of ChatGPT and natural-language artificial intelligence models for the understanding of laboratory medicine test results. An assessment by the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Working Group on Artificial Intelligence (WG-AI)

OBJECTIVES

ChatGPT, a tool based on natural language processing (NLP), is on everyone's mind, and several potential applications in healthcare have been already proposed. However, since the ability of this tool to interpret laboratory test results has not yet been tested, the EFLM Working group on Artificial Intelligence (WG-AI) has set itself the task of closing this gap with a systematic approach.

METHODS

WG-AI members generated 10 simulated laboratory reports of common parameters, which were then passed to ChatGPT for interpretation, according to reference intervals (RI) and units, using an optimized prompt. The results were subsequently evaluated independently by all WG-AI members with respect to relevance, correctness, helpfulness and safety.

RESULTS

ChatGPT recognized all laboratory tests, it could detect if they deviated from the RI and gave a test-by-test as well as an overall interpretation. The interpretations were rather superficial, not always correct, and, only in some cases, judged coherently. The magnitude of the deviation from the RI seldom plays a role in the interpretation of laboratory tests, and artificial intelligence (AI) did not make any meaningful suggestion regarding follow-up diagnostics or further procedures in general.

CONCLUSIONS

ChatGPT in its current form, being not specifically trained on medical data or laboratory data in particular, may only be considered a tool capable of interpreting a laboratory report on a test-by-test basis at best, but not on the interpretation of an overall diagnostic picture. Future generations of similar AIs with medical ground truth training data might surely revolutionize current processes in healthcare, despite this implementation is not ready yet.

Disruption vs. evolution in laboratory medicine. Current challenges and possible strategies, making laboratories and the laboratory specialist profession fit for the future

Since beginning of medical diagnostics, laboratory specialists have done an amazing job, continuously improving quality, spectrum and speed of laboratory tests, currently contributing to the majority of medical decision making. These improvements are mostly of an incremental evolutionary fashion, meaning improvements of current processes. Sometimes these evolutionary innovations are of a radical fashion, such as the invention of automated analyzers replacing manual testing or the implementation of mass spectrometry, leading to one big performance leap instead of several small ones. In few cases innovations may be of disruptive nature. In laboratory medicine this would be applicable to digitalization of medicine or the decoding of the human genetic material. Currently, laboratory medicine is again facing disruptive innovations or technologies, which need to be adapted to as soon as possible. One of the major disruptive technologies is the increasing availability and medical use of artificial intelligence. It is necessary to rethink the position of the laboratory specialist within healthcare settings and the added value he or she can provide to patient care. The future of the laboratory specialist profession is bright, as it the only medical profession comprising such vast experience in patient diagnostics. However, laboratory specialists need to develop strategies to provide this expertise, by adopting to the quickly evolving technologies and demands. This opinion paper summarizes some of the disruptive technologies as well as strategies to secure and/or improve the quality of diagnostic patient care and the laboratory specialist profession.

Precision Medicine and the future of Cardiovascular Diseases: A Clinically Oriented Comprehensive Review

Cardiac diseases form the lion's share of the global disease burden, owing to the paradigm shift to non-infectious diseases from infectious ones. The prevalence of CVDs has nearly doubled, increasing from 271 million in 1990 to 523 million in 2019. Additionally, the global trend for the years lived with disability has doubled, increasing from 17.7 million to 34.4 million over the same period. The advent of precision medicine in cardiology has ignited new possibilities for individually personalized, integrative, and patient-centric approaches to disease prevention and treatment, incorporating the standard clinical data with advanced "omics". These data help with the phenotypically adjudicated individualization of treatment. The major objective of this review was to compile the evolving clinically relevant tools of precision medicine that can help with the evidence-based precise individualized management of cardiac diseases with the highest DALY. The field of cardiology is evolving to provide targeted therapy, which is crafted as per the "omics", involving genomics, transcriptomics, epigenomics, proteomics, metabolomics, and microbiomics, for deep phenotyping. Research for individualizing therapy in heart diseases with the highest DALY has helped identify novel genes, biomarkers, proteins, and technologies to aid early diagnosis and treatment. Precision medicine has helped in targeted management, allowing early diagnosis, timely precise intervention, and exposure to minimal side effects. Despite these great impacts, overcoming the barriers to implementing precision medicine requires addressing the economic, cultural, technical, and socio-political issues. Precision medicine is proposed to be the future of cardiovascular medicine and holds the potential for a more efficient and personalized approach to the management of cardiovascular diseases, contrary to the standardized blanket approach.

From research cohorts to the patient - a role for "omics" in diagnostics and laboratory medicine?

Human pathologies are complex and might benefit from a more holistic diagnostic approach than currently practiced. Omics is a concept in biological research that aims to comprehensively characterize and quantify large numbers of biological molecules in complex samples, e.g., proteins (proteomics), low molecular weight molecules (metabolomics), glycans (glycomics) or amphiphilic molecules (lipidomics). Over the past decades, respective unbiased discovery approaches have been intensively applied to investigate functional physiological and pathophysiological relationships in various research study cohorts. In the context of clinical diagnostics, omics approaches seem to have potential in two main areas: (i) biomarker discovery i.e. identification of individual marker analytes for subsequent translation into diagnostics (as classical target analyses with conventional laboratory techniques), and (ii) the readout of complex, higher-dimensional signatures of diagnostic samples, in particular by means of spectrometric techniques in combination with biomathematical approaches of pattern recognition and artificial intelligence for diagnostic classification. Resulting diagnostic methods could potentially represent a disruptive paradigm shift away from current one-dimensional (i.e., single analyte marker based) laboratory diagnostics. The underlying hypothesis of omics approaches for diagnostics is that complex, multigenic pathologies can be more accurately diagnosed via the readout of "omics-type signatures" than with the current one-dimensional single marker diagnostic procedures. While this is indeed promising, one must realize that the clinical translation of high-dimensional analytical procedures into routine diagnostics brings completely new challenges with respect to long-term reproducibility and analytical standardization, data management, and quality assurance. In this article, the conceivable opportunities and challenges of omics-based laboratory diagnostics are discussed.

Artificial intelligence and thyroid disease management: considerations for thyroid function tests

Artificial intelligence (AI) is transforming healthcare and offers new tools in clinical research, personalized medicine, and medical diagnostics. Thyroid function tests represent an important asset for physicians in the diagnosis and monitoring of pathologies. Artificial intelligence tools can clearly assist physicians and specialists in laboratory medicine to optimize test prescription, tests interpretation, decision making, process optimization, and assay design. Our article is reviewing several of these aspects. As thyroid AI models rely on large data sets, which often requires distributed learning from multi-center contributions, this article also briefly discusses this issue.

A method for measuring the experimental resolution of laboratory assays (clinical biochemical, blood count, immunological, and qPCR) to evaluate analytical performance

Background

The measurement method for experimental resolution and related data to evaluate analytical performance is poorly explored in clinical research. We established a method to measure the experimental resolution of clinical tests, including biochemical tests, automatic hematology analyzer methods, immunoassays, chemical experiments, and qPCR, to evaluate their analytical performance.

Methods

Serially diluted samples in equal proportions were measured, and correlation analysis was performed between the relative concentration and the measured value. Results were accepted for p ≤ 0.01 of the correlation coefficient. The minimum concentration gradient (eg, 10%) was defined as the experimental resolution. For this method, the smaller the value, the higher the experimental resolution and the better the analytical performance.

Results

The experimental resolution of the most common biochemical indices reached 10%, with some even reaching 1%. The results of most counting experiments showed experimental resolution up to 10%, whereas the experimental resolution of the classical chemical assays reached 1%. Unexpectedly, the experimental resolution of more sensitive assays, such as immunoassays was only 25% when using the manual method and 10% for qPCR.

Conclusion

This study established a method for measuring the experimental resolution of laboratory assays and provides a new index for evaluating the reliability of methods in clinical laboratories.

Heart-type fatty acid binding protein is related to severity and established cardiac biomarkers of heart failure

Objectives

To determine concentrations of heart-type fatty acid-binding protein (HFABP) in patients with heart failure with reduced ejection fraction (HFrEF) and its potential value for prognostic assessment.

Methods

Circulating levels of HFABP were measured with an automated chemiluminescent immunoassay in 25 healthy volunteers and 60 HFrEF patients.

Results

Concentrations of HFABP were significantly increased in heart failure patients in comparison to healthy volunteers. HFABP levels were significantly correlated to New York Heart Association classes and to established biomarkers of cardiac dysfunction and remodeling (amino-terminal pro-B-type natriuretic peptide [NT-proBNP], fibroblast growth factor 23, and galectin-3). HFABP concentrations were also predictive of cardiovascular (CV) death and combination with NT-proBNP might be synergistic for risk assessment.

Conclusions

HFABP levels are increased in HFrEF patients, related to adverse CV outcomes, and might assist physicians for patient's management.

Benchmarking diagnostic laboratory performance: Survey results for Germany, Austria, and Switzerland

BACKGROUND AND AIMS

The need for patient safety through consistent diagnostic performance has increasingly been brought into focus during the last two decades. Around the globe operational efficiency of diagnostic laboratories plays a key role in satisfying this need, which has impressively been shown during the recent months of the SARS-CoV‑2 pandemic. On a global level, however, there has been a lack to collate and benchmark data for diagnostic laboratories. The goals of this study were to design and pilot a questionnaire addressing key aspects of diagnostic laboratory management.

METHODS

The questionnaire was designed using an iterative process and taking into consideration information that could be extracted from the literature, author experience and feedback from informal focus groups of laboratory professionals. The resulting tool consisted of 50 items, either relating to general information or more specifically addressing the topics of "operational performance", "integrated clinical care performance", and "financial sustainability". A limited number of laboratories were surveyed to be able to further improve the newly developed tool and motivate the global laboratory community to participate in further benchmarking activity.

RESULTS AND CONCLUSION

Altogether, 65 laboratories participated in the survey, 42 were hospital laboratories and 23 were commercial laboratories. Potential for further improvement and standardization became apparent across the board, e.g. use of IT for order management, auto-validation, or turn-around time (TAT) monitoring. Notably, a gap was identified regarding services provided to physicians, in particular "reflexive test suggestions", "proactive consultation on complex cases", and "diagnostic pathways guidance", which were only provided by about two thirds of laboratories. Concordantly, within-laboratory TAT (Lab TAT) was monitored by about 80% of respondents, while sample-to-result TAT, which is arguably the TAT most relevant to clinicians, was only monitored by 32% of respondents. Altogether, the need for stronger integration of the laboratory into the clinical care process became apparent and should be a main trajectory of future laboratory management.

Has the Flood Entered the Basement? A Systematic Literature Review about Machine Learning in Laboratory Medicine

This article presents a systematic literature review that expands and updates a previous review on the application of machine learning to laboratory medicine. We used Scopus and PubMed to collect, select and analyse the papers published from 2017 to the present in order to highlight the main studies that have applied machine learning techniques to haematochemical parameters and to review their diagnostic and prognostic performance. In doing so, we aim to address the question we asked three years ago about the potential of these techniques in laboratory medicine and the need to leverage a tool that was still under-utilised at that time.