Explainable AI: Bridging the Gap between Machine Learning Models and Human Understanding

Journal of Informatics Education and Research, Год журнала: 2024, Номер unknown

Опубликована: Янв. 1, 2024

Explainable AI (XAI) is one of the key game-changing features in machine learning models, which contribute to making them more transparent, regulated and usable different applications. In (the) investigation this paper, we consider four rows explanation methods—LIME, SHAP, Anchor, Decision Tree-based Explanation—in disentangling decision-making process black box models within fields. our experiments, use datasets that cover domains, for example, health, finance image classification, compare accuracy, fidelity, coverage, precision human satisfaction each method. Our work shows rule trees approach called (Decision explanation) mostly superior comparison other non-model-specific methods performing higher coverage regardless classifier. addition this, respondents who answered qualitative evaluation indicated they were very content with decision tree-based explanations these types are easy understandable. Furthermore, most famous sorts clarifications instinctive significant. The over discoveries stretch on utilize interpretable strategies facilitating hole between understanding thus advancing straightforwardness responsibility AI-driven decision-making.

Язык: Английский

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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

Diagnostics, Год журнала: 2024, Номер 14(11), С. 1103 - 1103

Опубликована: Май 26, 2024

Background: Artificial intelligence (AI) can radically change almost every aspect of the human experience. In medical field, there are numerous applications AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, fact being supported by exponential increase number publications which algorithms play an important role data analysis, pattern discovery, identification anomalies, therapeutic decision making. Furthermore, with technological development, have appeared new models machine learning (ML) deep (DP) that capable exploring various cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional many others. sense, present article aims provide general vision current state use cardiology. Results: We identified included subset 200 papers directly relevant research covering wide range applications. Thus, paper presents arithmology, clinical or emergency procedures summarized manner. Recent studies from highly scientific literature demonstrate feasibility advantages using different branches Conclusions: The integration cardiology offers promising perspectives for increasing accuracy decreasing error rate efficiency practice. From predicting risk sudden death ability respond cardiac resynchronization therapy diagnosis pulmonary embolism early detection valvular diseases, shown their potential mitigate feasible solutions. At same limits imposed small samples studied highlighted alongside challenges presented ethical implementation; these relate legal implications regarding responsibility making processes, ensuring patient confidentiality security. All constitute future directions will allow

Язык: Английский

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Prognostic role of cardiovascular magnetic resonance in Takotsubo syndrome: A systematic review

European Journal of Radiology, Год журнала: 2024, Номер 177, С. 111576 - 111576

Опубликована: Июнь 15, 2024

Takotsubo syndrome (TS) is characterized by transient myocardial dysfunction with outcomes ranging from favorable to life-threatening. Cardiovascular magnetic resonance (CMR) has emerged as an essential tool in its diagnosis and management consistently recommended current guidelines the diagnostic work-up. However, prognostic value of CMR patients TS remains undetermined. The aim this study was assess managing TS.

Язык: Английский

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The Role of Artificial Intelligence and Machine Learning in Cardiovascular Imaging and Diagnosis

Cureus, Год журнала: 2024, Номер unknown

Опубликована: Сен. 2, 2024

Cardiovascular diseases remain the leading cause of global mortality, underscoring critical need for accurate and timely diagnosis. This narrative review examines current applications future potential artificial intelligence (AI) machine learning (ML) in cardiovascular imaging. We discuss integration these technologies across various imaging modalities, including echocardiography, computed tomography, magnetic resonance imaging, nuclear techniques. The explores AI-assisted diagnosis key areas such as coronary artery disease detection, valve disorders assessment, cardiomyopathy classification, arrhythmia prediction events. AI demonstrates promise improving diagnostic accuracy, efficiency, personalized care. However, significant challenges persist, data quality standardization, model interpretability, regulatory considerations, clinical workflow integration. also address limitations ethical implications their implementation practice. Future directions point towards advanced architectures, multimodal integration, precision medicine population health management. emphasizes ongoing collaboration between clinicians, scientists, policymakers to realize full while ensuring equitable implementation. As field continues evolve, addressing will be crucial successful into care, potentially revolutionizing capabilities patient outcomes.

Язык: Английский

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Unraveling the Genetic Heartbeat: Decoding Cardiac Involvement in Duchenne Muscular Dystrophy

Biomedicines, Год журнала: 2025, Номер 13(1), С. 102 - 102

Опубликована: Янв. 4, 2025

Cardiomyopathy represents the most important life-limiting condition of Duchenne muscular dystrophy (DMD) patients after age 20. Genetic alterations in DMD gene result absence functional dystrophin protein, leading to skeletal/cardiac muscle impairment. The incidence is one 5000 live male births. Identifying genetic background, addition disease-causing variants, unmet needs understanding cardiac disease's pathogenetic mechanisms and its prognostic implications. clinical scenario made even more intricate by difficulty predicting onset progression cardiomyopathy, as no clear genotype/phenotype correspondence has been found thus far. evaluation genes involved primary cardiomyopathies could explore hypothesis that changes cytoskeletal sarcomeric protein function are modulators ventricular dysfunction patients. In last decade, with advent next-generation sequencing (NGS) technology, many modifiers have identified. Assessing origin phenotypic variability disease both cardiomyopathy would be extremely helpful managing these This review article aims spotlight background associated toward a predictive personalized model care.

Язык: Английский

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Interoception, cardiac health, and heart failure: The potential for artificial intelligence (AI)—driven diagnosis and treatment

Physiological Reports, Год журнала: 2025, Номер 13(1)

Опубликована: Янв. 1, 2025

Abstract “I see, I forget, read aloud, remember, and when do purposefully by writing it, not forget it.” This phenomenon is known as “interoception” refers to the sensing interpretation of internal body signals, allowing brain communicate with various systems. Dysfunction in interoception associated cardiovascular disorders. We delve into concept its impact on heart failure (HF) reviewing exploring neural mechanisms underlying interoceptive processing. Furthermore, we review potential artificial intelligence (AI) diagnosis, biomarker development, HF treatment. In context HF, AI algorithms can analyze interpret complex data, providing valuable insights for diagnosis These identify patterns disease markers that contribute early detection enabling timely intervention improved outcomes. biomarkers hold significant improving precision/efficacy HF. Additionally, AI‐powered technologies offer promising avenues By leveraging patient personalize therapeutic interventions. AI‐driven such remote monitoring devices wearable sensors enable patients' health. harnessing power AI, should aim advance treatment strategies explores diagnosing, developing biomarkers, managing

Язык: Английский

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Identifying Hypertrophic or Dilated Cardiomyopathy: Development and Validation of a Fine-Tuned ResNet50 Model Based on Electrocardiogram Image

Bioengineering, Год журнала: 2025, Номер 12(3), С. 250 - 250

Опубликована: Фев. 28, 2025

There is no established detecting tool for hypertrophic cardiomyopathy (HCM) and dilated (DCM). This study aimed to develop a deep-learning-based model identifying HCM DCM using standard 12-lead electrocardiogram (ECG) images. We obtained cohort of patients with (171 ECG images) or (364 images), confirmed by cardiovascular magnetic resonance (CMR) examinations, who underwent both CMR within 30 days at our institution. Age- sex-matched healthy controls (2314 were selected from Health Check Center. A total 2849 images processed via fine-tuned ResNet50 architecture, stratified five-fold cross-validation training, validation, testing. The proposed demonstrated strong performance in distinguishing DCM, achieving an area under the receiver operating curve (AUROC) 0.996 precision–recall (AUPRC) 0.940. For detection HCM, also achieved AUROC 0.980 AUPRC 0.953, respectively. prospectively exhibited stability temporal validation. Furthermore, representative Gradient-weighted Class Activation Mapping (Grad-CAM) technique analysis showed regions corresponding anterior anteroseptal leads most important areas prediction DCM. temporally validated shows promise inexpensively detect individuals

Язык: Английский

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Cardiovascular Magnetic Resonance Imaging of Takotsubo Syndrome: Evolving Diagnostic and Prognostic Perspectives

Echocardiography, Год журнала: 2024, Номер 41(10)

Опубликована: Окт. 1, 2024

Takotsubo syndrome (TS) is a temporary form of left ventricular (LV) dysfunction characterized by distinct pattern LV impairment, often triggered physical or emotional stressful event. Historically, TS was considered benign condition due to its prompt restoration myocardial function and generally excellent outcomes. However, recent studies have shown that complications similar those seen after infarction can occur, necessitating careful monitoring these patients. Among noninvasive imaging techniques, cardiovascular magnetic resonance (CMR) becoming increasingly important in evaluating patients with TS. CMR offers unique ability noninvasively assess tissue characteristics, allowing for detecting the typical features TS, such as specific wall motion abnormalities edema. Beyond well-established diagnostic utility clinical management has also proven valuable prognosis risk stratification Advances CMR, including strain parametric mapping expanded role diagnosis, prognosis, follow-up This review aims provide comprehensive overview potential applications prognostic evaluation It explores emerging use novel biomarkers may enhance improve accuracy, contribute overall

Язык: Английский

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Artificial Intelligence Advancements in Cardiomyopathies: Implications for Diagnosis and Management of Arrhythmogenic Cardiomyopathy

Current Heart Failure Reports, Год журнала: 2024, Номер 22(1)

Опубликована: Дек. 11, 2024

Язык: Английский

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