Machine Learning and Deep Learning based AI Tools for Development of Diagnostic Tools

Lepakshi, Vijayalakshmi A.

doi:10.1016/b978-0-323-91172-6.00011-x

Cited by 13 publications

(5 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, it is easier for AI models to identify and learn discriminative features when distinguishing between normal and abnormal BMD. In three-label classification, the model must discern finer distinctions, which can be more challenging and may require a larger, more complex model [107]. It is crucial to stress that the choice between binary (two-label) and ternary (three-label) classification should be guided by the specific clinical or research objectives.…”

Section: Search Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence Applications for Osteoporosis Classification Using Computed Tomography

Ong,

Liu,

Makmur

et al. 2023

Bioengineering

View full text Add to dashboard Cite

Osteoporosis, marked by low bone mineral density (BMD) and a high fracture risk, is a major health issue. Recent progress in medical imaging, especially CT scans, offers new ways of diagnosing and assessing osteoporosis. This review examines the use of AI analysis of CT scans to stratify BMD and diagnose osteoporosis. By summarizing the relevant studies, we aimed to assess the effectiveness, constraints, and potential impact of AI-based osteoporosis classification (severity) via CT. A systematic search of electronic databases (PubMed, MEDLINE, Web of Science, ClinicalTrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 39 articles were retrieved from the databases, and the key findings were compiled and summarized, including the regions analyzed, the type of CT imaging, and their efficacy in predicting BMD compared with conventional DXA studies. Important considerations and limitations are also discussed. The overall reported accuracy, sensitivity, and specificity of AI in classifying osteoporosis using CT images ranged from 61.8% to 99.4%, 41.0% to 100.0%, and 31.0% to 100.0% respectively, with areas under the curve (AUCs) ranging from 0.582 to 0.994. While additional research is necessary to validate the clinical efficacy and reproducibility of these AI tools before incorporating them into routine clinical practice, these studies demonstrate the promising potential of using CT to opportunistically predict and classify osteoporosis without the need for DEXA.

show abstract

Section: Search Resultsmentioning

confidence: 99%

“…Another reported method for determining BMD from CT images involves applying HU thresholds for osteoporosis screening [48,61,[106][107][108]. However, this method lacks reliability due to HU sensitivity to X-ray energy, beam hardening artifacts, positioning, and hardware-related variations, including different scan models and protocols [109][110][111].…”

mentioning

confidence: 99%

Artificial Intelligence Applications for Osteoporosis Classification Using Computed Tomography

Ong,

Liu,

Makmur

et al. 2023

Bioengineering

View full text Add to dashboard Cite

show abstract

“…Within healthcare, deep learning powers advanced diagnostic tools, including AI-assisted imaging analysis and NLP applications. Large language models (LLMs), such as Generative Pretrained Transformer (GPT), are a form of deep learning model that excels in understanding and generating human language [20,21]. Deep learning and LLMs can interpret medical literature, patient records and other textual data, aiding in the identification of relevant information for prescribing decisions and polypharmacy management.…”

Section: Introductionmentioning

confidence: 99%

Potential Applications of Artificial Intelligence (AI) in Managing Polypharmacy in Saudi Arabia: A Narrative Review

Alsanosi,

Padmanabhan

2024

Healthcare

View full text Add to dashboard Cite

Prescribing medications is a fundamental practice in the management of illnesses that necessitates in-depth knowledge of clinical pharmacology. Polypharmacy, or the concurrent use of multiple medications by individuals with complex health conditions, poses significant challenges, including an increased risk of drug interactions and adverse reactions. The Saudi Vision 2030 prioritises enhancing healthcare quality and safety, including addressing polypharmacy. Artificial intelligence (AI) offers promising tools to optimise medication plans, predict adverse drug reactions and ensure drug safety. This review explores AI’s potential to revolutionise polypharmacy management in Saudi Arabia, highlighting practical applications, challenges and the path forward for the integration of AI solutions into healthcare practices.

show abstract

“…Amongst novel approaches, machine learning has been increasingly used in the latest decade for various applications in medicine [22]. Machine learning is a branch of artificial intelligence that deals with teaching computers how to learn from and make predictions or judgments based on data via the use of statistical models and algorithms [23,24]. It focuses on creating systems that, through experience, may naturally perform better on a given task without having to be specifically designed to do so [25].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Intersection of Schizophrenia, Machine Learning, and Genomics: Scoping Review (Preprint)

Hudon,

Beaudoin,

Phraxayavong

et al. 2024

Preprint

View full text Add to dashboard Cite

BACKGROUND An increasing body of literature highlights the integration of machine learning with genomic data in psychiatry, particularly for complex mental health disorders such as schizophrenia. These advanced techniques offer promising potential for uncovering various facets of these disorders. A comprehensive review of the current applications of machine learning in conjunction with genomic data within this context can significantly enhance our understanding of the current state of research and its future directions. OBJECTIVE The objective of this study is to conduct a systematic scoping review of the use of machine learning algorithms with genomic data in the field of schizophrenia. METHODS A systematic, scoping review, search was performed in the electronic databases of Medline, Web of Science, PsycNet (PsycINFO), and Google Scholar from 2013 to 2024. Studies at the intersection of schizophrenia, genomic data, and machine learning were evaluated. RESULTS The literature search identified 2437 eligible articles after removing duplicates. Following abstract screening, 143 full-text articles were assessed, and 121 were subsequently excluded. Therefore, 21 studies were thoroughly assessed. Various machine learning algorithms were used in the identified studies, with support vector machines being the most common. The studies notably used genomic data to predict schizophrenia, identifying schizophrenia features, discovering drugs, classifying schizophrenia amongst other mental health disorders, and predicting the quality-of-life of patients. CONCLUSIONS Several high-quality studies were identified. Yet, the application of machine learning with genomic data in the context of schizophrenia remains limited. Future research is essential to further evaluate the portability of these models and to explore their potential clinical applications.

show abstract

Machine Learning and Deep Learning based AI Tools for Development of Diagnostic Tools

Cited by 13 publications

References 41 publications

Artificial Intelligence Applications for Osteoporosis Classification Using Computed Tomography

Artificial Intelligence Applications for Osteoporosis Classification Using Computed Tomography

Potential Applications of Artificial Intelligence (AI) in Managing Polypharmacy in Saudi Arabia: A Narrative Review

Exploring the Intersection of Schizophrenia, Machine Learning, and Genomics: Scoping Review (Preprint)

Contact Info

Product

Resources

About