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International Journal of Medical... Jun 2024The surge in emergency head CT imaging and artificial intelligence (AI) advancements, especially deep learning (DL) and convolutional neural networks (CNN), have... (Review)
Review
BACKGROUND
The surge in emergency head CT imaging and artificial intelligence (AI) advancements, especially deep learning (DL) and convolutional neural networks (CNN), have accelerated the development of computer-aided diagnosis (CADx) for emergency imaging. External validation assesses model generalizability, providing preliminary evidence of clinical potential.
OBJECTIVES
This study systematically reviews externally validated CNN-CADx models for emergency head CT scans, critically appraises diagnostic test accuracy (DTA), and assesses adherence to reporting guidelines.
METHODS
Studies comparing CNN-CADx model performance to reference standard were eligible. The review was registered in PROSPERO (CRD42023411641) and conducted on Medline, Embase, EBM-Reviews and Web of Science following PRISMA-DTA guideline. DTA reporting were systematically extracted and appraised using standardised checklists (STARD, CHARMS, CLAIM, TRIPOD, PROBAST, QUADAS-2).
RESULTS
Six of 5636 identified studies were eligible. The common target condition was intracranial haemorrhage (ICH), and intended workflow roles auxiliary to experts. Due to methodological and clinical between-study variation, meta-analysis was inappropriate. The scan-level sensitivity exceeded 90 % in 5/6 studies, while specificities ranged from 58,0-97,7 %. The SROC 95 % predictive region was markedly broader than the confidence region, ranging above 50 % sensitivity and 20 % specificity. All studies had unclear or high risk of bias and concern for applicability (QUADAS-2, PROBAST), and reporting adherence was below 50 % in 20 of 32 TRIPOD items.
CONCLUSION
0.01 % of identified studies met the eligibility criteria. The evidence on the DTA of CNN-CADx models for emergency head CT scans remains limited in the scope of this review, as the reviewed studies were scarce, inapt for meta-analysis and undermined by inadequate methodological conduct and reporting. Properly conducted, external validation remains preliminary for evaluating the clinical potential of AI-CADx models, but prospective and pragmatic clinical validation in comparative trials remains most crucial. In conclusion, future AI-CADx research processes should be methodologically standardized and reported in a clinically meaningful way to avoid research waste.
PubMed: 38901270
DOI: 10.1016/j.ijmedinf.2024.105523 -
Journal of Magnetic Resonance Imaging :... Jun 2024Distinguishing high-grade gliomas (HGGs) from brain metastases (BMs) using perfusion-weighted imaging (PWI) remains challenging. PWI offers quantitative measurements of...
Differentiation Between High-Grade Glioma and Brain Metastasis Using Cerebral Perfusion-Related Parameters (Cerebral Blood Volume and Cerebral Blood Flow): A Systematic Review and Meta-Analysis of Perfusion-weighted MRI Techniques.
BACKGROUND
Distinguishing high-grade gliomas (HGGs) from brain metastases (BMs) using perfusion-weighted imaging (PWI) remains challenging. PWI offers quantitative measurements of cerebral blood flow (CBF) and cerebral blood volume (CBV), but optimal PWI parameters for differentiation are unclear.
PURPOSE
To compare CBF and CBV derived from PWIs in HGGs and BMs, and to identify the most effective PWI parameters and techniques for differentiation.
STUDY TYPE
Systematic review and meta-analysis.
POPULATION
Twenty-four studies compared CBF and CBV between HGGs (n = 704) and BMs (n = 488).
FIELD STRENGTH/SEQUENCE
Arterial spin labeling (ASL), dynamic susceptibility contrast (DSC), dynamic contrast-enhanced (DCE), and dynamic susceptibility contrast-enhanced (DSCE) sequences at 1.5 T and 3.0 T.
ASSESSMENT
Following the PRISMA guidelines, four major databases were searched from 2000 to 2024 for studies evaluating CBF or CBV using PWI in HGGs and BMs.
STATISTICAL TESTS
Standardized mean difference (SMD) with 95% CIs was used. Risk of bias (ROB) and publication bias were assessed, and I statistic was used to assess statistical heterogeneity. A P-value<0.05 was considered significant.
RESULTS
HGGs showed a significant modest increase in CBF (SMD = 0.37, 95% CI: 0.05-0.69) and CBV (SMD = 0.26, 95% CI: 0.01-0.51) compared with BMs. Subgroup analysis based on region, sequence, ROB, and field strength for CBF (HGGs: 375 and BMs: 222) and CBV (HGGs: 493 and BMs: 378) values were conducted. ASL showed a considerable moderate increase (50% overlapping CI) in CBF for HGGs compared with BMs. However, no significant difference was found between ASL and DSC (P = 0.08).
DATA CONCLUSION
ASL-derived CBF may be more useful than DSC-derived CBF in differentiating HGGs from BMs. This suggests that ASL may be used as an alternative to DSC when contrast medium is contraindicated or when intravenous injection is not feasible.
TECHNICAL EFFICACY
Stage 2.
PubMed: 38899965
DOI: 10.1002/jmri.29473 -
Sensors (Basel, Switzerland) May 2024Emotion recognition has become increasingly important in the field of Deep Learning (DL) and computer vision due to its broad applicability by using human-computer... (Review)
Review
Emotion recognition has become increasingly important in the field of Deep Learning (DL) and computer vision due to its broad applicability by using human-computer interaction (HCI) in areas such as psychology, healthcare, and entertainment. In this paper, we conduct a systematic review of facial and pose emotion recognition using DL and computer vision, analyzing and evaluating 77 papers from different sources under Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our review covers several topics, including the scope and purpose of the studies, the methods employed, and the used datasets. The scope of this work is to conduct a systematic review of facial and pose emotion recognition using DL methods and computer vision. The studies were categorized based on a proposed taxonomy that describes the type of expressions used for emotion detection, the testing environment, the currently relevant DL methods, and the datasets used. The taxonomy of methods in our review includes Convolutional Neural Network (CNN), Faster Region-based Convolutional Neural Network (R-CNN), Vision Transformer (ViT), and "Other NNs", which are the most commonly used models in the analyzed studies, indicating their trendiness in the field. Hybrid and augmented models are not explicitly categorized within this taxonomy, but they are still important to the field. This review offers an understanding of state-of-the-art computer vision algorithms and datasets for emotion recognition through facial expressions and body poses, allowing researchers to understand its fundamental components and trends.
Topics: Deep Learning; Humans; Emotions; Neural Networks, Computer; Facial Expression
PubMed: 38894274
DOI: 10.3390/s24113484 -
Diagnostics (Basel, Switzerland) May 2024Automatic age estimation has garnered significant interest among researchers because of its potential practical uses. The current systematic review was undertaken to... (Review)
Review
Automatic age estimation has garnered significant interest among researchers because of its potential practical uses. The current systematic review was undertaken to critically appraise developments and performance of AI models designed for automated estimation using dento-maxillofacial radiographic images. In order to ensure consistency in their approach, the researchers followed the diagnostic test accuracy guidelines outlined in PRISMA-DTA for this systematic review. They conducted an electronic search across various databases such as PubMed, Scopus, Embase, Cochrane, Web of Science, Google Scholar, and the Saudi Digital Library to identify relevant articles published between the years 2000 and 2024. A total of 26 articles that satisfied the inclusion criteria were subjected to a risk of bias assessment using QUADAS-2, which revealed a flawless risk of bias in both arms for the patient-selection domain. Additionally, the certainty of evidence was evaluated using the GRADE approach. AI technology has primarily been utilized for automated age estimation through tooth development stages, tooth and bone parameters, bone age measurements, and pulp-tooth ratio. The AI models employed in the studies achieved a remarkably high precision of 99.05% and accuracy of 99.98% in the age estimation for models using tooth development stages and bone age measurements, respectively. The application of AI as an additional diagnostic tool within the realm of age estimation demonstrates significant promise.
PubMed: 38893606
DOI: 10.3390/diagnostics14111079 -
Journal of Clinical Medicine May 2024Large language models (LLMs) represent a recent advancement in artificial intelligence with medical applications across various healthcare domains. The objective of... (Review)
Review
Large language models (LLMs) represent a recent advancement in artificial intelligence with medical applications across various healthcare domains. The objective of this review is to highlight how LLMs can be utilized by clinicians and surgeons in their everyday practice. A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Six databases were searched to identify relevant articles. Eligibility criteria emphasized articles focused primarily on clinical and surgical applications of LLMs. The literature search yielded 333 results, with 34 meeting eligibility criteria. All articles were from 2023. There were 14 original research articles, four letters, one interview, and 15 review articles. These articles covered a wide variety of medical specialties, including various surgical subspecialties. : LLMs have the potential to enhance healthcare delivery. In clinical settings, LLMs can assist in diagnosis, treatment guidance, patient triage, physician knowledge augmentation, and administrative tasks. In surgical settings, LLMs can assist surgeons with documentation, surgical planning, and intraoperative guidance. However, addressing their limitations and concerns, particularly those related to accuracy and biases, is crucial. LLMs should be viewed as tools to complement, not replace, the expertise of healthcare professionals.
PubMed: 38892752
DOI: 10.3390/jcm13113041 -
Journal of Affective Disorders Jun 2024This study underscores the importance of exploring AI's creative applications in treating depressive disorders to revolutionize mental health care. Through innovative... (Review)
Review
OBJECTIVES
This study underscores the importance of exploring AI's creative applications in treating depressive disorders to revolutionize mental health care. Through innovative integration of AI technologies, the research confirms their positive effects on preventing, diagnosing, and treating depression. The systematic review establishes an evidence base for AI in depression management, offering directions for effective interventions.
METHODS
This systematic literature review investigates the effectiveness of AI in depression management by analyzing studies from January 1, 2017, to May 31, 2022. Utilizing search engines like IEEE Xplore, PubMed, and Web of Science, the review focused on keywords such as Depression/Mental Health, Machine Learning/Artificial Intelligence, and Prediction/Diagnosis. The analysis of 95 documents involved classification based on use, data type, and algorithm type.
RESULTS
The study revealed that AI in depression management excelled in accuracy, particularly in monitoring and prediction. Biomarker-derived data demonstrated the highest accuracy, with the CNN algorithm proving most effective. The findings affirm the therapeutic benefits of AI, including treatment, detection, and disease prediction, highlighting its potential in analyzing monitored data for depression management.
LIMITATIONS
This study exclusively examined the application of AI in individuals with depressive disorders. Interpretation should be cautious due to the limited scope of subjects to this specific population.
CONCLUSIONS
To introduce digital healthcare and therapies for ongoing depression management, it's crucial to present empirical evidence on the medical fee payment system, safety, and efficacy. These findings support enhanced medical accessibility through digital healthcare, offering personalized disease management for patients seeking non-face-to-face treatment.
PubMed: 38889858
DOI: 10.1016/j.jad.2024.06.035 -
BMC Medicine Jun 2024The global population of adults aged 60 and above surpassed 1 billion in 2020, constituting 13.5% of the global populace. Projections indicate a rise to 2.1 billion by... (Meta-Analysis)
Meta-Analysis
BACKGROUND
The global population of adults aged 60 and above surpassed 1 billion in 2020, constituting 13.5% of the global populace. Projections indicate a rise to 2.1 billion by 2050. While Hospital-at-Home (HaH) programs have emerged as a promising alternative to traditional routine hospital care, showing initial benefits in metrics such as lower mortality rates, reduced readmission rates, shorter treatment durations, and improved mental and functional status among older individuals, the robustness and magnitude of these effects relative to conventional hospital settings call for further validation through a comprehensive meta-analysis.
METHODS
A comprehensive literature search was executed during April-June 2023, across PubMed, MEDLINE, Embase, Web of Science, and Cumulative Index of Nursing and Allied Health Literature (CINAHL) to include both RCT and non-RCT HaH studies. Statistical analyses were conducted using Review Manager (version 5.4), with Forest plots and I statistics employed to detect inter-study heterogeneity. For I > 50%, indicative of substantial heterogeneity among the included studies, we employed the random-effects model to account for the variability. For I ≤ 50%, we used the fixed effects model. Subgroup analyses were conducted in patients with different health conditions, including cancer, acute medical conditions, chronic medical conditions, orthopedic issues, and medically complex conditions.
RESULTS
Fifteen trials were included in this systematic review, including 7 RCTs and 8 non-RCTs. Outcome measures include mortality, readmission rates, treatment duration, functional status (measured by the Barthel index), and mental status (measured by MMSE). Results suggest that early discharge HaH is linked to decreased mortality, albeit supported by low-certainty evidence across 13 studies. It also shortens the length of treatment, corroborated by seven trials. However, its impact on readmission rates and mental status remains inconclusive, supported by nine and two trials respectively. Functional status, gauged by the Barthel index, indicated potential decline with early discharge HaH, according to four trials. Subgroup analyses reveal similar trends.
CONCLUSIONS
While early discharge HaH shows promise in specific metrics like mortality and treatment duration, its utility is ambiguous in the contexts of readmission, mental status, and functional status, necessitating cautious interpretation of findings.
Topics: Humans; Patient Discharge; Aged; Patient Readmission; Home Care Services, Hospital-Based; Aged, 80 and over
PubMed: 38886793
DOI: 10.1186/s12916-024-03463-3 -
Survey of Ophthalmology Jun 2024Diabetic retinopathy (DR) poses a significant challenge in diabetes management, with its progression often asymptomatic until advanced stages. This underscores the... (Review)
Review
Diabetic retinopathy (DR) poses a significant challenge in diabetes management, with its progression often asymptomatic until advanced stages. This underscores the urgent need for cost-effective and reliable screening methods. Consequently, the integration of Artificial Intelligence tools presents a promising avenue to address this need effectively. We provide an overview of the current state of the art results and techniques in DR screening using AI, while also identifying gaps in research for future exploration. By synthesizing existing database and pinpointing areas requiring further investigation, this paper seeks to guide the direction of future research in the field of automatic diabetic retinopathy screening. There has been a continuous rise in the number of articles detailing Deep Learning methods designed for the automatic screening of Diabetic Retinopathy especially by the year 2021. Researchers utilized various databases, with a primary focus on the IDRiD dataset. This dataset comprises color fundus images captured at an ophthalmological clinic situated in India. It comprises 516 images that depict various stages of diabetic retinopathy and diabetic macular edema. Each of the chosen papers concentrates on various DR signs. Nevertheless, a significant portion of the authors primarily focused on detecting exudates, which remains insufficient to assess the overall presence of this disease. Various AI methods have been employed to identify DR signs. Among the chosen papers, 4.7% utilized detection methods, 46.5% employed classification techniques, 41.9% relied on segmentation, and 7% opted for a combination of classification and segmentation. Metrics calculated from 80% of the articles employing preprocessing techniques demonstrated the significant benefits of this approach in enhancing results quality. In addition, multiple Deep Learning techniques, starting by classification, detection then segmentation. Researchers used mostly YOLO for detection, ViT for classification and U-Net for segmentation. Another perspective on the evolving landscape of AI models for diabetic retinopathy screening lies in the increasing adoption of Convolutional Neural Networks for classification tasks and U-Net architectures for segmentation purposes;However, there is a growing realization within the research community that these techniques, while powerful individually, can be even more effective when integrated. This integration holds promise for not only diagnosing DR but also accurately classifying its different stages, thereby enabling more tailored treatment strategies. Despite this potential, the development of AI models for DR screening is fraught with challenges. Chief among these is the difficulty in obtaining high-quality, labeled data necessary for training models to perform effectively. This scarcity of data poses significant barriers to achieving robust performance and can hinder progress in developing accurate screening systems. Moreover, managing the complexity of these models, particularly deep neural networks, presents its own set of challenges. Additionally, interpreting the outputs of these models and ensuring their reliability in real-world clinical settings remain ongoing concerns. Furthermore, the iterative process of training and adapting these models to specific datasets can be time-consuming and resource-intensive. These challenges underscore the multifaceted nature of developing effective AI models for DR screening. Addressing these obstacles requires concerted efforts from researchers, clinicians, and technologists to innovate new approaches and overcome existing limitations. By doing so, a full potential of AI may transform DR screening and improve patient outcomes.
PubMed: 38885761
DOI: 10.1016/j.survophthal.2024.05.008 -
Frontiers in Cardiovascular Medicine 2024In recent years, the use of artificial intelligence (AI) models to generate individualised risk assessments and predict patient outcomes post-Transcatheter Aortic Valve...
OBJECTIVES
In recent years, the use of artificial intelligence (AI) models to generate individualised risk assessments and predict patient outcomes post-Transcatheter Aortic Valve Implantation (TAVI) has been a topic of increasing relevance in literature. This study aims to evaluate the predictive accuracy of AI algorithms in forecasting post-TAVI mortality as compared to traditional risk scores.
METHODS
Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses for Systematic Reviews (PRISMA) standard, a systematic review was carried out. We searched four databases in total-PubMed, Medline, Embase, and Cochrane-from 19 June 2023-24 June, 2023.
RESULTS
From 2,239 identified records, 1,504 duplicates were removed, 735 manuscripts were screened, and 10 studies were included in our review. Our pooled analysis of 5 studies and 9,398 patients revealed a significantly higher mean area under curve (AUC) associated with AI mortality predictions than traditional score predictions (MD: -0.16, CI: -0.22 to -0.10, < 0.00001). Subgroup analyses of 30-day mortality (MD: -0.08, CI: -0.13 to -0.03, = 0.001) and 1-year mortality (MD: -0.18, CI: -0.27 to -0.10, < 0.0001) also showed significantly higher mean AUC with AI predictions than traditional score predictions. Pooled mean AUC of all 10 studies and 22,933 patients was 0.79 [0.73, 0.85].
CONCLUSION
AI models have a higher predictive accuracy as compared to traditional risk scores in predicting post-TAVI mortality. Overall, this review demonstrates the potential of AI in achieving personalised risk assessment in TAVI patients.
REGISTRATION AND PROTOCOL
This systematic review and meta-analysis was registered under the International Prospective Register of Systematic Reviews (PROSPERO), under the registration name "All-Cause Mortality in Transcatheter Aortic Valve Replacement Assessed by Artificial Intelligence" and registration number CRD42023437705. A review protocol was not prepared. There were no amendments to the information provided at registration.
SYSTEMATIC REVIEW REGISTRATION
https://www.crd.york.ac.uk/, PROSPERO (CRD42023437705).
PubMed: 38883982
DOI: 10.3389/fcvm.2024.1343210 -
Heliyon Jun 2024Sustainable smart ecotourism, utilizing smart technologies like smartphones, artificial intelligence (AI), and the Internet of Things (IoT), aims to minimize harm to...
Sustainable smart ecotourism, utilizing smart technologies like smartphones, artificial intelligence (AI), and the Internet of Things (IoT), aims to minimize harm to natural and cultural ecosystems, promoting education and environmental conservation. This review aims to examine the concept of sustainable smart ecotourism, analyzing existing literature to gain insights into the significance, components, challenges, and contributions to sustainable development on a global scale. A systematic review was conducted to evaluate sustainable smart ecotourism using PRISMA guidelines. The review focused on scholarly, peer-reviewed studies from developing countries, using databases like ScienceDirect, Jstor, Taylor & Francis, and IEEE. The study used Joanna Briggs Institute and Cochrane Risk of Bias tools to assess study quality. Thematic analysis techniques were used to extract and synthesize data, identifying patterns and trends relevant to smart ecotourism sustainability. Dual analyst verification ensured data integrity and reliability. After conducting a thorough quality evaluation using the Joanna Briggs Institute Checklist and Cochrane Risk of Bias Tool, we identified 29 studies of exceptional quality from an original pool of 9583 records. The use of thematic analysis sheds light on the diverse and important role of the IoT in promoting sustainable ecotourism. This study uncovered both the obstacles and possibilities associated with this technology. The findings provide important insights into the worldwide implementation of smart ecotourism techniques and highlight the significant impact of technology in promoting sustainable tourism models. Smart ecotourism involves multiple stakeholders to enhance environmental impact. Key characteristics include dynamic interactions, co-creation of value, sustainable development, resource sharing, and innovation services. Technology like IoT is crucial for sustainable tourism management. Collaboration with governments, local stakeholders, and organizations is recommended for sustainable policies. As a result of this study, sustainable ecotourism policies result from a collaborative effort between local communities, government agencies, and practitioners in the industry. Smart technologies, including AR/VR and AI, have the potential to enhance operational efficiency while reducing environmental concerns. Ecotourism, partnerships, and education are key to successful implementation and capacity building.
PubMed: 38882334
DOI: 10.1016/j.heliyon.2024.e31996