scholarly journals The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review

2020 ◽  
Vol 49 (1) ◽  
pp. 20190107 ◽  
Author(s):  
Kuofeng Hung ◽  
Carla Montalvao ◽  
Ray Tanaka ◽  
Taisuke Kawai ◽  
Michael M. Bornstein
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Clinical Practice ◽  
Diagnostic Performance ◽  
Clinical Applications ◽  
Quality Analysis ◽  
Maxillofacial Radiology ◽  
Diagnosis Of Osteoporosis ◽  
Electronic Search ◽  
And Performance

Objectives: To investigate the current clinical applications and diagnostic performance of artificial intelligence (AI) in dental and maxillofacial radiology (DMFR). Methods: Studies using applications related to DMFR to develop or implement AI models were sought by searching five electronic databases and four selected core journals in the field of DMFR. The customized assessment criteria based on QUADAS-2 were adapted for quality analysis of the studies included. Results: The initial electronic search yielded 1862 titles, and 50 studies were eventually included. Most studies focused on AI applications for an automated localization of cephalometric landmarks, diagnosis of osteoporosis, classification/segmentation of maxillofacial cysts and/or tumors, and identification of periodontitis/periapical disease. The performance of AI models varies among different algorithms. Conclusion: The AI models proposed in the studies included exhibited wide clinical applications in DMFR. Nevertheless, it is still necessary to further verify the reliability and applicability of the AI models prior to transferring these models into clinical practice.

scholarly journals A systematic review and quality analysis of pediatric traumatic brain injury clinical practice guidelines

PLoS ONE ◽  
2018 ◽  
Vol 13 (8) ◽  
pp. e0201550 ◽  
Author(s):  
Roselyn Appenteng ◽  
Taylor Nelp ◽  
Jihad Abdelgadir ◽  
Nelly Weledji ◽  
Michael Haglund ◽  
...  
Keyword(s):  
Systematic Review ◽  
Traumatic Brain Injury ◽  
Clinical Practice ◽  
Brain Injury ◽  
Clinical Practice Guidelines ◽  
Practice Guidelines ◽  
Quality Analysis ◽  
Pediatric Traumatic Brain Injury

scholarly journals Diagnostic Performance of Artificial Intelligence-Centred Systems in the Diagnosis and Postoperative Surveillance of Upper Gastrointestinal Malignancies Using Computed Tomography Imaging: A Systematic Review and Meta-Analysis of Diagnostic Accuracy

2021 ◽  
Author(s):  
Swathikan Chidambaram ◽  
Viknesh Sounderajah ◽  
Nick Maynard ◽  
Sheraz R. Markar
Keyword(s):  
Artificial Intelligence ◽  
Gastric Cancer ◽  
Systematic Review ◽  
Esophageal Cancer ◽  
Diagnostic Accuracy ◽  
Diagnostic Performance ◽  
Meta Analysis ◽  
Diagnostic Methods ◽  
Gastrointestinal Malignancies ◽  
Upper Gastrointestinal

Abstract Background Upper gastrointestinal cancers are aggressive malignancies with poor prognosis, even following multimodality therapy. As such, they require timely and accurate diagnostic and surveillance strategies; however, such radiological workflows necessitate considerable expertise and resource to maintain. In order to lessen the workload upon already stretched health systems, there has been increasing focus on the development and use of artificial intelligence (AI)-centred diagnostic systems. This systematic review summarizes the clinical applicability and diagnostic performance of AI-centred systems in the diagnosis and surveillance of esophagogastric cancers. Methods A systematic review was performed using the MEDLINE, EMBASE, Cochrane Review, and Scopus databases. Articles on the use of AI and radiomics for the diagnosis and surveillance of patients with esophageal cancer were evaluated, and quality assessment of studies was performed using the QUADAS-2 tool. A meta-analysis was performed to assess the diagnostic accuracy of sequencing methodologies. Results Thirty-six studies that described the use of AI were included in the qualitative synthesis and six studies involving 1352 patients were included in the quantitative analysis. Of these six studies, four studies assessed the utility of AI in gastric cancer diagnosis, one study assessed its utility for diagnosing esophageal cancer, and one study assessed its utility for surveillance. The pooled sensitivity and specificity were 73.4% (64.6–80.7) and 89.7% (82.7–94.1), respectively. Conclusions AI systems have shown promise in diagnosing and monitoring esophageal and gastric cancer, particularly when combined with existing diagnostic methods. Further work is needed to further develop systems of greater accuracy and greater consideration of the clinical workflows that they aim to integrate within.

Speech as a Biomarker: Opportunities, Interpretability, and Challenges

2022 ◽  
pp. 1-8
Author(s):  
Vikram Ramanarayanan ◽  
Adam C. Lammert ◽  
Hannah P. Rowe ◽  
Thomas F. Quatieri ◽  
Jordan R. Green
Keyword(s):  
Artificial Intelligence ◽  
Clinical Practice ◽  
Speech Processing ◽  
Clinical Applications ◽  
Routine Clinical Practice ◽  
Medical Assessment ◽  
Physiological Conditions ◽  
The Past ◽  
Assessment And Monitoring ◽  
Digital Biomarkers

Purpose: Over the past decade, the signal processing and machine learning literature has demonstrated notable advancements in automated speech processing with the use of artificial intelligence for medical assessment and monitoring (e.g., depression, dementia, and Parkinson's disease, among others). Meanwhile, the clinical speech literature has identified several interpretable, theoretically motivated measures that are sensitive to abnormalities in the cognitive, linguistic, affective, motoric, and anatomical domains. Both fields have, thus, independently demonstrated the potential for speech to serve as an informative biomarker for detecting different psychiatric and physiological conditions. However, despite these parallel advancements, automated speech biomarkers have not been integrated into routine clinical practice to date. Conclusions: In this article, we present opportunities and challenges for adoption of speech as a biomarker in clinical practice and research. Toward clinical acceptance and adoption of speech-based digital biomarkers, we argue for the importance of several factors such as robustness, specificity, diversity, and physiological interpretability of speech analytics in clinical applications.

scholarly journals Clinical applications and performance of intelligent systems in dental and maxillofacial radiology: A review

2020 ◽  
Vol 50 (2) ◽  
pp. 81 ◽  
Author(s):  
Ravleen Nagi ◽  
Konidena Aravinda ◽  
N Rakesh ◽  
Rajesh Gupta ◽  
Ajay Pal ◽  
...  
Keyword(s):  
Intelligent Systems ◽  
Clinical Applications ◽  
Maxillofacial Radiology ◽  
And Performance

scholarly journals Current Approaches to the Use of Artificial Intelligence for Injury Risk Assessment and Performance Prediction in Team Sports: a Systematic Review

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
João Gustavo Claudino ◽  
Daniel de Oliveira Capanema ◽  
Thiago Vieira de Souza ◽  
Julio Cerca Serrão ◽  
Adriano C. Machado Pereira ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Risk Assessment ◽  
Performance Prediction ◽  
Injury Risk ◽  
Team Sports ◽  
And Performance

scholarly journals Application and Performance of Artificial Intelligence Technology in Oral Cancer Diagnosis and Prediction of Prognosis: A Systematic Review

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1004
Author(s):  
Sanjeev B. Khanagar ◽  
Sachin Naik ◽  
Abdulaziz Abdullah Al Kheraif ◽  
Satish Vishwanathaiah ◽  
Prabhadevi C. Maganur ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Oral Cancer ◽  
Cox Regression ◽  
Eligibility Criteria ◽  
Cox Regression Analysis ◽  
Electronic Search ◽  
Clinical Strategies ◽  
Artificial Intelligence Technology ◽  
And Performance ◽  
Prediction Of Prognosis

Oral cancer (OC) is a deadly disease with a high mortality and complex etiology. Artificial intelligence (AI) is one of the outstanding innovations in technology used in dental science. This paper intends to report on the application and performance of AI in diagnosis and predicting the occurrence of OC. In this study, we carried out data search through an electronic search in several renowned databases, which mainly included PubMed, Google Scholar, Scopus, Embase, Cochrane, Web of Science, and the Saudi Digital Library for articles that were published between January 2000 to March 2021. We included 16 articles that met the eligibility criteria and were critically analyzed using QUADAS-2. AI can precisely analyze an enormous dataset of images (fluorescent, hyperspectral, cytology, CT images, etc.) to diagnose OC. AI can accurately predict the occurrence of OC, as compared to conventional methods, by analyzing predisposing factors like age, gender, tobacco habits, and bio-markers. The precision and accuracy of AI in diagnosis as well as predicting the occurrence are higher than the current, existing clinical strategies, as well as conventional statistics like cox regression analysis and logistic regression.

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