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European Archives of... Feb 2023This PRISMA-compliant systematic review aims to analyze the existing applications of artificial intelligence (AI), machine learning, and deep learning for rhinological... (Review)
Review
PURPOSE
This PRISMA-compliant systematic review aims to analyze the existing applications of artificial intelligence (AI), machine learning, and deep learning for rhinological purposes and compare works in terms of data pool size, AI systems, input and outputs, and model reliability.
METHODS
MEDLINE, Embase, Web of Science, Cochrane Library, and ClinicalTrials.gov databases. Search criteria were designed to include all studies published until December 2021 presenting or employing AI for rhinological applications. We selected all original studies specifying AI models reliability. After duplicate removal, abstract and full-text selection, and quality assessment, we reviewed eligible articles for data pool size, AI tools used, input and outputs, and model reliability.
RESULTS
Among 1378 unique citations, 39 studies were deemed eligible. Most studies (n = 29) were technical papers. Input included compiled data, verbal data, and 2D images, while outputs were in most cases dichotomous or selected among nominal classes. The most frequently employed AI tools were support vector machine for compiled data and convolutional neural network for 2D images. Model reliability was variable, but in most cases was reported to be between 80% and 100%.
CONCLUSIONS
AI has vast potential in rhinology, but an inherent lack of accessible code sources does not allow for sharing results and advancing research without reconstructing models from scratch. While data pools do not necessarily represent a problem for model construction, presently available tools appear limited in allowing employment of raw clinical data, thus demanding immense interpretive work prior to the analytic process.
Topics: Humans; Artificial Intelligence; Deep Learning; Reproducibility of Results; Machine Learning; Databases, Factual
PubMed: 36260141
DOI: 10.1007/s00405-022-07701-3 -
Journal of Orthopaedic Surgery and... Dec 2022In the emergency room, clinicians spend a lot of time and are exposed to mental stress. In addition, fracture classification is important for determining the surgical...
BACKGROUND
In the emergency room, clinicians spend a lot of time and are exposed to mental stress. In addition, fracture classification is important for determining the surgical method and restoring the patient's mobility. Recently, with the help of computers using artificial intelligence (AI) or machine learning (ML), diagnosis and classification of hip fractures can be performed easily and quickly. The purpose of this systematic review is to search for studies that diagnose and classify for hip fracture using AI or ML, organize the results of each study, analyze the usefulness of this technology and its future use value.
METHODS
PubMed Central, OVID Medline, Cochrane Collaboration Library, Web of Science, EMBASE, and AHRQ databases were searched to identify relevant studies published up to June 2022 with English language restriction. The following search terms were used [All Fields] AND (", "[MeSH Terms] OR (""[All Fields] AND "bone"[All Fields]) OR "bone fractures"[All Fields] OR "fracture"[All Fields]). The following information was extracted from the included articles: authors, publication year, study period, type of image, type of fracture, number of patient or used images, fracture classification, reference diagnosis of fracture diagnosis and classification, and augments of each studies. In addition, AI name, CNN architecture type, ROI or important region labeling, data input proportion in training/validation/test, and diagnosis accuracy/AUC, classification accuracy/AUC of each studies were also extracted.
RESULTS
In 14 finally included studies, the accuracy of diagnosis for hip fracture by AI was 79.3-98%, and the accuracy of fracture diagnosis in AI aided humans was 90.5-97.1. The accuracy of human fracture diagnosis was 77.5-93.5. AUC of fracture diagnosis by AI was 0.905-0.99. The accuracy of fracture classification by AI was 86-98.5 and AUC was 0.873-1.0. The forest plot represented that the mean AI diagnosis accuracy was 0.92, the mean AI diagnosis AUC was 0.969, the mean AI classification accuracy was 0.914, and the mean AI classification AUC was 0.933. Among the included studies, the architecture based on the GoogLeNet architectural model or the DenseNet architectural model was the most common with three each. Among the data input proportions, the study with the lowest training rate was 57%, and the study with the highest training rate was 95%. In 14 studies, 5 studies used Grad-CAM for highlight important regions.
CONCLUSION
We expected that our study may be helpful in making judgments about the use of AI in the diagnosis and classification of hip fractures. It is clear that AI is a tool that can help medical staff reduce the time and effort required for hip fracture diagnosis with high accuracy. Further studies are needed to determine what effect this causes in actual clinical situations.
Topics: Humans; Artificial Intelligence; Hip Fractures; Machine Learning; Databases, Factual; Emergency Service, Hospital
PubMed: 36456982
DOI: 10.1186/s13018-022-03408-7 -
International Journal of Nursing Studies Jul 2023A virtual conversational agent is a program that typically utilizes artificial intelligence technology to mimic human interactions. Many robust and high-quality clinical... (Review)
Review
BACKGROUND
A virtual conversational agent is a program that typically utilizes artificial intelligence technology to mimic human interactions. Many robust and high-quality clinical trials have been conducted to test the effectiveness of conversational agent-based interventions. However, there is a lack of systematic reviews of randomized controlled trials that evaluate the effects of artificial intelligence-driven conversational agents in healthcare interventions.
OBJECTIVE
To examine the feasibility and effectiveness of conversational agent-based interventions evaluated by randomized controlled trials in the healthcare context, as well as to evaluate the information quality of artificial intelligence-driven conversational agents.
DESIGN
A systematic review.
DATA SOURCE
A systematic search of relevant literature published in English in Scopus, Pubmed, Embase, PsycINFO, Cochrane Library, Information Science & Technology, and Web of Science, was performed. Only randomized controlled trials from the inception of the databases until May 2022 were included.
REVIEW METHODS
Two reviewers independently selected the articles according to the inclusion and exclusion criteria. Study findings were narratively synthesized and summarized. The studies' risk of bias was evaluated using the Risk of Bias 2.0 tool. The Silberg Scale was used to evaluate the quality of the conversational agent system utilized in each reviewed study.
RESULTS
Twenty-one studies were included in the data synthesis. The recruitment rates ranged from 34% to 100% (mean = 84%), and completion rates ranged from 40% to 100% (mean = 83%). A moderate to high level of intervention acceptability was reported. The intervention approaches included health counseling and education (n = 8), cognitive-behavioral interventions (n = 7), storytelling (n = 1), acceptance and commitment therapy (n = 1), and coping skills training (n = 1). Findings indicated inconsistent effects on improving participants' physical activity and function, healthy lifestyle modifications, knowledge of the diseases, and mental health and psychosocial outcomes. The overall risk of bias varied from low risk (n = 6) to high risk (n = 7) across the studies. The mean Silberg score of included studies was 5.4/9, with a standard deviation of 1.6.
CONCLUSION
Our review findings indicated that conversational agent-based interventions were feasible, acceptable, and had positive effects on physical functioning, healthy lifestyle, mental health and psychosocial outcomes. Conversational agents can provide low-threshold access to healthcare services. They can serve as remote medical assistants to support patients' recovery or health promotion needs before or after medical treatments. The conversational agent-based interventions can also play adjunctive roles and be integrated into current healthcare systems, which could improve the comprehensiveness of services and make more efficient use of physicians' and nurses' time.
Topics: Humans; Acceptance and Commitment Therapy; Artificial Intelligence; Delivery of Health Care; Feasibility Studies; Randomized Controlled Trials as Topic
PubMed: 37146391
DOI: 10.1016/j.ijnurstu.2023.104494 -
Journal of Plastic, Reconstructive &... Feb 2023Artificial Intelligence (AI) is already being successfully employed to aid the interpretation of multiple facets of burns care. In the light of the growing influence of... (Meta-Analysis)
Meta-Analysis Review
INTRODUCTION AND AIM
Artificial Intelligence (AI) is already being successfully employed to aid the interpretation of multiple facets of burns care. In the light of the growing influence of AI, this systematic review and diagnostic test accuracy meta-analyses aim to appraise and summarise the current direction of research in this field.
METHOD
A systematic literature review was conducted of relevant studies published between 1990 and 2021, yielding 35 studies. Twelve studies were suitable for a Diagnostic Test Meta-Analyses.
RESULTS
The studies generally focussed on burn depth (Accuracy 68.9%-95.4%, Sensitivity 90.8% and Specificity 84.4%), burn segmentation (Accuracy 76.0%-99.4%, Sensitivity 97.9% and specificity 97.6%) and burn related mortality (Accuracy >90%-97.5% Sensitivity 92.9% and specificity 93.4%). Neural networks were the most common machine learning (ML) algorithm utilised in 69% of the studies. The QUADAS-2 tool identified significant heterogeneity between studies.
DISCUSSION
The potential application of AI in the management of burns patients is promising, especially given its propitious results across a spectrum of dimensions, including burn depth, size, mortality, related sepsis and acute kidney injuries. The accuracy of the results analysed within this study is comparable to current practices in burns care.
CONCLUSION
The application of AI in the treatment and management of burns patients, as a series of point of care diagnostic adjuncts, is promising. Whilst AI is a potentially valuable tool, a full evaluation of its current utility and potential is limited by significant variations in research methodology and reporting.
Topics: Humans; Artificial Intelligence; Algorithms; Burns
PubMed: 36571960
DOI: 10.1016/j.bjps.2022.11.049 -
Frontiers in Endocrinology 2023To systematically evaluate the diagnostic value of an artificial intelligence (AI) algorithm model for various types of diabetic retinopathy (DR) in prospective studies... (Meta-Analysis)
Meta-Analysis
AIMS
To systematically evaluate the diagnostic value of an artificial intelligence (AI) algorithm model for various types of diabetic retinopathy (DR) in prospective studies over the previous five years, and to explore the factors affecting its diagnostic effectiveness.
MATERIALS AND METHODS
A search was conducted in Cochrane Library, Embase, Web of Science, PubMed, and IEEE databases to collect prospective studies on AI models for the diagnosis of DR from January 2017 to December 2022. We used QUADAS-2 to evaluate the risk of bias in the included studies. Meta-analysis was performed using MetaDiSc and STATA 14.0 software to calculate the combined sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of various types of DR. Diagnostic odds ratios, summary receiver operating characteristic (SROC) plots, coupled forest plots, and subgroup analysis were performed according to the DR categories, patient source, region of study, and quality of literature, image, and algorithm.
RESULTS
Finally, 21 studies were included. Meta-analysis showed that the pooled sensitivity, specificity, pooled positive likelihood ratio, pooled negative likelihood ratio, area under the curve, Cochrane Q index, and pooled diagnostic odds ratio of AI model for the diagnosis of DR were 0.880 (0.875-0.884), 0.912 (0.99-0.913), 13.021 (10.738-15.789), 0.083 (0.061-0.112), 0.9798, 0.9388, and 206.80 (124.82-342.63), respectively. The DR categories, patient source, region of study, sample size, quality of literature, image, and algorithm may affect the diagnostic efficiency of AI for DR.
CONCLUSION
AI model has a clear diagnostic value for DR, but it is influenced by many factors that deserve further study.
SYSTEMATIC REVIEW REGISTRATION
https://www.crd.york.ac.uk/prospero/, identifier CRD42023389687.
Topics: Humans; Artificial Intelligence; Prospective Studies; Diabetic Retinopathy; Algorithms; Software; Diabetes Mellitus
PubMed: 37383397
DOI: 10.3389/fendo.2023.1197783 -
Osteoarthritis and Cartilage Mar 2024As an increasing number of studies apply artificial intelligence (AI) algorithms in osteoarthritis (OA) detection, we performed a systematic review and meta-analysis to... (Meta-Analysis)
Meta-Analysis Review
OBJECTIVES
As an increasing number of studies apply artificial intelligence (AI) algorithms in osteoarthritis (OA) detection, we performed a systematic review and meta-analysis to pool the data on diagnostic performance metrics of AI, and to compare them with clinicians' performance.
MATERIALS AND METHODS
A search in PubMed and Scopus was performed to find studies published up to April 2022 that evaluated and/or validated an AI algorithm for the detection or classification of OA. We performed a meta-analysis to pool the data on the metrics of diagnostic performance. Subgroup analysis based on the involved joint and meta-regression based on multiple parameters were performed to find potential sources of heterogeneity. The risk of bias was assessed using Prediction Model Study Risk of Bias Assessment Tool reporting guidelines.
RESULTS
Of the 61 studies included, 27 studies with 91 contingency tables provided sufficient data to enter the meta-analysis. The pooled sensitivities for AI algorithms and clinicians on internal validation test sets were 88% (95% confidence interval [CI]: 86,91) and 80% (95% CI: 68,88) and pooled specificities were 81% (95% CI: 75,85) and 79% (95% CI: 80,85), respectively. At external validation, the pooled sensitivity and specificity for AI algorithms were 94% (95% CI: 90,97) and 91% (95% CI: 77,97), respectively.
CONCLUSION
Although the results of this meta-analysis should be interpreted with caution due to the potential pitfalls in the included studies, the promising role of AI as a diagnostic adjunct to radiologists is indisputable.
Topics: Humans; Artificial Intelligence; Algorithms; Benchmarking; Osteoarthritis
PubMed: 37863421
DOI: 10.1016/j.joca.2023.09.011 -
Journal of Medical Internet Research Nov 2023The application of artificial intelligence (AI) in the delivery of health care is a promising area, and guidelines, consensus statements, and standards on AI regarding... (Review)
Review
BACKGROUND
The application of artificial intelligence (AI) in the delivery of health care is a promising area, and guidelines, consensus statements, and standards on AI regarding various topics have been developed.
OBJECTIVE
We performed this study to assess the quality of guidelines, consensus statements, and standards in the field of AI for medicine and to provide a foundation for recommendations about the future development of AI guidelines.
METHODS
We searched 7 electronic databases from database establishment to April 6, 2022, and screened articles involving AI guidelines, consensus statements, and standards for eligibility. The AGREE II (Appraisal of Guidelines for Research & Evaluation II) and RIGHT (Reporting Items for Practice Guidelines in Healthcare) tools were used to assess the methodological and reporting quality of the included articles.
RESULTS
This systematic review included 19 guideline articles, 14 consensus statement articles, and 3 standard articles published between 2019 and 2022. Their content involved disease screening, diagnosis, and treatment; AI intervention trial reporting; AI imaging development and collaboration; AI data application; and AI ethics governance and applications. Our quality assessment revealed that the average overall AGREE II score was 4.0 (range 2.2-5.5; 7-point Likert scale) and the mean overall reporting rate of the RIGHT tool was 49.4% (range 25.7%-77.1%).
CONCLUSIONS
The results indicated important differences in the quality of different AI guidelines, consensus statements, and standards. We made recommendations for improving their methodological and reporting quality.
TRIAL REGISTRATION
PROSPERO International Prospective Register of Systematic Reviews (CRD42022321360); https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=321360.
Topics: Humans; Artificial Intelligence; Consensus; Databases, Factual; Medicine; Guidelines as Topic
PubMed: 37991819
DOI: 10.2196/46089 -
Clinical Otolaryngology : Official... May 2022To summarise the accuracy of artificial intelligence (AI) computer vision algorithms to classify ear disease from otoscopy. (Meta-Analysis)
Meta-Analysis
OBJECTIVES
To summarise the accuracy of artificial intelligence (AI) computer vision algorithms to classify ear disease from otoscopy.
DESIGN
Systematic review and meta-analysis.
METHODS
Using the PRISMA guidelines, nine online databases were searched for articles that used AI computer vision algorithms developed from various methods (convolutional neural networks, artificial neural networks, support vector machines, decision trees and k-nearest neighbours) to classify otoscopic images. Diagnostic classes of interest: normal tympanic membrane, acute otitis media (AOM), otitis media with effusion (OME), chronic otitis media (COM) with or without perforation, cholesteatoma and canal obstruction.
MAIN OUTCOME MEASURES
Accuracy to correctly classify otoscopic images compared to otolaryngologists (ground truth). The Quality Assessment of Diagnostic Accuracy Studies Version 2 tool was used to assess the quality of methodology and risk of bias.
RESULTS
Thirty-nine articles were included. Algorithms achieved 90.7% (95%CI: 90.1-91.3%) accuracy to difference between normal or abnormal otoscopy images in 14 studies. The most common multiclassification algorithm (3 or more diagnostic classes) achieved 97.6% (95%CI: 97.3-97.9%) accuracy to differentiate between normal, AOM and OME in three studies. AI algorithms outperformed human assessors to classify otoscopy images achieving 93.4% (95%CI: 90.5-96.4%) versus 73.2% (95%CI: 67.9-78.5%) accuracy in three studies. Convolutional neural networks achieved the highest accuracy compared to other classification methods.
CONCLUSION
AI can classify ear disease from otoscopy. A concerted effort is required to establish a comprehensive and reliable otoscopy database for algorithm training. An AI-supported otoscopy system may assist health care workers, trainees and primary care practitioners with less otology experience identify ear disease.
Topics: Artificial Intelligence; Ear Diseases; Humans; Otitis Media; Otitis Media with Effusion; Otoscopes; Otoscopy
PubMed: 35253378
DOI: 10.1111/coa.13925 -
Frontiers in Endocrinology 2023Polycystic Ovarian Syndrome (PCOS) is the most common endocrinopathy in women of reproductive age and remains widely underdiagnosed leading to significant morbidity....
INTRODUCTION
Polycystic Ovarian Syndrome (PCOS) is the most common endocrinopathy in women of reproductive age and remains widely underdiagnosed leading to significant morbidity. Artificial intelligence (AI) and machine learning (ML) hold promise in improving diagnostics. Thus, we performed a systematic review of literature to identify the utility of AI/ML in the diagnosis or classification of PCOS.
METHODS
We applied a search strategy using the following databases MEDLINE, Embase, the Cochrane Central Register of Controlled Trials, the Web of Science, and the IEEE Xplore Digital Library using relevant keywords. Eligible studies were identified, and results were extracted for their synthesis from inception until January 1, 2022.
RESULTS
135 studies were screened and ultimately, 31 studies were included in this study. Data sources used by the AI/ML interventions included clinical data, electronic health records, and genetic and proteomic data. Ten studies (32%) employed standardized criteria (NIH, Rotterdam, or Revised International PCOS classification), while 17 (55%) used clinical information with/without imaging. The most common AI techniques employed were support vector machine (42% studies), K-nearest neighbor (26%), and regression models (23%) were the commonest AI/ML. Receiver operating curves (ROC) were employed to compare AI/ML with clinical diagnosis. Area under the ROC ranged from 73% to 100% (n=7 studies), diagnostic accuracy from 89% to 100% (n=4 studies), sensitivity from 41% to 100% (n=10 studies), specificity from 75% to 100% (n=10 studies), positive predictive value (PPV) from 68% to 95% (n=4 studies), and negative predictive value (NPV) from 94% to 99% (n=2 studies).
CONCLUSION
Artificial intelligence and machine learning provide a high diagnostic and classification performance in detecting PCOS, thereby providing an avenue for early diagnosis of this disorder. However, AI-based studies should use standardized PCOS diagnostic criteria to enhance the clinical applicability of AI/ML in PCOS and improve adherence to methodological and reporting guidelines for maximum diagnostic utility.
SYSTEMATIC REVIEW REGISTRATION
https://www.crd.york.ac.uk/prospero/, identifier CRD42022295287.
Topics: Female; Humans; Artificial Intelligence; Polycystic Ovary Syndrome; Proteomics; Machine Learning; Cluster Analysis
PubMed: 37790605
DOI: 10.3389/fendo.2023.1106625 -
Fertility and Sterility Jul 2023Despite the increasing number of assisted reproductive technologies based treatments being performed worldwide, there has been little improvement in fertilization and... (Review)
Review
Despite the increasing number of assisted reproductive technologies based treatments being performed worldwide, there has been little improvement in fertilization and pregnancy outcomes. Male infertility is a major contributing factor, and sperm evaluation is a crucial step in diagnosis and treatment. However, embryologists face the daunting task of selecting a single sperm from millions in a sample based on various parameters, which can be time-consuming, subjective, and may even cause damage to the sperm, deeming them unusable for fertility treatments. Artificial intelligence algorithms have revolutionized the field of medicine, particularly in image processing, because of their discerning abilities, efficacy, and reproducibility. Artificial intelligence algorithms have the potential to address the challenges of sperm selection with their large-data processing capabilities and high objectivity. These algorithms could provide valuable assistance to embryologists in sperm analysis and selection. Furthermore, these algorithms could continue to improve over time as larger and more robust datasets become available for their training.
Topics: Pregnancy; Female; Male; Humans; Artificial Intelligence; Reproducibility of Results; Semen; Spermatozoa; Infertility, Male
PubMed: 37236418
DOI: 10.1016/j.fertnstert.2023.05.157