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American Journal of Clinical Pathology Jun 2024The high incidence of prostate cancer causes prostatic samples to significantly affect pathology laboratories workflow and turnaround times (TATs). Whole-slide imaging...
OBJECTIVES
The high incidence of prostate cancer causes prostatic samples to significantly affect pathology laboratories workflow and turnaround times (TATs). Whole-slide imaging (WSI) and artificial intelligence (AI) have both gained approval for primary diagnosis in prostate pathology, providing physicians with novel tools for their daily routine.
METHODS
A systematic review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was carried out in electronic databases to gather the available evidence on the application of AI-based algorithms to prostate cancer.
RESULTS
Of 6290 articles, 80 were included, mostly (59%) dealing with biopsy specimens. Glass slides were digitized to WSI in most studies (89%), roughly two-thirds of which (66%) exploited convolutional neural networks for computational analysis. The algorithms achieved good to excellent results about cancer detection and grading, along with significantly reduced TATs. Furthermore, several studies showed a relevant correlation between AI-identified histologic features and prognostic predictive variables such as biochemical recurrence, extraprostatic extension, perineural invasion, and disease-free survival.
CONCLUSIONS
The published evidence suggests that AI can be reliably used for prostate cancer detection and grading, assisting pathologists in the time-consuming screening of slides. Further technologic improvement would help widening AI's adoption in prostate pathology, as well as expanding its prognostic predictive potential.
Topics: Humans; Prostatic Neoplasms; Male; Artificial Intelligence; Algorithms
PubMed: 38381582
DOI: 10.1093/ajcp/aqad182 -
La Clinica Terapeutica 2024Artificial intelligence (AI) is the ability of a computer machine to display human capabilities such as reasoning, learning, planning, and creativity. Such processing...
OBJECTIVE
Artificial intelligence (AI) is the ability of a computer machine to display human capabilities such as reasoning, learning, planning, and creativity. Such processing technology receives the data (already prepared or collected), processes them, using models and algorithms, and answers questions about forecasting and decision-making. AI systems are also able to adapt their behavior by analyzing the effects of previous actions and working then autonomously. Artificial intelligence is already present in our lives, even if it often goes unnoticed (shopping networked, home automation, vehicles). Even in the medical field, artificial intelligence can be used to analyze large amounts of medical data and discover matches and patterns to improve diagnosis and prevention. In forensic medicine, the applications of AI are numerous and are becoming more and more valuable.
METHOD
A systematic review was conducted, selecting the articles in one of the most widely used electronic databases (PubMed). The research was conducted using the keywords "AI forensic" and "machine learning forensic". The research process included about 2000 Articles published from 1990 to the present.
RESULTS
We have focused on the most common fields of use and have been then 6 macro-topics were identified and analyzed. Specifically, articles were analyzed concerning the application of AI in forensic pathology (main area), toxicology, radiology, Personal identification, forensic anthropology, and forensic psychiatry.
CONCLUSION
The aim of the study is to evaluate the current applications of AI in forensic medicine for each field of use, trying to grasp future and more usable applications and underline their limitations.
Topics: Artificial Intelligence; Humans; Forensic Medicine; Machine Learning; Forecasting
PubMed: 38767078
DOI: 10.7417/CT.2024.5062 -
Artificial Intelligence in Medicine Sep 2023Diabetic Retinopathy (DR) is the most popular debilitating impairment of diabetes and it progresses symptom-free until a sudden loss of vision occurs. Understanding the... (Review)
Review
Diabetic Retinopathy (DR) is the most popular debilitating impairment of diabetes and it progresses symptom-free until a sudden loss of vision occurs. Understanding the progression of DR is a pressing issue in clinical research and practice. In this systematic review of articles on Machine Learning (ML) based risk prediction models for DR progression, ever since the use of Artificial Intelligence (AI) for DR detection, there have been more cross-sectional studies with different algorithms of use of AI, there haven't been many longitudinal studies for the AI based risk prediction models. This paper proposes a novel review to fill in the gaps identified in current reviews and facilitate other researchers with current research solutions for developing AI-based risk prediction models for DR progression and closely related problems; synthesize the current results from these studies and identify research challenges, limitations and gaps to inform the selection of machine learning techniques and predictors to build novel prediction models. Additionally, this paper suggested six (6) deep AI-related technical and critical discussion of the adopted strategies and approaches. The Systematic Literature Review (SLR) methodology was employed to gather relevant studies. We searched IEEE Xplore, PubMed, Springer Link, Google Scholar, and Science Direct electronic databases for papers published from January 2017 to 30th April 2023. Thirteen (13) studies were chosen on the basis of their relevance to the review questions and satisfying the selection criteria. However, findings from the literature review exposed some critical research gaps that need to be addressed in future research to improve on the performance of risk prediction models for DR progression.
Topics: Humans; Diabetic Retinopathy; Artificial Intelligence; Cross-Sectional Studies; Machine Learning; Algorithms; Diabetes Mellitus
PubMed: 37673580
DOI: 10.1016/j.artmed.2023.102617 -
Computers in Biology and Medicine Oct 2023Machine learning has gained popularity in predicting survival time in the medical field. This review examines studies utilizing machine learning and data-mining... (Review)
Review
Machine learning has gained popularity in predicting survival time in the medical field. This review examines studies utilizing machine learning and data-mining techniques to predict lung cancer survival using clinical data. A systematic literature review searched MEDLINE, Scopus, and Google Scholar databases, following reporting guidelines and using the COVIDENCE system. Studies published from 2000 to 2023 employing machine learning for lung cancer survival prediction were included. Risk of bias assessment used the prediction model risk of bias assessment tool. Thirty studies were reviewed, with 13 (43.3%) using the surveillance, epidemiology, and end results database. Missing data handling was addressed in 12 (40%) studies, primarily through data transformation and conversion. Feature selection algorithms were used in 19 (63.3%) studies, with age, sex, and N stage being the most chosen features. Random forest was the predominant machine learning model, used in 17 (56.6%) studies. While the number of lung cancer survival prediction studies is limited, the use of machine learning models based on clinical data has grown since 2012. Consideration of diverse patient cohorts and data pre-processing are crucial. Notably, most studies did not account for missing data, normalization, scaling, or standardized data, potentially introducing bias. Therefore, a comprehensive study on lung cancer survival prediction using clinical data is needed, addressing these challenges.
Topics: Humans; Lung Neoplasms; Algorithms; Machine Learning; Data Mining; Random Forest
PubMed: 37625260
DOI: 10.1016/j.compbiomed.2023.107338 -
Clinical Oral Investigations Jan 2024This study aimed to review and synthesize studies using artificial intelligence (AI) for classifying, detecting, or segmenting oral mucosal lesions on photographs. (Meta-Analysis)
Meta-Analysis Review
OBJECTIVE
This study aimed to review and synthesize studies using artificial intelligence (AI) for classifying, detecting, or segmenting oral mucosal lesions on photographs.
MATERIALS AND METHOD
Inclusion criteria were (1) studies employing AI to (2) classify, detect, or segment oral mucosa lesions, (3) on oral photographs of human subjects. Included studies were assessed for risk of bias using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). A PubMed, Scopus, Embase, Web of Science, IEEE, arXiv, medRxiv, and grey literature (Google Scholar) search was conducted until June 2023, without language limitation.
RESULTS
After initial searching, 36 eligible studies (from 8734 identified records) were included. Based on QUADAS-2, only 7% of studies were at low risk of bias for all domains. Studies employed different AI models and reported a wide range of outcomes and metrics. The accuracy of AI for detecting oral mucosal lesions ranged from 74 to 100%, while that for clinicians un-aided by AI ranged from 61 to 98%. Pooled diagnostic odds ratio for studies which evaluated AI for diagnosing or discriminating potentially malignant lesions was 155 (95% confidence interval 23-1019), while that for cancerous lesions was 114 (59-221).
CONCLUSIONS
AI may assist in oral mucosa lesion screening while the expected accuracy gains or further health benefits remain unclear so far.
CLINICAL RELEVANCE
Artificial intelligence assists oral mucosa lesion screening and may foster more targeted testing and referral in the hands of non-specialist providers, for example. So far, it remains unclear if accuracy gains compared with specialized can be realized.
Topics: Humans; Artificial Intelligence; Mouth Mucosa; Referral and Consultation
PubMed: 38217733
DOI: 10.1007/s00784-023-05475-4 -
Asian Pacific Journal of Cancer... Nov 2023Colonoscopy may detect colorectal polyp and facilitate its removal in order to prevent colorectal cancer. However, substantial miss rate for colorectal adenomas... (Meta-Analysis)
Meta-Analysis
INTRODUCTION
Colonoscopy may detect colorectal polyp and facilitate its removal in order to prevent colorectal cancer. However, substantial miss rate for colorectal adenomas detection still occurred during screening colonoscopy procedure. Nowadays, artificial intelligence (AI) have been employed in trials to improve polyp detection rate (PDR) and adenoma detection rate (ADR). Therefore, we would like to determine the impact of AI in increasing PDR and ADR.
METHODS
The present study adhered to the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-analyses 2020 (PRISMA 2020) statement. To identify relevant literature, comprehensive searches were conducted on major scientific databases, including Pubmed, EBSCO-host, and Proquest. The search was limited to articles published up to November 30, 2022. Inclusion criteria for the study encompassed full-text accessibility, articles written in the English language, and randomized controlled trials (RCTs) that reported both ADR and PDR values, comparing conventional diagnostic methods with AI-aided approaches. To synthesize the data, we computed the combined pooled odds ratio (OR) using a random-effects model. This model was chosen due to the expectation of considerable heterogeneity among the selected studies. To evaluate potential publication bias, the Begg's funnel diagram was employed.
RESULTS
A total of 13 studies were included in this study. Colonoscopy with AI had significantly higher PDR compared to without AI (pooled OR 1.46, 95% CI 1.13-1.89, p = 0.003) and higher ADR (pooled OR 1.58, 95% CI 1.37-1.82, p < 0.00001). PDR analysis showed moderate heterogeneity between included studies (p = 0.004; I2=63%). Furthermore, ADR analysis showed moderate heterogeneity (p < 0.007; I2 = 57%). Additionally, the funnels plot of ADR and PDR analysis showed an asymmetry plot and low publication bias.
CONCLUSION
AI may improve colonoscopy result quality through improving PDR and ADR.
Topics: Humans; Adenoma; Artificial Intelligence; Colonoscopy; Colorectal Neoplasms; Databases, Factual
PubMed: 38019222
DOI: 10.31557/APJCP.2023.24.11.3655 -
Polski Przeglad Chirurgiczny Nov 2023<b><br>Introduction:</b> Artificial intelligence (AI) is an emerging technology with vast potential for use in several fields of medicine. However,...
<b><br>Introduction:</b> Artificial intelligence (AI) is an emerging technology with vast potential for use in several fields of medicine. However, little is known about the application of AI in treatment decisions for patients with polytrauma. In this systematic review, we investigated the benefits and performance of AI in predicting the management of patients with polytrauma and trauma.</br> <b><br>Methods:</b> This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Studies were extracted from the PubMed and Google Scholar databases from their inception until November 2022, using the search terms "Artificial intelligence," "polytrauma," and "decision." Seventeen articles were identified and screened for eligibility. Animal studies, review articles, systematic reviews, meta-analyses, and studies that did not involve polytrauma or severe trauma management decisions were excluded. Eight studies were eligible for final review.</br> <b><br>Results:</b> Eight studies focusing on patients with trauma, including two on military trauma, were included. The AI applications were mainly implemented for predictions and/or decisions on shock, bleeding, and blood transfusion. Few studies predicted death/survival. The identification of trauma patients using AI was proposed in a previous study. The overall performance of AI was good (six studies), excellent (one study), and acceptable (one study).</br> <b><br>Discussion:</b> AI demonstrated satisfactory performance in decision-making and management prediction in patients with polytrauma/severe trauma, especially in situations of shock/bleeding.</br> <b><br>Importance:</b> The present study serves as a basis for further research to develop practical AI applications for the management of patients with trauma.</br>.
Topics: Animals; Humans; Artificial Intelligence; Multiple Trauma
PubMed: 38348980
DOI: 10.5604/01.3001.0053.9857 -
PloS One 2023In this review, we assessed the diagnostic efficiency of artificial intelligence (AI) models in detecting temporomandibular joint osteoarthritis (TMJOA) using... (Meta-Analysis)
Meta-Analysis
Artificial intelligence for detecting temporomandibular joint osteoarthritis using radiographic image data: A systematic review and meta-analysis of diagnostic test accuracy.
In this review, we assessed the diagnostic efficiency of artificial intelligence (AI) models in detecting temporomandibular joint osteoarthritis (TMJOA) using radiographic imaging data. Based upon the PRISMA guidelines, a systematic review of studies published between January 2010 and January 2023 was conducted using PubMed, Web of Science, Scopus, and Embase. Articles on the accuracy of AI to detect TMJOA or degenerative changes by radiographic imaging were selected. The characteristics and diagnostic information of each article were extracted. The quality of studies was assessed by the QUADAS-2 tool. Pooled data for sensitivity, specificity, and summary receiver operating characteristic curve (SROC) were calculated. Of 513 records identified through a database search, six met the inclusion criteria and were collected. The pooled sensitivity, specificity, and area under the curve (AUC) were 80%, 90%, and 92%, respectively. Substantial heterogeneity between AI models mainly arose from imaging modality, ethnicity, sex, techniques of AI, and sample size. This article confirmed AI models have enormous potential for diagnosing TMJOA automatically through radiographic imaging. Therefore, AI models appear to have enormous potential to diagnose TMJOA automatically using radiographic images. However, further studies are needed to evaluate AI more thoroughly.
Topics: Humans; Artificial Intelligence; ROC Curve; Temporomandibular Joint; Osteoarthritis; Diagnostic Tests, Routine
PubMed: 37450501
DOI: 10.1371/journal.pone.0288631 -
International Journal of Computer... Aug 2023The implementation of artificial intelligence in hand surgery and rehabilitation is gaining popularity. The purpose of this scoping review was to give an overview of... (Review)
Review
PURPOSE
The implementation of artificial intelligence in hand surgery and rehabilitation is gaining popularity. The purpose of this scoping review was to give an overview of implementations of artificial intelligence in hand surgery and rehabilitation and their current significance in clinical practice.
METHODS
A systematic literature search of the MEDLINE/PubMed and Cochrane Collaboration libraries was conducted. The review was conducted according to the framework outlined by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews. A narrative summary of the papers is presented to give an orienting overview of this rapidly evolving topic.
RESULTS
Primary search yielded 435 articles. After application of the inclusion/exclusion criteria and addition of supplementary search, 235 articles were included in the final review. In order to facilitate navigation through this heterogenous field, the articles were clustered into four groups of thematically related publications. The most common applications of artificial intelligence in hand surgery and rehabilitation target automated image analysis of anatomic structures, fracture detection and localization and automated screening for other hand and wrist pathologies such as carpal tunnel syndrome, rheumatoid arthritis or osteoporosis. Compared to other medical subspecialties the number of applications in hand surgery is still small.
CONCLUSION
Although various promising applications of artificial intelligence in hand surgery and rehabilitation show strong performances, their implementation mostly takes place within the context of experimental studies. Therefore, their use in daily clinical routine is still limited.
Topics: Humans; Artificial Intelligence; Carpal Tunnel Syndrome; Fractures, Bone; Hand; Image Processing, Computer-Assisted
PubMed: 36633789
DOI: 10.1007/s11548-023-02831-3 -
Diseases of the Esophagus : Official... Nov 2023Early detection of esophageal cancer is limited by accurate endoscopic diagnosis of subtle macroscopic lesions. Endoscopic interpretation is subject to expertise,... (Meta-Analysis)
Meta-Analysis
Early detection of esophageal cancer is limited by accurate endoscopic diagnosis of subtle macroscopic lesions. Endoscopic interpretation is subject to expertise, diagnostic skill, and thus human error. Artificial intelligence (AI) in endoscopy is increasingly bridging this gap. This systematic review and meta-analysis consolidate the evidence on the use of AI in the endoscopic diagnosis of esophageal cancer. The systematic review was carried out using Pubmed, MEDLINE and Ovid EMBASE databases and articles on the role of AI in the endoscopic diagnosis of esophageal cancer management were included. A meta-analysis was also performed. Fourteen studies (1590 patients) assessed the use of AI in endoscopic diagnosis of esophageal squamous cell carcinoma-the pooled sensitivity and specificity were 91.2% (84.3-95.2%) and 80% (64.3-89.9%). Nine studies (478 patients) assessed AI capabilities of diagnosing esophageal adenocarcinoma with the pooled sensitivity and specificity of 93.1% (86.8-96.4) and 86.9% (81.7-90.7). The remaining studies formed the qualitative summary. AI technology, as an adjunct to endoscopy, can assist in accurate, early detection of esophageal malignancy. It has shown superior results to endoscopists alone in identifying early cancer and assessing depth of tumor invasion, with the added benefit of not requiring a specialized skill set. Despite promising results, the application in real-time endoscopy is limited, and further multicenter trials are required to accurately assess its use in routine practice.
Topics: Humans; Esophageal Neoplasms; Esophageal Squamous Cell Carcinoma; Artificial Intelligence; Endoscopy; Adenocarcinoma
PubMed: 37480192
DOI: 10.1093/dote/doad048