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Techniques in Coloproctology Aug 2023Artificial intelligence (AI) has the potential to revolutionize surgery in the coming years. Still, it is essential to clarify what the meaningful current applications... (Review)
Review
Artificial intelligence (AI) has the potential to revolutionize surgery in the coming years. Still, it is essential to clarify what the meaningful current applications are and what can be reasonably expected. This AI-powered review assessed the role of AI in colorectal surgery. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-compliant systematic search of PubMed, Embase, Scopus, Cochrane Library databases, and gray literature was conducted on all available articles on AI in colorectal surgery (from January 1 1997 to March 1 2021), aiming to define the perioperative applications of AI. Potentially eligible studies were identified using novel software powered by natural language processing (NLP) and machine learning (ML) technologies dedicated to systematic reviews. Out of 1238 articles identified, 115 were included in the final analysis. Available articles addressed the role of AI in several areas of interest. In the preoperative phase, AI can be used to define tailored treatment algorithms, support clinical decision-making, assess the risk of complications, and predict surgical outcomes and survival. Intraoperatively, AI-enhanced surgery and integration of AI in robotic platforms have been suggested. After surgery, AI can be implemented in the Enhanced Recovery after Surgery (ERAS) pathway. Additional areas of applications included the assessment of patient-reported outcomes, automated pathology assessment, and research. Available data on these aspects are limited, and AI in colorectal surgery is still in its infancy. However, the rapid evolution of technologies makes it likely that it will increasingly be incorporated into everyday practice.
Topics: Humans; Artificial Intelligence; Colorectal Surgery; Digestive System Surgical Procedures; Robotics
PubMed: 36805890
DOI: 10.1007/s10151-023-02772-8 -
Nutrients Apr 2024In industry 4.0, where the automation and digitalization of entities and processes are fundamental, artificial intelligence (AI) is increasingly becoming a pivotal tool... (Review)
Review
In industry 4.0, where the automation and digitalization of entities and processes are fundamental, artificial intelligence (AI) is increasingly becoming a pivotal tool offering innovative solutions in various domains. In this context, nutrition, a critical aspect of public health, is no exception to the fields influenced by the integration of AI technology. This study aims to comprehensively investigate the current landscape of AI in nutrition, providing a deep understanding of the potential of AI, machine learning (ML), and deep learning (DL) in nutrition sciences and highlighting eventual challenges and futuristic directions. A hybrid approach from the systematic literature review (SLR) guidelines and the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines was adopted to systematically analyze the scientific literature from a search of major databases on artificial intelligence in nutrition sciences. A rigorous study selection was conducted using the most appropriate eligibility criteria, followed by a methodological quality assessment ensuring the robustness of the included studies. This review identifies several AI applications in nutrition, spanning smart and personalized nutrition, dietary assessment, food recognition and tracking, predictive modeling for disease prevention, and disease diagnosis and monitoring. The selected studies demonstrated the versatility of machine learning and deep learning techniques in handling complex relationships within nutritional datasets. This study provides a comprehensive overview of the current state of AI applications in nutrition sciences and identifies challenges and opportunities. With the rapid advancement in AI, its integration into nutrition holds significant promise to enhance individual nutritional outcomes and optimize dietary recommendations. Researchers, policymakers, and healthcare professionals can utilize this research to design future projects and support evidence-based decision-making in AI for nutrition and dietary guidance.
Topics: Humans; Artificial Intelligence; Deep Learning; Machine Learning; Nutritional Status; Automation
PubMed: 38613106
DOI: 10.3390/nu16071073 -
Journal of Clinical Monitoring and... Apr 2024Application of artificial intelligence (AI) in medicine is quickly expanding. Despite the amount of evidence and promising results, a thorough overview of the current... (Review)
Review
PURPOSE
Application of artificial intelligence (AI) in medicine is quickly expanding. Despite the amount of evidence and promising results, a thorough overview of the current state of AI in clinical practice of anesthesiology is needed. Therefore, our study aims to systematically review the application of AI in this context.
METHODS
A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched Medline and Web of Science for articles published up to November 2022 using terms related with AI and clinical practice of anesthesiology. Articles that involved animals, editorials, reviews and sample size lower than 10 patients were excluded. Characteristics and accuracy measures from each study were extracted.
RESULTS
A total of 46 articles were included in this review. We have grouped them into 4 categories with regard to their clinical applicability: (1) Depth of Anesthesia Monitoring; (2) Image-guided techniques related to Anesthesia; (3) Prediction of events/risks related to Anesthesia; (4) Drug administration control. Each group was analyzed, and the main findings were summarized. Across all fields, the majority of AI methods tested showed superior performance results compared to traditional methods.
CONCLUSION
AI systems are being integrated into anesthesiology clinical practice, enhancing medical professionals' skills of decision-making, diagnostic accuracy, and therapeutic response.
Topics: Animals; Humans; Anesthesiology; Artificial Intelligence; Anesthesia; Sample Size
PubMed: 37864754
DOI: 10.1007/s10877-023-01088-0 -
Neurological Sciences : Official... Feb 2023The expansion of the availability of advanced imaging methods needs more time, expertise, and resources which is in contrast to the primary goal of the imaging... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
The expansion of the availability of advanced imaging methods needs more time, expertise, and resources which is in contrast to the primary goal of the imaging techniques. To overcome most of these difficulties, artificial intelligence (AI) can be used. A number of studies used AI models for multiple sclerosis (MS) diagnosis and reported diverse results. Therefore, we aim to perform a comprehensive systematic review and meta-analysis study on the role of AI in the diagnosis of MS.
METHODS
We performed a systematic search using four databases including PubMed, Scopus, Web of Science, and IEEE. Studies that applied deep learning or AI to the diagnosis of MS based on any modalities were considered eligible in our study. The accuracy, sensitivity, specificity, precision, and area under curve (AUC) were pooled with a random-effects model and 95% confidence interval (CI).
RESULTS
After the screening, 41 articles with 5989 individuals met the inclusion criteria and were included in our qualitative and quantitative synthesis. Our analysis showed that the overall accuracy among studies was 94% (95%CI: 93%, 96%). The pooled sensitivity and specificity were 92% (95%CI: 90%, 95%) and 93% (95%CI: 90%, 96%), respectively. Furthermore, our analysis showed 92% precision in MS diagnosis for AI studies (95%CI: 88%, 97%). Also, the overall pooled AUC was 93% (95%CI: 89%, 96%).
CONCLUSION
Overall, AI models can further improve our diagnostic practice in MS patients. Our results indicate that the use of AI can aid the clinicians in accurate diagnosis of MS and improve current diagnostic approaches as most of the parameters including accuracy, sensitivity, specificity, precision, and AUC were considerably high, especially when using MRI data.
Topics: Humans; Artificial Intelligence; Multiple Sclerosis; Area Under Curve; Databases, Factual
PubMed: 36303065
DOI: 10.1007/s10072-022-06460-7 -
Artificial Intelligence in Medicine Sep 2022Acute respiratory distress syndrome (ARDS) is a life-threatening pulmonary disease with a high clinical and cost burden across the globe. Artificial intelligence (AI),... (Review)
Review
BACKGROUND AND OBJECTIVE
Acute respiratory distress syndrome (ARDS) is a life-threatening pulmonary disease with a high clinical and cost burden across the globe. Artificial intelligence (AI), an emerging area, has been used for various purposes in ARDS. We aim to summarize the currently available literature on various applications of AI in ARDS through a systematic review.
METHODOLOGY
PubMed was searched from inception to February 2021 to collate all the studies. Additionally, a bibliographic search of included studies and a random search on Google, Google Scholar, and Research Gate were performed to identify relevant articles. Studies published in English language that employed data about developing and/or assessing the role of AI in the various aspects of ARDS were considered for this review. Three independent reviewers performed study selection and data extraction; any disagreements were settled through consensus or discussion with another member of the research team.
RESULTS
A total of 19 studies published between the year 2002 and 2020 were included. In these included studies, AI was used for various purposes in ARDS such as diagnosis (n = 10; 53 %), risk stratification (n = 1; 5 %), prediction of severity (n = 3; 17 %), management (n = 2; 10 %), prediction of mortality (n = 2; 10 %), and decision making (n = 1; 5 %). The area under the curve among the developed models in the included studies ranged between 0.8 and 1, which is considered to be very good to excellent.
CONCLUSION
AI is revolutionizing healthcare and has a wide range of applications in ARDS, such as minimizing cost and enhancing outcomes.
Topics: Artificial Intelligence; Humans; Respiratory Distress Syndrome
PubMed: 36100348
DOI: 10.1016/j.artmed.2022.102361 -
Multiple Sclerosis and Related Disorders Mar 2022In recent years Artificial intelligence (AI) techniques are rapidly evolving into clinical practices such as diagnosis and prognosis processes, assess treatment... (Review)
Review
BACKGROUND
In recent years Artificial intelligence (AI) techniques are rapidly evolving into clinical practices such as diagnosis and prognosis processes, assess treatment effectiveness, and monitoring of diseases. The previous studies showed interesting results regarding the diagnostic efficiency of AI methods in differentiating Multiple sclerosis (MS) patients from healthy controls or other demyelinating diseases. There is a great lack of a comprehensive systematic review study on the role of AI in the diagnosis of MS. We aimed to perform a systematic review to document the performance of AI in MS diagnosis.
METHODS
A systematic search was performed using four databases including PubMed, Scopus, Web of Science, and IEEE on August 2021. All original studies which focused on deep learning or AI to analyze any modalities with the purpose of diagnosing MS were included in our study.
RESULTS
Finally, 38 studies were included in our systematic review after the abstract and full-text screening. A total of 5433 individuals were included, including 2924 cases of MS and 2509 healthy controls. Sensitivity and specificity were reported in 29 studies which ranged from 76.92 to 100 for sensitivity and 74 to 100 for specificity. Furthermore, 34 studies reported accuracy ranged 81 to 100. Among included studies, Magnetic Resonance Imaging (MRI) (20 studies), OCT (six studies), serum and cerebrospinal fluid markers (six studies), movement function (three studies), and other modalities such as breathing and evoked potential was used for detecting MS via AI.
CONCLUSION
In conclusion, diagnosis of MS based on new markers and AI is a growing field of research with MRI images, followed by images obtained from OCT, serum and CSF biomarkers, and motor associated markers. All of these results show that with advances made in AI, the way we monitor and diagnose our MS patients can change drastically.
Topics: Artificial Intelligence; Humans; Magnetic Resonance Imaging; Multiple Sclerosis
PubMed: 35180619
DOI: 10.1016/j.msard.2022.103673 -
Journal of Gastroenterology and... Mar 2024Discrimination of gastrointestinal tuberculosis (GITB) and Crohn's disease (CD) is difficult. Use of artificial intelligence (AI)-based technologies may help in...
BACKGROUND AND AIM
Discrimination of gastrointestinal tuberculosis (GITB) and Crohn's disease (CD) is difficult. Use of artificial intelligence (AI)-based technologies may help in discriminating these two entities.
METHODS
We conducted a systematic review on the use of AI for discrimination of GITB and CD. Electronic databases (PubMed and Embase) were searched on June 6, 2022, to identify relevant studies. We included any study reporting the use of clinical, endoscopic, and radiological information (textual or images) to discriminate GITB and CD using any AI technique. Quality of studies was assessed with MI-CLAIM checklist.
RESULTS
Out of 27 identified results, a total of 9 studies were included. All studies used retrospective databases. There were five studies of only endoscopy-based AI, one of radiology-based AI, and three of multiparameter-based AI. The AI models performed fairly well with high accuracy ranging from 69.6-100%. Text-based convolutional neural network was used in three studies and Classification and regression tree analysis used in two studies. Interestingly, irrespective of the AI method used, the performance of discriminating GITB and CD did not match in discriminating from other diseases (in studies where a third disease was also considered).
CONCLUSION
The use of AI in differentiating GITB and CD seem to have acceptable accuracy but there were no direct comparisons with traditional multiparameter models. The use of multiple parameter-based AI models have the potential for further exploration in search of an ideal tool and improve on the accuracy of traditional models.
Topics: Humans; Artificial Intelligence; Crohn Disease; Neural Networks, Computer; Retrospective Studies; Tuberculosis, Gastrointestinal; Diagnosis, Computer-Assisted
PubMed: 38058246
DOI: 10.1111/jgh.16430 -
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 -
BMC Medical Informatics and Decision... Aug 2023The purpose of this paper was to systematically evaluate the application value of artificial intelligence in predicting mortality among COVID-19 patients. (Meta-Analysis)
Meta-Analysis
BACKGROUND
The purpose of this paper was to systematically evaluate the application value of artificial intelligence in predicting mortality among COVID-19 patients.
METHODS
The PubMed, Embase, Web of Science, CNKI, Wanfang, China Biomedical Literature, and VIP databases were systematically searched from inception to October 2022 to identify studies that evaluated the predictive effects of artificial intelligence on mortality among COVID-19 patients. The retrieved literature was screened according to the inclusion and exclusion criteria. The quality of the included studies was assessed using the QUADAS-2 tools. Statistical analysis of the included studies was performed using Review Manager 5.3, Stata 16.0, and Meta-DiSc 1.4 statistical software. This meta-analysis was registered in PROSPERO (CRD42022315158).
FINDINGS
Of 2193 studies, 23 studies involving a total of 25 AI models met the inclusion criteria. Among them, 18 studies explicitly mentioned training and test sets, and 5 studies did not explicitly mention grouping. In the training set, the pooled sensitivity was 0.93 [0.87, 0.96], the pooled specificity was 0.94 [0.87, 0.97], and the area under the ROC curve was 0.98 [0.96, 0.99]. In the validation set, the pooled sensitivity was 0.84 [0.78, 0.88], the pooled specificity was 0.89 [0.85, 0.92], and the area under the ROC curve was 0.93 [1.00, 0.00]. In the subgroup analysis, the areas under the summary receiver operating characteristic (SROC) curves of the artificial intelligence models KNN, SVM, ANN, RF and XGBoost were 0.98, 0.98, 0.94, 0.92, and 0.91, respectively. The Deeks funnel plot indicated that there was no significant publication bias in this study (P > 0.05).
INTERPRETATION
Artificial intelligence models have high accuracy in predicting mortality among COVID-19 patients and have high prognostic value. Among them, the KNN, SVM, ANN, RF, XGBoost, and other models have the highest levels of accuracy.
Topics: Humans; Sensitivity and Specificity; Artificial Intelligence; COVID-19; ROC Curve; China
PubMed: 37559062
DOI: 10.1186/s12911-023-02256-7 -
Journal of Nephrology Sep 2022Transplant nephropathology is a highly specialized field of pathology comprising both the evaluation of organ donor biopsy for organ allocation and post-transplant graft... (Review)
Review
BACKGROUND
Transplant nephropathology is a highly specialized field of pathology comprising both the evaluation of organ donor biopsy for organ allocation and post-transplant graft biopsy for assessment of rejection or graft damage. The introduction of digital pathology with whole-slide imaging (WSI) in clinical research, trials and practice has catalyzed the application of artificial intelligence (AI) for histopathology, with development of novel machine-learning models for tissue interrogation and discovery. We aimed to review the literature for studies specifically applying AI algorithms to WSI-digitized pre-implantation kidney biopsy.
METHODS
A systematic search was carried out in the electronic databases PubMed-MEDLINE and Embase until 25th September, 2021 with a combination of the key terms "kidney", "biopsy", "transplantation" and "artificial intelligence" and their aliases. Studies dealing with the application of AI algorithms coupled with WSI in pre-implantation kidney biopsies were included. The main theme addressed was detection and quantification of tissue components. Extracted data were: author, year and country of the study, type of biopsy features investigated, number of cases, type of algorithm deployed, main results of the study in terms of diagnostic outcome, and the main limitations of the study.
RESULTS
Of 5761 retrieved articles, 7 met our inclusion criteria. All studies focused largely on AI-based detection and classification of glomerular structures and to a lesser extent on tubular and vascular structures. Performance of AI algorithms was excellent and promising.
CONCLUSION
All studies highlighted the importance of expert pathologist annotation to reliably train models and the need to acknowledge clinical nuances of the pre-implantation setting. Close cooperation between computer scientists and practicing as well as expert renal pathologists is needed, helping to refine the performance of AI-based models for routine pre-implantation kidney biopsy clinical practice.
Topics: Algorithms; Artificial Intelligence; Biopsy; Humans; Intelligence; Kidney
PubMed: 35441256
DOI: 10.1007/s40620-022-01327-8