Artificial Intelligence Screening Research Articles

Using artificial intelligence to support and streamline rapid systematic evidence reviews

Abstract Issue The Rapid Evidence Service, initiated by the National Collaborating Centre for Methods and Tools (NCCMT) during COVID-19, supports public health decision making by conducting rapid reviews on priority topics. Description of issue Integral to rapid reviews is an expedited timeline but the quantity of available literature for most public health review questions takes significant time to screen manually. NCCMT integrated 4 artificial intelligence (AI) features into the screening process. DAISY Rank applies predictions learned from manual screening patterns to re-order remaining studies, with most relevant appearing first. AI Screening automatically screens remaining studies based on prediction scores. Check for Screening Errors and Re-Rank Report use previous screening patterns to identify studies that were potentially falsely excluded and predict the total number of included studies, respectively. These features were tested by comparing results provided by AI with those produced manually for select test sets. Results NCCMT used AI to support and expedite screening, assess screening progress, and/or minimize risk of inappropriately excluding studies for 35 rapid reviews on 20 topics. Using DAISY Rank enabled one screener to review over 4000 references in 9 hours, compared to a different review, where the same amount of screening took 28 hours without DAISY Rank. AI Screening correctly excluded up to 80% of irrelevant search results across reviews. Check for Screening Errors identified 37 potential includes manually excluded in one review; these were reviewed and 3 were included. Re-Rank Report allowed NCCMT to re-allocate staff to subsequent steps in the review process when most included studies were identified. Lessons Integrating AI features into screening led to less time required, better anticipated timelines, more accurate staff allocation and reduced errors. More rigorous study of AI best practices is needed to continue to improve rapid review method efficiencies. Key messages • Rapid reviews can be an important source of evidence for decision makers if they can be completed quickly but maintain rigor and accuracy. • AI holds promise as a way to improve screening efficiency.

Evaluation of the Clinical Impact of a Smartphone Application for Cataract Detection.

Background Approximately 10 million people in India suffer from bilateral blindness, with cataracts accounting for roughly 70% of these cases. However, there is a severe scarcity of ophthalmologists in India (12,000 across the country), which makes routine cataract screening very difficult, particularly in rural areas. To tackle this problem, we investigated the use of an artificial intelligence (AI)-based application for cataract screening at All India Institute of Medical Sciences (AIIMS), Bibinagar, that can be used by nursing officers and other healthcare professionals as a primary screening tool. Ophthalmologists from AIIMS Bibinagar additionally validate the results of this application. Purpose The aim of this study was to assess the clinical performance of a smartphone-based cataract screening application that uses an AI module to identify cataracts in photos taken with the device's camera. The study compared the application's results with diagnoses made by ophthalmologists using a slit lamp. Methods At AIIMS Bibinagar, 495 patients participated in a prospective clinical trial. The AI-based screening solution examined smartphone images that were taken in accordance with a set protocol to identify whether cataracts were present. The results of the application were then compared with the diagnoses made by ophthalmologists based on slit-lamp tests. Results The study included 990 eye images. The AI screening application demonstrated an overall accuracy of 90.01% for cataract detection. Specific metrics include a sensitivity of 89.50%, specificity of 89.73%, precision of 91.43%, and an F1 score of 90.36%. The positive predictive value (PPV) was approximately 91.3%, based on 485 true positives and 46 false positives. The negative predictive value (NPV) was approximately 87.6%, based on 402 true negatives and 57 false negatives. Conclusions The smartphone-based cataract screening application proves to be an effective tool for community-level cataract screening in remote areas where access to expensive equipment and specialized ophthalmic care is limited. Its high accuracy and efficiency make it a valuable option for low-resource settings and suitable for home screening, particularly in the post-COVID era.

Emerging challenges in the application of artificial intelligence for the eye disease screening in Chinese primary healthcare institutions

Breakthroughs have been achieved recently in the application of artificial intelligence (AI) for the eye disease screening in Chinese primary healthcare institutions, but challenges have also emerged. First, AI software has continuously evolved, expanding the range of eye diseases that can be screened, enhancing diagnostic accuracy, and progressing towards predicting the course of eye diseases. However, inadequate infrastructure such as 5G and a shortage of specialized personnel have hindered the coverage of screenings. Second, while the cost-effectiveness of AI is well-established, new screening models have impacted the equity of screenings. It is essential to tailor AI application models to the specific context of China. Third, AI screening guidelines have been increasingly improved, providing direction for AI development and reference for the promotion and application of AI technologies. Nonetheless, high-quality empirical research is urgently needed to provide scientific evidence for policymaking related to AI in the eye disease screening. Therefore, it is suggested to develop multimodal AI models that integrate basic data such as symptoms and medical history with simple ophthalmic examinations, to accelerate the construction of infrastructure like 5G and focus on cultivating interdisciplinary talents, to explore suitable service systems and models for the large-scale eye disease screening tailored to local conditions, and to conduct long-term, multi-center, empirical studies.

Artificial intelligence for screening and early diagnosis of pancreatic neoplasms in the context of centralization of the laboratory service in the region.

Objective. Determination of the optimal machine learning model for the creation of software for screening and early diagnosis of pancreatic neoplasms in the context of centralization of the laboratory service in the region. Material and Methods. The clinical material was based on 1254 patients who were examined in the centralized laboratory of the Volgograd Consultative and Diagnostic Polyclinic No. 2. Of these, 139 were subsequently operated on at the Volgograd Regional Clinical Oncology Dispensary for pancreatic malignancies. In 65 (46.7 %) cases, distal pancreatic resection was performed, and in 74 (53.3 %) cases, pancreaticoduodenectomy was performed. In 28 (20.1 %) cases, at the time of tumor detection, patients did not have clinical symptoms. Statistical processing of the data was carried out using the Python programming language. Five different classifiers were used for machine learning. Results. In the course of factor analysis, 11 parameters were selected from 62 laboratory blood parameters, the dynamics of changes in which should be specifically assessed at the stages of screening and early diagnosis of pancreatic neoplasms. A comparative assessment of machine learning techniques showed that the best option for creating the appropriate software was Hist Gradient Boosting (diagnostic accuracy 0.909, sensitivity 0.642, specificity 0.965, negative predictability 0.928, positive predictability 0.794, F1 0.828, logistic regression loss function 0.352, area under the ROC curve 0.89). Conclusion. The creation of software based on the selected algorithm will make it possible to clarify the real effectiveness of machine learning on a larger population of patients with pancreatic neoplasms.

P-554 Artificial intelligence (AI) for embryo genetic risk assessment: a novel form of preimplantation genetic screening

Abstract Study question Can an AI-based embryo ploidy screening tool effectively provide a genetic risk assessment on Day 5 embryos? Summary answer AI screening results showed strong predictive value for early detection of at-risk embryos warranting confirmatory diagnostic testing, based only on time lapse video assessment. What is known already Euploid embryo transfer is the dogma of IVF success. Embryo evaluation relies extensively on diagnostic preimplantation genetic testing for aneuploidy (PGT-A) by means of trophectoderm biopsy. Though pivotal, this presents substantial practical and financial limitations. There is demand for an alternative genetic screening method that provides a quantitative embryo genetic risk assessment for patients who opt out of invasive diagnostic testing, or who intend to undergo fresh transfer. These results can provide information for understanding your aneuploidy risk for more precise prognosis counseling and testing. This is important since diagnostic tests may incur excess financial burden, inconclusive outcomes, and time-to-results. Study design, size, duration Time lapse videos (N = 5,000) were used for AI training. The AI core architecture was a video transformer model for spatio-temporal video sampling. Known ploidy and live-birth results were used as ground truth, with maternal age and embryo quality scores incorporated as input parameters during calibrated logistic regression. A blind test dataset (N = 708 embryos; euploid=352; aneuploid=356; mean ± SD patient age: 35.9 ± 5.4 years) was used to evaluate predictive accuracy. Participants/materials, setting, methods The AI outputs a scalar (1-99) that associates with euploidy likelihood. The AI’s screening value was determined by its specificity (correct prediction of aneuploid), sensitivity (correct prediction of euploid), false positive, and false negative rate. We determined optimal AI score thresholds that identified high aneuploidy and euploidy risk categories (AI score <33; AI score >66, respectively). Main results and the role of chance Specificity of the AI screening test was 83.0%, with a sensitivity of 56.0%. The false positive rate was 26.7% and the false negative rate was 29.3%. The AI’s high specificity relative to its sensitivity shows its clinical value to deselect embryos with the strongest risk of genetic abnormality, and for the prioritization of embryos for confirmatory PGT. Boxplot visualization of the full AI score distribution showed successful discrimination between aneuploid and euploid embryos (mean AI score for aneuploid and euploid were 51.3 and 43.5, respectively) with appropriately minimized overlapping of the interquartile range. The optimal AI score thresholds for detecting embryos with the highest and lowest likelihoods of euploidy were >66 /<33, respectively, representing 71% likelihood of euploidy for the >66 group and 72% aneuploidy for the <33 group. Limitations, reasons for caution The screening does not provide diagnostic genetic testing on the chromosome level. The influence of mosaicism on the false positive/negative rates was not assessed. Wider implications of the findings Results support the use of an AI ploidy screening test for effective decision-making and triage of at-risk embryos for transfer or confirmatory diagnostic testing via PGT-A. Trial registration number Not applicable

A pilot cost-analysis study comparing AI-based EyeArt® and ophthalmologist assessment of diabetic retinopathy in minority women in Oslo, Norway

BackgroundDiabetic retinopathy (DR) is the leading cause of adult blindness in the working age population worldwide, which can be prevented by early detection. Regular eye examinations are recommended and crucial for detecting sight-threatening DR. Use of artificial intelligence (AI) to lessen the burden on the healthcare system is needed.PurposeTo perform a pilot cost-analysis study for detecting DR in a cohort of minority women with DM in Oslo, Norway, that have the highest prevalence of diabetes mellitus (DM) in the country, using both manual (ophthalmologist) and autonomous (AI) grading. This is the first study in Norway, as far as we know, that uses AI in DR- grading of retinal images.MethodsOn Minority Women’s Day, November 1, 2017, in Oslo, Norway, 33 patients (66 eyes) over 18 years of age diagnosed with DM (T1D and T2D) were screened. The Eidon - True Color Confocal Scanner (CenterVue, United States) was used for retinal imaging and graded for DR after screening had been completed, by an ophthalmologist and automatically, using EyeArt Automated DR Detection System, version 2.1.0 (EyeArt, EyeNuk, CA, USA). The gradings were based on the International Clinical Diabetic Retinopathy (ICDR) severity scale [1] detecting the presence or absence of referable DR. Cost-minimization analyses were performed for both grading methods.Results33 women (64 eyes) were eligible for the analysis. A very good inter-rater agreement was found: 0.98 (P < 0.01), between the human and AI-based EyeArt grading system for detecting DR. The prevalence of DR was 18.6% (95% CI: 11.4–25.8%), and the sensitivity and specificity were 100% (95% CI: 100–100% and 95% CI: 100–100%), respectively. The cost difference for AI screening compared to human screening was $143 lower per patient (cost-saving) in favour of AI.ConclusionOur results indicate that The EyeArt AI system is both a reliable, cost-saving, and useful tool for DR grading in clinical practice.

The Impact of Artificial Intelligence Replacing Humans in Making Human Resource Management Decisions on Fairness: A Case of Resume Screening

A growing number of organizations have used artificial intelligence (AI) to make decisions to replace human resource (HR) workers; yet, the fairness perceptions of the people affected by the decision are still unclear. Given that an organization’s sustainability is significantly influenced by individuals’ perceptions of fairness, this study takes a resume-screening scenario as an example to explore the impact of AI replacing humans on applicants’ perceptions of fairness. This study adopts the method of the online scenario experiment and uses SPSS to analyze the experimental data: 189 and 214 people, respectively, participated in two online scenarios, with two independent variables of decision makers (AI and humans), two dependent variables of procedural and distributive fairness, and two moderating variables of outcome favorability and the expertise of AI. The results show that the applicants tend to view AI screening resumes as less fair than humans. Furthermore, moderating effects exist between the outcome favorability and the expertise of AI. This study reveals the impact of AI substituting for humans in decision-making on fairness. The proposed model can help organizations use AI to screen resumes more effectively. And future research can explore the collaboration between humans and AI to make human resource management decisions.

Cost-effectiveness and cost-utility of a digital technology-driven hierarchical healthcare screening pattern in China

Utilization of digital technologies for cataract screening in primary care is a potential solution for addressing the dilemma between the growing aging population and unequally distributed resources. Here, we propose a digital technology-driven hierarchical screening (DH screening) pattern implemented in China to promote the equity and accessibility of healthcare. It consists of home-based mobile artificial intelligence (AI) screening, community-based AI diagnosis, and referral to hospitals. We utilize decision-analytic Markov models to evaluate the cost-effectiveness and cost-utility of different cataract screening strategies (no screening, telescreening, AI screening and DH screening). A simulated cohort of 100,000 individuals from age 50 is built through a total of 30 1-year Markov cycles. The primary outcomes are incremental cost-effectiveness ratio and incremental cost-utility ratio. The results show that DH screening dominates no screening, telescreening and AI screening in urban and rural China. Annual DH screening emerges as the most economically effective strategy with 341 (338 to 344) and 1326 (1312 to 1340) years of blindness avoided compared with telescreening, and 37 (35 to 39) and 140 (131 to 148) years compared with AI screening in urban and rural settings, respectively. The findings remain robust across all sensitivity analyses conducted. Here, we report that DH screening is cost-effective in urban and rural China, and the annual screening proves to be the most cost-effective option, providing an economic rationale for policymakers promoting public eye health in low- and middle-income countries.

Clinical and genetic studies for a cohort of patients with Leber congenital amaurosis

PurposeLeber congenital amaurosis (LCA) is a group of early-onset retinal degenerative disorders, resulting in blindness in children. This study aimed to describe the clinical and genetic characteristics of a cohort of patients with LCA and to investigate the retinal vascular characteristics in LCA patients.MethodsFifty-two children with LCA were included in the study. All patients underwent detailed ocular examinations. Electroretinography (ERG) was used to evaluate the retinal function. Optical coherence tomography (OCT) was used to assess the structure change of the retina for those patients who were able to cooperate very well. Panel-based next-generation sequencing was performed to identify pathogenic variants in genes associated with LCA. Diameters of the retinal vessels were measured using the EVision AI screening system with an artificial intelligence (AI) technique. An ultrasound Doppler was used to evaluate hemodynamic parameters, including peak systolic velocity (PSV), resistive index (RI), and pulsatility index (PI), in the ophthalmic, central retinal, posterior ciliary, carotid, and internal carotid as well as external carotid arteries in 12 patients aged from 3 to 14 years.ResultsWe detected 75 pathogenic variants from ten genes of RPGRIP1, CEP290, GUCY2D, LCA5, AIPL1, CRB1, RPE65, CRX, RDH12, and TULP1, including 29 novel and 36 previously reported variants in 52 affected children with LCA, with the highest detective rate in RPGRIP1 (26.9%). Fundus appearance is diverse in patients with LCA, ranging from normal to severe peripheral or central retinopathy. Retinal vasculature was evaluated in 12 patients with different gene variants, showing narrowed arteries with an average diameter of 43.6 ± 3.8 μm compared to that of 51.7 ± 2.6 μm in the normal controls (P < 0.001, n = 12). Meanwhile, their hemodynamic parameters were changed as well in the ophthalmic artery (OA), with a decreased PSV (P = 0.0132, n = 12) and slightly increased PI (P = 0.0488, n = 12) compared to the normal controls. However, the hemodynamic parameters did not change significantly in the other vessels.ConclusionsBlood supply to the eyeball is predicted to be reduced in patients with LCA, presumably due to photoreceptor cell degeneration. The novel identified variants will expand the spectrum of variants in LCA-related genes and be useful for studying the molecular mechanisms of LCA.

An Economic Analysis for the Use of Artificial Intelligence in Screening for Diabetic Retinopathy in Trinidad and Tobago.

This is a systematic review of 25 publications on the topics of the prevalence and cost of diabetic retinopathy (DR) in Trinidad and Tobago, the cost of traditional methods of screening for DR, and the use and cost of artificial intelligence (AI) in screening for DR. Analysis of these publications was used to identify and make estimates for how resources allocated to ophthalmology in public health systems in Trinidad and Tobago can be more efficiently utilized by employing AI in diagnosing treatable DR. DR screening was found to be an effective method of detecting the disease. Screening wasfound to be a universally cost-effective method of disease prevention and for altering the natural history of the disease in the spectrum of low-middle to high-income economies, such as Rwanda, Thailand, China, South Korea, and Singapore. AI and deep learning systems were found to be clinically superior to, or as effective as, human graders in areas where they were deployed, indicating that the systems are clinically safe. They have been shown to improve access to diabetic retinal screening, improve compliance with screening appointments, and prove to be cost-effective, especially in rural areas. Trinidad and Tobago, which is estimated to be disproportionately more affected by the burden of DR when projected out to the mid-21st century, stands to save as much as US$60 million annually from the implementation of an AI-based system to screen for DR versus conventional manual grading.

Artificial intelligence in screening and diagnosis of surgical diseases: A narrative review.

Artificial intelligence (AI) is playing an increasing role in several fields of medicine. It is also gaining popularity among surgeons as a valuable screening and diagnostic tool for many conditions such as benign and malignant colorectal, gastric, thyroid, parathyroid, and breast disorders. In the literature, there is no review that groups together the various application domains of AI when it comes to the screening and diagnosis of main surgical diseases. The aim of this review is to describe the use of AI in these settings. We performed a literature review by searching PubMed, Web of Science, Scopus, and Embase for all studies investigating the role of AI in the surgical setting, published between January 01, 2000, and June 30, 2023. Our focus was on randomized controlled trials (RCTs), meta-analysis, systematic reviews, and observational studies, dealing with large cohorts of patients. We then gathered further relevant studies from the reference list of the selected publications. Based on the studies reviewed, it emerges that AI could strongly enhance the screening efficiency, clinical ability, and diagnostic accuracy for several surgical conditions. Some of the future advantages of this technology include implementing, speeding up, and improving the automaticity with which AI recognizes, differentiates, and classifies the various conditions.

Diagnostic test accuracy of artificial intelligence in screening for referable diabetic retinopathy in real-world settings: A systematic review and meta-analysis.

Retrospective studies on artificial intelligence (AI) in screening for diabetic retinopathy (DR) have shown promising results in addressing the mismatch between the capacity to implement DR screening and increasing DR incidence. This review sought to evaluate the diagnostic test accuracy (DTA) of AI in screening for referable diabetic retinopathy (RDR) in real-world settings. We searched CENTRAL, PubMed, CINAHL, Scopus, and Web of Science on 9 February 2023. We included prospective DTA studies assessing AI against trained human graders (HGs) in screening for RDR in patients with diabetes. Two reviewers independently extracted data and assessed methodological quality against QUADAS-2 criteria. We used the hierarchical summary receiver operating characteristics (HSROC) model to pool estimates of sensitivity and specificity and, forest plots and SROC plots to visually examine heterogeneity in accuracy estimates. From our initial search results of 3899 studies, we included 15 studies comprising 17 datasets. Meta-analyses revealed a sensitivity of 95.33% (95%CI: 90.60-100%) and specificity of 92.01% (95%CI: 87.61-96.42%) for patient-level analysis (10 datasets, N = 45,785) while, for the eye-level analysis, sensitivity was 91.24% (95%CI: 79.15-100%) and specificity, 93.90% (95%CI: 90.63-97.16%) (7 datasets, N = 15,390). Subgroup analyses did not provide variations in the diagnostic accuracy of country classification and DR classification criteria. However, a moderate increase was observed in diagnostic accuracy in the primary-level healthcare settings: sensitivity of 99.35% (95%CI: 96.85-100%), specificity of 93.72% (95%CI: 88.83-98.61%) and, a minimal decrease in the tertiary-level healthcare settings: sensitivity of 94.71% (95%CI: 89.00-100%), specificity of 90.88% (95%CI: 83.22-98.53%). Sensitivity analyses did not show any variations in studies that included diabetic macular edema in the RDR definition, nor studies with ≥3 HGs. This review provides evidence, for the first time from prospective studies, for the effectiveness of AI in screening for RDR in real-world settings. The results may serve to strengthen existing guidelines to improve current practices.

Characteristics of a Large, Labeled Data Set for the Training of Artificial Intelligence for Glaucoma Screening with Fundus Photographs.

Significant visual impairment due to glaucoma is largely caused by the disease being detected too late. To build a labeled data set for training artificial intelligence (AI) algorithms for glaucoma screening by fundus photography, to assess the accuracy of the graders, and to characterize the features of all eyes with referable glaucoma (RG). Cross-sectional study. Color fundus photographs (CFPs) of 113 893 eyes of 60 357 individuals were obtained from EyePACS, California, United States, from a population screening program for diabetic retinopathy. Carefully selected graders (ophthalmologists and optometrists) graded the images. To qualify, they had to pass the European Optic Disc Assessment Trial optic disc assessment with ≥ 85% accuracy and 92% specificity. Of 90 candidates, 30 passed. Each image of the EyePACS set was then scored by varying random pairs of graders as "RG," "no referable glaucoma (NRG)," or "ungradable (UG)." In case of disagreement, a glaucoma specialist made the final grading. Referable glaucoma was scored if visual field damage was expected. In case of RG, graders were instructed to mark up to 10 relevant glaucomatous features. Qualitative features in eyes with RG. The performance of each grader was monitored; if the sensitivity and specificity dropped below 80% and 95%, respectively (the final grade served as reference), they exited the study and their gradings were redone by other graders. In all, 20 graders qualified; their mean sensitivity and specificity (standard deviation [SD]) were 85.6% (5.7) and 96.1% (2.8), respectively. The 2 graders agreed in 92.45% of the images (Gwet's AC2, expressing the inter-rater reliability, was 0.917). Of all gradings, the sensitivity and specificity (95% confidence interval) were 86.0 (85.2-86.7)% and 96.4 (96.3-96.5)%, respectively. Of all gradable eyes (n= 111 183; 97.62%) the prevalence of RG was 4.38%. The most common features of RG were the appearance of the neuroretinal rim (NRR) inferiorly and superiorly. A large data set of CFPs was put together of sufficient quality to develop AI screening solutions for glaucoma. The most common features of RG were the appearance of the NRR inferiorly and superiorly. Disc hemorrhages were a rare feature of RG. Proprietary or commercial disclosure may be found after the references.

Feasibility and acceptance of artificial intelligence-based diabetic retinopathy screening in Rwanda

BackgroundEvidence on the practical application of artificial intelligence (AI)-based diabetic retinopathy (DR) screening is needed.MethodsConsented participants were screened for DR using retinal imaging with AI interpretation from March 2021 to...

Remote diagnostic imaging using artificial intelligence for diagnosing hip dysplasia in infants: Results from a mixed-methods feasibility pilot study.

Infant hip dysplasia or Developmental Dysplasia of the Hip (DDH) occurs in 1-2% of births worldwide and leads to hip arthritis if untreated. We sought to evaluate the feasibility of implementing an artificial intelligence-enhanced portable ultrasound tool for infant hip dysplasia (DDH) screening in primary care, through determining its effectiveness in practice and evaluating patient and provider feedback. A US-FDA-cleared artificial intelligence (AI) screening device for DDH (MEDO-Hip) was added to routine well-child visits from age 6 to 10 weeks. A total of 306 infants were screened during a 1-year pilot study within three family medicine clinics in Alberta, Canada. Patient and provider satisfaction were quantified using the System Usability Survey (SUS), while provider perceptions were further investigated through semi-structured interviews. Provider and user surveys commonly identified best features of the tool as immediate diagnosis, offering reassurance/knowledge and avoiding travel, and noted technical glitches most frequently as a barrier. A total of 369 scans of 306 infants were performed from Feb 1, 2021 until Mar 31, 2022. Eighty percent of hips scanned were normal on initial scans, 14% of scans required a follow-up study in the primary care clinic, and DDH cases were identified and treated at the expected 2% rate (6 infants). It is feasible to implement a point-of-care ultrasound AI screening tool in primary care to screen for infants with DDH. Beyond improved screening and detection, this innovation was well accepted by patients and fee-for-service providers with a culture and history of innovation.

Readiness for Artificial Intelligence in Biobanking

Readiness for Artificial Intelligence in Biobanking

An overview of artificial intelligence use in diabetic retinopathy treatment: a narrative review

Background: Artificial intelligence (AI), usually called machine intelligence, is a scientific field that allows robots thinking like people. The most common diseases that cause visual impairment and blindness, such as age-related macular degeneration (ARMD), cataract, glaucoma, and diabetic retinopathy (DR), have recently been subject to deep learning-based on AI screening and prediction models. Aim: to discuss the core ideas of AI and its apply to DR, as well as to evaluate the currently faced issues and the future of ophthalmology. Methods: In this review, English studies from common databases such as Pubmed/MEDLINE, Google Scholar, Web of Science, Scopus, and the Cochrane Library with the keywords "Machine Learning," "Artificial Intelligence," "Deep Learning," combined with keywords, involving "diabetic retinopathy" were involved. The end date for this review is April 2022. Scientific novelty: This paper illustrates the core AI concepts and their application in diabetic retinopathy. The current ophthalmology issues and future opportunities, offering undiscovered knowledge in this field are also analysed. It will raise community knowledge of employing AI and reveal new capabilities in the analysis of ocular disorders to present the fundamental idea of AI regarding its therapeutic applications. Conclusion: Medical professionals can make quick and precise decisions using AI technologies in orded to analyse massive volumes of data, such as physiological imaging and clinical presentations. It is believed that over time, AI systems will become more precise and successful at predicting the onset and course of DR.

Artificial intelligence in screening for obstructive sleep apnoea syndrome (OSAS): a narrative review

Artificial intelligence in screening for obstructive sleep apnoea syndrome (OSAS): a narrative review

Leveraging artificial intelligence for the management of postoperative delirium following cardiac surgery

BACKGROUND Postoperative delirium is a highly relevant complication of cardiac surgery. It is associated with worse outcomes and considerably increased costs of care. A novel approach of monitoring patients with machine learning enabled prediction software could trigger pre-emptive implementation of mitigation strategies as well as timely intervention. OBJECTIVE This study evaluates the predictive accuracy of an artificial intelligence (AI) model for anticipating postoperative delirium by comparing it to established standards and measures of risk and vulnerability. DESIGN Retrospective predictive accuracy study. SETTING Records were gathered from a database for anaesthesia quality assurance at a specialised heart surgery centre in Germany. PATIENTS Between January and July 2021, 131 patients had been enrolled into the database and had data available for AI prediction modelling. After exclusion of incomplete follow-ups, a subset of 114 was included in the statistical analysis. MAIN OUTCOME MEASURES Delirium was diagnosed with the Confusion Assessment Method for the ICU (CAM-ICU) over three days postoperatively with specific follow-up visits. AI predictions were also compared with risk assessment through a frailty screening, a Shulman Clock Drawing Test, and using a checklist of predisposing factors including comorbidity, reduced mobility, and substance abuse. RESULTS Postoperative delirium was diagnosed in 23.7% of patients. Postoperative AI screening exhibited reasonable performance with an area under the receiver operating curve (AUROC) of 0.79, 95% confidence interval (CI), 0.69–0.87. But pre-operative prediction was weak for all methods (AUROC range from 0.55 to 0.66). There were significant associations with postoperative delirium: open heart surgery versus endovascular valve replacement (33.3% vs. 10.4%, P &lt; 0.01), postinterventional hospitalisation (12.8 vs. 8.6 days, P &lt; 0.01), and length of ICU stay (1.7 vs. 0.3 days, P &lt; 0.01) were all significantly associated with postoperative delirium. CONCLUSION AI is a promising approach with considerable potential and delivered noninferior results compared with the usual approach of structured evaluation of risk factors and questionnaires. Since these established methods do not provide the desired confidence level, improved AI may soon deliver a better performance. TRIAL REGISTRATION None.

AI telemedicine screening in ophthalmology: health economic considerations

AI telemedicine screening in ophthalmology: health economic considerations