Artificial intelligence in diabetic retinopathy: Bibliometric analysis.

BACKGROUND: The use of artificial intelligence in diabetic retinopathy has become a popular research focus in the past decade. However, no scientometric report has provided a systematic overview of this scientific area. AIMS: We utilized a bibliometric approach to identify and analyse the academic literature on artificial intelligence in diabetic retinopathy and explore emerging research trends, key authors, co-authorship networks, institutions, countries, and journals. We further captured the diabetic retinopathy conditions and technology commonly used within this area. METHODS: Web of Science was used to collect relevant articles on artificial intelligence use in diabetic retinopathy published between January 1, 2012, and December 31, 2022 . All the retrieved titles were screened for eligibility, with one criterion that they must be in English. All the bibliographic information was extracted and used to perform a descriptive analysis. Bibliometrix (R tool) and VOSviewer (Leiden University) were used to construct and visualize the annual numbers of publications, journals, authors, countries, institutions, collaboration networks, keywords, and references. RESULTS: In total, 931 articles that met the criteria were collected. The number of annual publications showed an increasing trend over the last ten years. Investigative Ophthalmology & Visual Science (58/931), IEEE Access (54/931), and Computers in Biology and Medicine (23/931) were the most journals with most publications. China (211/931), India (143/931, USA (133/931), and South Korea (44/931) were the most productive countries of origin. The National University of Singapore (40/931), Singapore Eye Research Institute (35/931), and Johns Hopkins University (34/931) were the most productive institutions. Ting D. (34/931), Wong T. (28/931), and Tan G. (17/931) were the most productive researchers. CONCLUSION: This study summarizes the recent advances in artificial intelligence technology on diabetic retinopathy research and sheds light on the emerging trends, sources, leading institutions, and hot topics through bibliometric analysis and network visualization. Although this field has already shown great potential in health care, our findings will provide valuable clues relevant to future research directions and clinical practice.

Improved diagnosis-medication association mining to reduce pseudo-associations.

BACKGROUND AND OBJECTIVE: Association rule mining has been adopted to medical fields to discover prescribing patterns or relationships among diseases and/or medications; however, it has generated unreasonable associations among these entities. This study aims to identify the real-world profile of disease-medication (DM) associations using the modified mining algorithm and assess its performance in reducing DM pseudo-associations. METHODS: We retrieved data from outpatient records between January 2011 and December 2015 in claims databases maintained by the Health and Welfare Data Science Center, Ministry of Health and Welfare, Taiwan. The association rule mining's lift (Q-value) was adopted to quantify DM associations, referred to as Q1 for the original algorithm and as Q2 for the modified algorithm. One thousand DM pairs with positive Q1-values (Q1+) and negative or no Q2-values (Q2- or Q2∅) were selected as the validation dataset, in which two pharmacists assessed the DM associations. RESULTS: A total of 3,120,449 unique DM pairs were identified, of which there were 333,347 Q1+Q2- pairs and 429,931 Q1+Q2∅ pairs. Q1+Q2- rates were relatively high in ATC classes C (29.91%) and R (30.24%). Classes L (69.91%) and V (52.52%) demonstrated remarkably high Q1+Q2∅ rates. For the 1000 pairs in the validation, 93.7% of the Q1+Q2- or Q1+Q2∅ DM pairs were assessed as pseudo-associations. However, classes M (5.3%), H (4.5%), and B (4.1%) showed the highest rates of plausible associations falsely given Q2- or Q2∅ by the modified algorithm. CONCLUSIONS: The modified algorithm demonstrated high accuracy to identify pseudo-associations regarded as positive associations by the original algorithm and would potentially be applied to improve secondary databases to facilitate research on real-world prescribing patterns and further enhance drug safety.

Obesity and Mortality Among Patients Diagnosed With COVID-19: A Systematic Review and Meta-Analysis.

Coronavirus disease 2019 (COVID-19) has already raised serious concern globally as the number of confirmed or suspected cases have increased rapidly. Epidemiological studies reported that obesity is associated with a higher rate of mortality in patients with COVID-19. Yet, to our knowledge, there is no comprehensive systematic review and meta-analysis to assess the effects of obesity and mortality among patients with COVID-19. We, therefore, aimed to evaluate the effect of obesity, associated comorbidities, and other factors on the risk of death due to COVID-19. We did a systematic search on PubMed, EMBASE, Google Scholar, Web of Science, and Scopus between January 1, 2020, and August 30, 2020. We followed Cochrane Guidelines to find relevant articles, and two reviewers extracted data from retrieved articles. Disagreement during those stages was resolved by discussion with the main investigator. The random-effects model was used to calculate effect sizes. We included 17 articles with a total of 543,399 patients. Obesity was significantly associated with an increased risk of mortality among patients with COVID-19 (RRadjust: 1.42 (95%CI: 1.24-1.63, p < 0.001). The pooled risk ratio for class I, class II, and class III obesity were 1.27 (95%CI: 1.05-1.54, p = 0.01), 1.56 (95%CI: 1.11-2.19, p < 0.01), and 1.92 (95%CI: 1.50-2.47, p < 0.001), respectively). In subgroup analysis, the pooled risk ratio for the patients with stroke, CPOD, CKD, and diabetes were 1.80 (95%CI: 0.89-3.64, p = 0.10), 1.57 (95%CI: 1.57-1.91, p < 0.001), 1.34 (95%CI: 1.18-1.52, p < 0.001), and 1.19 (1.07-1.32, p = 0.001), respectively. However, patients with obesity who were more than 65 years had a higher risk of mortality (RR: 2.54; 95%CI: 1.62-3.67, p < 0.001). Our study showed that obesity was associated with an increased risk of death from COVID-19, particularly in patients aged more than 65 years. Physicians should aware of these risk factors when dealing with patients with COVID-19 and take early treatment intervention to reduce the mortality of COVID-19 patients.

Use of Mobile Phone App Interventions to Promote Weight Loss: Meta-Analysis.

BACKGROUND: Obesity and lack of physical activity are major health risk factors for many life-threatening diseases, such as cardiovascular diseases, type 2 diabetes, and cancer. The use of mobile app interventions to promote weight loss and boost physical activity among children and adults is fascinating owing to the demand for cutting-edge and more efficient interventions. Previously published studies have examined different types of technology-based interventions and their impact on weight loss and increase in physical activity, but evidence regarding the impact of only a mobile phone app on weight loss and increase in physical activity is still lacking. OBJECTIVE: The main objective of this study was to assess the efficacy of a mobile phone app intervention for reducing body weight and increasing physical activity among children and adults. METHODS: PubMed, Google Scholar, Scopus, EMBASE, and the Web of Science electronic databases were searched for studies published between January 1, 2000, and April 30, 2019, without language restrictions. Two experts independently screened all the titles and abstracts to find the most appropriate studies. To be included, studies had to be either a randomized controlled trial or a case-control study that assessed a mobile phone app intervention with body weight loss and physical activity outcomes. The Cochrane Collaboration Risk of Bias tool was used to examine the risk of publication bias. RESULTS: A total of 12 studies involving a mobile phone app intervention were included in this meta-analysis. Compared with the control group, the use of a mobile phone app was associated with significant changes in body weight (-1.07 kg, 95% CI -1.92 to -0.21, P=.01) and body mass index (-0.45 kg/m2, 95% CI -0.78 to -0.12, P=.008). Moreover, a nonsignificant increase in physical activity was observed (0.17, 95% CI -2.21 to 2.55, P=.88). CONCLUSIONS: The findings of this study demonstrate the promising and emerging efficacy of using mobile phone app interventions for weight loss. Future studies are needed to explore the long-term efficacy of mobile app interventions in larger samples.

Statin Use and the Risk of Hepatocellular Carcinoma: A Meta-Analysis of Observational Studies.

Background and Aims: Statins are the first-line medication to treating hypercholesterolemia. Several studies have investigated the impact of statins on the risk of hepatocellular carcinoma (HCC). However, the extent to which statins may prevent HCC remains uncertain. Therefore, we performed a meta-analysis of relevant studies to quantify the magnitude of the association between statins use and the risk of HCC. Methods: A systematic literature search of PubMed, EMBASE, Google Scholar, Web of Science, and Scopus was performed for studies published between January 1, 1990, and September 1, 2019, with no restriction of language. Two reviewers independently evaluated the literature and included observational and experimental studies that reported the association between statin use and HCC risk. The random-effect model was used to calculate the overall risk ratio (RR) with a 95% confidence interval (CI), and the heterogeneity among the studies was assessed using the Q statistic and I2 statistic. The Newcastle Ottawa Scale (NOS) was also used to evaluate the quality of the included studies. Results: A total of 24 studies with 59,073 HCC patients was identified. Statin use was associated with a reduced risk of HCC development (RR: 0.54, 95% CI: 0.47-0.61, I2 = 84.39%) compared with nonusers. Moreover, the rate of HCC reduction was also significant among patients with diabetes (RR: 0.44, 95% CI: 0.28-0.70), liver cirrhosis (RR: 0.36, 95% CI: 0.30-0.42), and antiviral therapy (RR: 0.21, 95% CI: 0.08-0.59) compared with nonusers. Conclusion: This study serves as additional evidence supporting the beneficial inhibitory effect of statins on HCC incidence. The subgroup analyses of this study also highlight that statins are significantly associated with a reduced risk of HCC and may help to direct future prevention efforts. Additional large clinical studies are needed to determine whether statins are associated with a lower risk of HCC.

Deep learning algorithms for detection of diabetic retinopathy in retinal fundus photographs: A systematic review and meta-analysis.

BACKGROUND: Diabetic retinopathy (DR) is one of the leading causes of blindness globally. Earlier detection and timely treatment of DR are desirable to reduce the incidence and progression of vision loss. Currently, deep learning (DL) approaches have offered better performance in detecting DR from retinal fundus images. We, therefore, performed a systematic review with a meta-analysis of relevant studies to quantify the performance of DL algorithms for detecting DR. METHODS: A systematic literature search on EMBASE, PubMed, Google Scholar, Scopus was performed between January 1, 2000, and March 31, 2019. The search strategy was based on the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines, and DL-based study design was mandatory for articles inclusion. Two independent authors screened abstracts and titles against inclusion and exclusion criteria. Data were extracted by two authors independently using a standard form and the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was used for the risk of bias and applicability assessment. RESULTS: Twenty-three studies were included in the systematic review; 20 studies met inclusion criteria for the meta-analysis. The pooled area under the receiving operating curve (AUROC) of DR was 0.97 (95%CI: 0.95-0.98), sensitivity was 0.83 (95%CI: 0.83-0.83), and specificity was 0.92 (95%CI: 0.92-0.92). The positive- and negative-likelihood ratio were 14.11 (95%CI: 9.91-20.07), and 0.10 (95%CI: 0.07-0.16), respectively. Moreover, the diagnostic odds ratio for DL models was 136.83 (95%CI: 79.03-236.93). All the studies provided a DR-grading scale, a human grader (e.g. trained caregivers, ophthalmologists) as a reference standard. CONCLUSION: The findings of our study showed that DL algorithms had high sensitivity and specificity for detecting referable DR from retinal fundus photographs. Applying a DL-based automated tool of assessing DR from color fundus images could provide an alternative solution to reduce misdiagnosis and improve workflow. A DL-based automated tool offers substantial benefits to reduce screening costs, accessibility to healthcare and ameliorate earlier treatments.

Clinical Characteristics and Neonatal Outcomes of Pregnant Patients With COVID-19: A Systematic Review.

Background and Objective: Coronavirus disease 2019 (COVID-19) characterized by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has created serious concerns about its potential adverse effects. There are limited data on clinical, radiological, and neonatal outcomes of pregnant women with COVID-19 pneumonia. This study aimed to assess clinical manifestations and neonatal outcomes of pregnant women with COVID-19. Methods: We conducted a systematic article search of PubMed, EMBASE, Scopus, Google Scholar, and Web of Science for studies that discussed pregnant patients with confirmed COVID-19 between January 1, 2020, and April 20, 2020, with no restriction on language. Articles were independently evaluated by two expert authors. We included all retrospective studies that reported the clinical features and outcomes of pregnant patients with COVID-19. Results: Forty-seven articles were assessed for eligibility; 13 articles met the inclusion criteria for the systematic review. Data is reported for 235 pregnant women with COVID-19. The age range of patients was 25-40 years, and the gestational age ranged from 8 to 40 weeks plus 6 days. Clinical characteristics were fever [138/235 (58.72%)], cough [111/235 (47.23%)], and sore throat [21/235 (8.93%)]. One hundred fifty six out of 235 (66.38%) pregnant women had cesarean section, and 79 (33.62%) had a vaginal delivery. All the patients showed lung abnormalities in CT scan images, and none of the patients died. Neutrophil cell count, C-reactive protein (CRP) concentration, ALT, and AST were increased but lymphocyte count and albumin levels were decreased. Amniotic fluid, neonatal throat swab, and breastmilk samples were taken to test for SARS-CoV-2 but all found negativ results. Recent published evidence showed the possibility of vertical transmission up to 30%, and neonatal death up to 2.5%. Pre-eclampsia, fetal distress, PROM, pre-mature delivery were the major complications of pregnant women with COVID-19. Conclusions: Our study findings show that the clinical, laboratory and radiological characteristics of pregnant women with COVID-19 were similar to those of the general populations. The possibility of vertical transmission cannot be ignored but C-section should not be routinely recommended anymore according to latest evidences and, in any case, decisions should be taken after proper discussion with the family. Future studies are needed to confirm or refute these findings with a larger number of sample sizes and a long-term follow-up period.

Prediction of fatty liver disease using machine learning algorithms.

BACKGROUND AND OBJECTIVE: Fatty liver disease (FLD) is a common clinical complication; it is associated with high morbidity and mortality. However, an early prediction of FLD patients provides an opportunity to make an appropriate strategy for prevention, early diagnosis and treatment. We aimed to develop a machine learning model to predict FLD that could assist physicians in classifying high-risk patients and make a novel diagnosis, prevent and manage FLD. METHODS: We included all patients who had an initial fatty liver screening at the New Taipei City Hospital between 1st and 31st December 2009. Classification models such as random forest (RF), Naïve Bayes (NB), artificial neural networks (ANN), and logistic regression (LR) were developed to predict FLD. The area under the receiver operating characteristic curve (ROC) was used to evaluate performances among the four models. RESULTS: A total of 577 patients were included in this study; of those 377 patients had fatty liver. The area under the receiver operating characteristic (AUROC) of RF, NB, ANN, and LR with 10 fold-cross validation was 0.925, 0.888, 0.895, and 0.854 respectively. Additionally, The accuracy of RF, NB, ANN, and LR 87.48, 82.65, 81.85, and 76.96%. CONCLUSION: In this study, we developed and compared the four classification models to predict fatty liver disease accurately. However, the random forest model showed higher performance than other classification models. Implementation of a random forest model in the clinical setting could help physicians to stratify fatty liver patients for primary prevention, surveillance, early treatment, and management.

A probabilistic model for reducing medication errors: A sensitivity analysis using Electronic Health Records data.

OBJECTIVES: Medication-related clinical decision support systems have already been considered as a sophisticated method to improve healthcare quality, however, its importance has not been fully recognized. This paper's aim was to validate an existing probabilistic model that can automatically identify medication errors by performing a sensitivity analysis from electronic medical record data. METHODS: We first built a knowledge base that consisted of 2.22 million disease-medication (DM) and 0.78 million medication-medication (MM) associations using Taiwan Health and Welfare data science claims data between January 1st, 2009 and December 31st, 2011. Further, we collected 0.6 million outpatient visit prescriptions from six departments across five different medical centers/hospitals. Afterward, we employed the data to our AESOP model and validated it using a sensitivity analysis of 11 various thresholds (α = [0.5; 1.5]) that were used to identify positive DM and MM associations. We randomly selected 2400 randomly prescriptions and compared them to the gold standard of 18 physicians' manual review for appropriateness. RESULTS: One hundred twenty-one results of 2400 prescriptions with various thresholds were tested by the AESOP model. Validation against the gold standard showed a high accuracy (over 80%), sensitivity (80-96%), and positive predictive value (over 85%). The negative predictive values ranged from 45 to 75% across three departments, cardiology, neurology, and ophthalmology. CONCLUSION: We performed a sensitivity analysis and validated the AESOP model in different hospitals. Thus, picking the optimal threshold of the model depended on balancing false negatives with false positives and depending on the specialty and the purpose of the system.

Adverse outcomes of long-term use of proton pump inhibitors: a systematic review and meta-analysis.

The association between the long-term use of proton pump inhibitors (PPIs) and the risks of various diseases remains controversial. Therefore, the primary objective of this study was to quantify the associations as presented in the literature and to also provide this information to healthcare professionals and patients about their potentially adverse effects. In July 2016, we searched through Medline (PubMed), Embase, and the Cochrane Library from inception using common keywords. We included observational studies that provided risk estimates on the long-term use of PPIs and their adverse effects. Overall, 43 studies were included in the systematic review, of which 28 studies were also included in the random effect meta-analysis. Odds of community-acquired pneumonia, hip fracture, and colorectal cancer were 67% [odds ratio (OR)=1.67; 95% confidence interval (CI): 1.04-2.67], 42% (OR=1.42; 95% CI: 1.33-1.53), and 55% (OR=1.55; 95% CI: 0.88-2.73) higher in patients with long-term PPIs use compared with patients who did not use PPIs. Although the use of PPIs provides short-term health benefits, their prolonged use is associated with minor and also potentially major adverse health outcomes. Hence, we strongly recommend that the prescription of PPIs should be done with caution to improve the medication's efficacy and patients' safety.