Correction: Effectiveness of an Artificial Intelligence-Assisted App for Improving Eating Behaviors: Mixed Methods Evaluation.

[This corrects the article DOI: 10.2196/46036.].

Effectiveness of an Artificial Intelligence-Assisted App for Improving Eating Behaviors: Mixed Methods Evaluation.

BACKGROUND: A plethora of weight management apps are available, but many individuals, especially those living with overweight and obesity, still struggle to achieve adequate weight loss. An emerging area in weight management is the support for one's self-regulation over momentary eating impulses. OBJECTIVE: This study aims to examine the feasibility and effectiveness of a novel artificial intelligence-assisted weight management app in improving eating behaviors in a Southeast Asian cohort. METHODS: A single-group pretest-posttest study was conducted. Participants completed the 1-week run-in period of a 12-week app-based weight management program called the Eating Trigger-Response Inhibition Program (eTRIP). This self-monitoring system was built upon 3 main components, namely, (1) chatbot-based check-ins on eating lapse triggers, (2) food-based computer vision image recognition (system built based on local food items), and (3) automated time-based nudges and meal stopwatch. At every mealtime, participants were prompted to take a picture of their food items, which were identified by a computer vision image recognition technology, thereby triggering a set of chatbot-initiated questions on eating triggers such as who the users were eating with. Paired 2-sided t tests were used to compare the differences in the psychobehavioral constructs before and after the 7-day program, including overeating habits, snacking habits, consideration of future consequences, self-regulation of eating behaviors, anxiety, depression, and physical activity. Qualitative feedback were analyzed by content analysis according to 4 steps, namely, decontextualization, recontextualization, categorization, and compilation. RESULTS: The mean age, self-reported BMI, and waist circumference of the participants were 31.25 (SD 9.98) years, 28.86 (SD 7.02) kg/m2, and 92.60 (SD 18.24) cm, respectively. There were significant improvements in all the 7 psychobehavioral constructs, except for anxiety. After adjusting for multiple comparisons, statistically significant improvements were found for overeating habits (mean -0.32, SD 1.16; P<.001), snacking habits (mean -0.22, SD 1.12; P<.002), self-regulation of eating behavior (mean 0.08, SD 0.49; P=.007), depression (mean -0.12, SD 0.74; P=.007), and physical activity (mean 1288.60, SD 3055.20 metabolic equivalent task-min/day; P<.001). Forty-one participants reported skipping at least 1 meal (ie, breakfast, lunch, or dinner), summing to 578 (67.1%) of the 862 meals skipped. Of the 230 participants, 80 (34.8%) provided textual feedback that indicated satisfactory user experience with eTRIP. Four themes emerged, namely, (1) becoming more mindful of self-monitoring, (2) personalized reminders with prompts and chatbot, (3) food logging with image recognition, and (4) engaging with a simple, easy, and appealing user interface. The attrition rate was 8.4% (21/251). CONCLUSIONS: eTRIP is a feasible and effective weight management program to be tested in a larger population for its effectiveness and sustainability as a personalized weight management program for people with overweight and obesity. TRIAL REGISTRATION: ClinicalTrials.gov NCT04833803; https://classic.clinicaltrials.gov/ct2/show/NCT04833803.

A data-driven approach to evaluate factors affecting resident performance in cataract surgery.

PURPOSE: To evaluate the operative duration and clinical performance of ophthalmology residents performing standard phacoemulsification cataract surgeries using information available from electronic health records (EHR). METHODS: This is a retrospective cohort study. De-identified surgical records of all standard phacoemulsifications performed in a tertiary institution between 1st January 2015 and 8th August 2018 were retrieved from the hospital EHR. The main outcome measures were improvement in operative duration with case experience, corrected distance visual acuity (CDVA) improvement, and intra-operative complication rates. RESULTS: Twelve ophthalmology residents performed a total of 1427 standard phacoemulsifications. The median operative duration was 27 min (interquartile range, 22-34 min), which improved from 31 to 24 min (before the 101st case [Group 1] versus 101st case onwards [Group 2], p < 0.001). Gradient change analysis (non-linear regression) showed significant reduction until the 100th case (p = 0.043). Older patients (0.019), worse pre-operative CDVA (0.343), and surgery performed by Group 1 (1.115) were significantly associated with operative duration above 30 min. LogMAR CDVA improved from a mean of 0.57 ± 0.52 pre-operatively to 0.10 ± 0.18 post-operatively (p < 0.001). Posterior capsule rupture (PCR) rate decreased from 4.0% [Group 1] to 2.1% [Group 2] (p = 0.096), while overall complication rate decreased from 8.9% to 3.1% (p < 0.001). CONCLUSION: The median operative duration reduced consistently with surgical experience for the first 100 cases. Older patients, poorer pre-operative VA, and surgical experience of less than 100 cases were significantly associated with an operative duration above 30 min. There was a statistically significant decrease in complication rate between Group 1 and 2.

Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review.

BACKGROUND: Artificial intelligence (AI) applications are growing at an unprecedented pace in health care, including disease diagnosis, triage or screening, risk analysis, surgical operations, and so forth. Despite a great deal of research in the development and validation of health care AI, only few applications have been actually implemented at the frontlines of clinical practice. OBJECTIVE: The objective of this study was to systematically review AI applications that have been implemented in real-life clinical practice. METHODS: We conducted a literature search in PubMed, Embase, Cochrane Central, and CINAHL to identify relevant articles published between January 2010 and May 2020. We also hand searched premier computer science journals and conferences as well as registered clinical trials. Studies were included if they reported AI applications that had been implemented in real-world clinical settings. RESULTS: We identified 51 relevant studies that reported the implementation and evaluation of AI applications in clinical practice, of which 13 adopted a randomized controlled trial design and eight adopted an experimental design. The AI applications targeted various clinical tasks, such as screening or triage (n=16), disease diagnosis (n=16), risk analysis (n=14), and treatment (n=7). The most commonly addressed diseases and conditions were sepsis (n=6), breast cancer (n=5), diabetic retinopathy (n=4), and polyp and adenoma (n=4). Regarding the evaluation outcomes, we found that 26 studies examined the performance of AI applications in clinical settings, 33 studies examined the effect of AI applications on clinician outcomes, 14 studies examined the effect on patient outcomes, and one study examined the economic impact associated with AI implementation. CONCLUSIONS: This review indicates that research on the clinical implementation of AI applications is still at an early stage despite the great potential. More research needs to assess the benefits and challenges associated with clinical AI applications through a more rigorous methodology.

Health Care Analytics With Time-Invariant and Time-Variant Feature Importance to Predict Hospital-Acquired Acute Kidney Injury: Observational Longitudinal Study.

BACKGROUND: Acute kidney injury (AKI) develops in 4% of hospitalized patients and is a marker of clinical deterioration and nephrotoxicity. AKI onset is highly variable in hospitals, which makes it difficult to time biomarker assessment in all patients for preemptive care. OBJECTIVE: The study sought to apply machine learning techniques to electronic health records and predict hospital-acquired AKI by a 48-hour lead time, with the aim to create an AKI surveillance algorithm that is deployable in real time. METHODS: The data were sourced from 20,732 case admissions in 16,288 patients over 1 year in our institution. We enhanced the bidirectional recurrent neural network model with a novel time-invariant and time-variant aggregated module to capture important clinical features temporal to AKI in every patient. Time-series features included laboratory parameters that preceded a 48-hour prediction window before AKI onset; the latter's corresponding reference was the final in-hospital serum creatinine performed in case admissions without AKI episodes. RESULTS: The cohort was of mean age 53 (SD 25) years, of whom 29%, 12%, 12%, and 53% had diabetes, ischemic heart disease, cancers, and baseline eGFR <90 mL/min/1.73 m2, respectively. There were 911 AKI episodes in 869 patients. We derived and validated an algorithm in the testing dataset with an AUROC of 0.81 (0.78-0.85) for predicting AKI. At a 15% prediction threshold, our model generated 699 AKI alerts with 2 false positives for every true AKI and predicted 26% of AKIs. A lowered 5% prediction threshold improved the recall to 60% but generated 3746 AKI alerts with 6 false positives for every true AKI. Representative interpretation results produced by our model alluded to the top-ranked features that predicted AKI that could be categorized in association with sepsis, acute coronary syndrome, nephrotoxicity, or multiorgan injury, specific to every case at risk. CONCLUSIONS: We generated an accurate algorithm from electronic health records through machine learning that predicted AKI by a lead time of at least 48 hours. The prediction threshold could be adjusted during deployment to optimize recall and minimize alert fatigue, while its precision could potentially be augmented by targeted AKI biomarker assessment in the high-risk cohort identified.

Authorship Correction: International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study.

[This corrects the article DOI: 10.2196/31400.].

International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study.

BACKGROUND: Many countries have experienced 2 predominant waves of COVID-19-related hospitalizations. Comparing the clinical trajectories of patients hospitalized in separate waves of the pandemic enables further understanding of the evolving epidemiology, pathophysiology, and health care dynamics of the COVID-19 pandemic. OBJECTIVE: In this retrospective cohort study, we analyzed electronic health record (EHR) data from patients with SARS-CoV-2 infections hospitalized in participating health care systems representing 315 hospitals across 6 countries. We compared hospitalization rates, severe COVID-19 risk, and mean laboratory values between patients hospitalized during the first and second waves of the pandemic. METHODS: Using a federated approach, each participating health care system extracted patient-level clinical data on their first and second wave cohorts and submitted aggregated data to the central site. Data quality control steps were adopted at the central site to correct for implausible values and harmonize units. Statistical analyses were performed by computing individual health care system effect sizes and synthesizing these using random effect meta-analyses to account for heterogeneity. We focused the laboratory analysis on C-reactive protein (CRP), ferritin, fibrinogen, procalcitonin, D-dimer, and creatinine based on their reported associations with severe COVID-19. RESULTS: Data were available for 79,613 patients, of which 32,467 were hospitalized in the first wave and 47,146 in the second wave. The prevalence of male patients and patients aged 50 to 69 years decreased significantly between the first and second waves. Patients hospitalized in the second wave had a 9.9% reduction in the risk of severe COVID-19 compared to patients hospitalized in the first wave (95% CI 8.5%-11.3%). Demographic subgroup analyses indicated that patients aged 26 to 49 years and 50 to 69 years; male and female patients; and black patients had significantly lower risk for severe disease in the second wave than in the first wave. At admission, the mean values of CRP were significantly lower in the second wave than in the first wave. On the seventh hospital day, the mean values of CRP, ferritin, fibrinogen, and procalcitonin were significantly lower in the second wave than in the first wave. In general, countries exhibited variable changes in laboratory testing rates from the first to the second wave. At admission, there was a significantly higher testing rate for D-dimer in France, Germany, and Spain. CONCLUSIONS: Patients hospitalized in the second wave were at significantly lower risk for severe COVID-19. This corresponded to mean laboratory values in the second wave that were more likely to be in typical physiological ranges on the seventh hospital day compared to the first wave. Our federated approach demonstrated the feasibility and power of harmonizing heterogeneous EHR data from multiple international health care systems to rapidly conduct large-scale studies to characterize how COVID-19 clinical trajectories evolve.

What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask.

Coincident with the tsunami of COVID-19-related publications, there has been a surge of studies using real-world data, including those obtained from the electronic health record (EHR). Unfortunately, several of these high-profile publications were retracted because of concerns regarding the soundness and quality of the studies and the EHR data they purported to analyze. These retractions highlight that although a small community of EHR informatics experts can readily identify strengths and flaws in EHR-derived studies, many medical editorial teams and otherwise sophisticated medical readers lack the framework to fully critically appraise these studies. In addition, conventional statistical analyses cannot overcome the need for an understanding of the opportunities and limitations of EHR-derived studies. We distill here from the broader informatics literature six key considerations that are crucial for appraising studies utilizing EHR data: data completeness, data collection and handling (eg, transformation), data type (ie, codified, textual), robustness of methods against EHR variability (within and across institutions, countries, and time), transparency of data and analytic code, and the multidisciplinary approach. These considerations will inform researchers, clinicians, and other stakeholders as to the recommended best practices in reviewing manuscripts, grants, and other outputs from EHR-data derived studies, and thereby promote and foster rigor, quality, and reliability of this rapidly growing field.

Robust estimation of the effect of an exposure on the change in a continuous outcome.

BACKGROUND: The change in two measurements of a continuous outcome can be modelled directly with a linear regression model, or indirectly with a random effects model (REM) of the individual measurements. These methods are susceptible to model misspecifications, which are commonly addressed by applying monotonic transformations (e.g., Box-Cox transformation) to the outcomes. However, transforming the outcomes complicates the data analysis, especially when variable selection is involved. We propose a robust alternative through a novel application of the conditional probit (cprobit) model. METHODS: The cprobit model analyzes the ordered outcomes within each subject, making the estimate invariant to monotonic transformation on the outcome. By scaling the estimate from the cprobit model, we obtain the exposure effect on the change in the observed or Box-Cox transformed outcome, pending the adequacy of the normality assumption on the raw or transformed scale. RESULTS: Using simulated data, we demonstrated a similar good performance of the cprobit model and REM with and without transformation, except for some bias from both methods when the Box-Cox transformation was applied to scenarios with small sample size and strong effects. Only the cprobit model was robust to skewed subject-specific intercept terms when a Box-Cox transformation was used. Using two real datasets from the breast cancer and inpatient glycemic variability studies which utilize electronic medical records, we illustrated the application of our proposed robust approach as a seamless three-step workflow that facilitates the use of Box-Cox transformation to address non-normality with a common underlying model. CONCLUSIONS: The cprobit model provides a seamless and robust inference on the change in continuous outcomes, and its three-step workflow is implemented in an R package for easy accessibility.

Reinforcement Learning for Clinical Decision Support in Critical Care: Comprehensive Review.

BACKGROUND: Decision support systems based on reinforcement learning (RL) have been implemented to facilitate the delivery of personalized care. This paper aimed to provide a comprehensive review of RL applications in the critical care setting. OBJECTIVE: This review aimed to survey the literature on RL applications for clinical decision support in critical care and to provide insight into the challenges of applying various RL models. METHODS: We performed an extensive search of the following databases: PubMed, Google Scholar, Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, Web of Science, Medical Literature Analysis and Retrieval System Online (MEDLINE), and Excerpta Medica Database (EMBASE). Studies published over the past 10 years (2010-2019) that have applied RL for critical care were included. RESULTS: We included 21 papers and found that RL has been used to optimize the choice of medications, drug dosing, and timing of interventions and to target personalized laboratory values. We further compared and contrasted the design of the RL models and the evaluation metrics for each application. CONCLUSIONS: RL has great potential for enhancing decision making in critical care. Challenges regarding RL system design, evaluation metrics, and model choice exist. More importantly, further work is required to validate RL in authentic clinical environments.

Close air support: enhancing emergency care in the COVID-19 pandemic.

The COVID-19 pandemic has taken the world by storm and overwhelmed healthcare institutions even in developed countries. In response, clinical staff and resources have been redeployed to the areas of greatest need, that is, intensive care units and emergency rooms (ER), to reinforce front-line manpower. We introduce the concept of close air support (CAS) to augment ER operations in an efficient, safe and scalable manner. Teams of five comprising two on-site junior ER physicians would be paired with two CAS doctors, who would be off-site but be in constant communication via teleconferencing to render real-time administrative support. They would be supervised by an ER attending. This reduces direct viral exposure to doctors, conserves precious personal protective equipment and allows ER physicians to focus on patient care. Medical students can also be involved in a safe and supervised manner. After 1 month, the average time to patient disposition was halved. General feedback was also positive. CAS improves efficiency and is safe, scalable and sustainable. It has also empowered a previously untapped group of junior clinicians to support front-line medical operations, while simultaneously protecting them from viral exposure. Institutions can consider adopting our novel approach, with modifications made according to their local context.

Big data and machine learning algorithms for health-care delivery.

Analysis of big data by machine learning offers considerable advantages for assimilation and evaluation of large amounts of complex health-care data. However, to effectively use machine learning tools in health care, several limitations must be addressed and key issues considered, such as its clinical implementation and ethics in health-care delivery. Advantages of machine learning include flexibility and scalability compared with traditional biostatistical methods, which makes it deployable for many tasks, such as risk stratification, diagnosis and classification, and survival predictions. Another advantage of machine learning algorithms is the ability to analyse diverse data types (eg, demographic data, laboratory findings, imaging data, and doctors' free-text notes) and incorporate them into predictions for disease risk, diagnosis, prognosis, and appropriate treatments. Despite these advantages, the application of machine learning in health-care delivery also presents unique challenges that require data pre-processing, model training, and refinement of the system with respect to the actual clinical problem. Also crucial are ethical considerations, which include medico-legal implications, doctors' understanding of machine learning tools, and data privacy and security. In this Review, we discuss some of the benefits and challenges of big data and machine learning in health care.

The Efficacy and Safety of High-Intensity Focused Ultrasound (HIFU) Therapy for Benign Thyroid Nodules-A Single Center Experience from Singapore.

BACKGROUND: High-intensity focused ultrasound (HIFU) is a recent noninvasive technique of treating thyroid nodules. Our study aims to investigate the efficacy and safety of HIFU in treating benign thyroid nodules. METHODS: This is a retrospective analysis of consecutive patients who underwent HIFU of benign thyroid nodules at our institution from July 2017-2018. All procedures were performed by a single surgeon. Patients were evaluated immediately post-procedure, and at subsequent intervals of 1 week, 1 month, 3 months, and 6 months. The primary endpoint was thyroid nodule volume reduction at 6 months posttreatment. Secondary endpoints were post-procedure local complications. RESULTS: Ten patients with 13 thyroid nodules were included. The median follow-up period was 426 days (range 238-573). Mean maximum diameter reduced from 2.6 cm (±0.8) pretreatment to 1.4 cm (±0.7, P < 0.05) 6 months posttreatment. Mean nodule volume reduced from 5.2 cm3 (±4.2) pretreatment to 1.5 cm3 (±1.3, P = 0.01) 6 months posttreatment. Mean volume reduction ratio (VRR) at 6 months posttreatment was 63.2% (±22.5, P < 0.05), with volume reduction of ≥50% in 10 of 13 (76.9%) nodules. Two nodules (15.4%) showed size increases from 4 months posttreatment. No patients experienced local skin burns or hematomas. Mean pain scores were 1.5 (±1.2) immediate post-procedure, 0.8 (±1.5) at 1 week, and 0.6 (±1.2) at 1 month post-procedure, respectively, with no reports of pain beyond 1 month. Only two (20.0%) patients had early, temporary posttreatment voice hoarseness. CONCLUSION: Our study shows HIFU ablation to be efficacious and safe-with significant thyroid nodule volume reductions, and no significant or prolonged local complications.

BRAF mutation in papillary thyroid cancer-Prevalence and clinical correlation in a South-East Asian cohort.

OBJECTIVE: BRAF mutation is the commonest mutation seen in papillary thyroid cancer (PTC), but its prevalence and clinical significance vary across countries. We aim to evaluate the prevalence and clinico-pathological correlation of BRAF mutation in PTC patients at our centre. STUDY DESIGN: Retrospective cohort study of 75 consecutive archival thyroid specimens, whereby BRAF mutation was detected using a polymerase chain reaction (PCR) technique and correlated with clinical and pathological features and outcomes. SETTING: Tertiary university hospital in Singapore. PARTICIPANTS: A total of 75 consecutive histologically proven archival thyroid specimens from patients who underwent thyroidectomy for PTC were accrued for this study. MAIN OUTCOME MEASURES: Main outcome is to determine the prevalence of the BRAF mutation in our South-East Asian population. Secondary aim is to correlate the mutational status with adverse pathological features like histological variants, multi-focality, lymphovascular invasion and extra-thyroidal extension, clinical features like demographics, TNM stage, recurrence and survival, as well as treatment details like type of surgery performed and radioiodine doses. RESULTS: BRAF mutation was detected in 56% (42/75) of PTC. All but one BRAF-mutated PTC had the BRAFV600E mutation. BRAF-mutated tumours were associated with an advanced T-stage (P = 0.049) and were more likely to have a central neck dissection (P = 0.036). There was no significant correlation between BRAF mutation status and clinical outcomes. CONCLUSION: The prevalence of BRAF mutation is 56%. BRAF mutation-positive tumours were associated with locally advanced disease, but not poorer survival.

Evaluating Chatbot responses to patient questions in the field of glaucoma.

Objective: The aim of this study was to evaluate the accuracy, comprehensiveness, and safety of a publicly available large language model (LLM)-ChatGPT in the sub-domain of glaucoma. Design: Evaluation of diagnostic test or technology. Subjects participants and/or controls: We seek to evaluate the responses of an artificial intelligence chatbot ChatGPT (version GPT-3.5, OpenAI). Methods intervention or testing: We curated 24 clinically relevant questions in the domain of glaucoma. The questions spanned four categories: pertaining to diagnosis, treatment, surgeries, and ocular emergencies. Each question was posed to the LLM and the responses obtained were graded by an expert grader panel of three glaucoma specialists with combined experience of more than 30 years in the field. For responses which performed poorly, the LLM was further prompted to self-correct. The subsequent responses were then re-evaluated by the expert panel. Main outcome measures: Accuracy, comprehensiveness, and safety of the responses of a public domain LLM. Results: There were a total of 24 questions and three expert graders with a total number of responses of n = 72. The scores were ranked from 1 to 4, where 4 represents the best score with a complete and accurate response. The mean score of the expert panel was 3.29 with a standard deviation of 0.484. Out of the 24 question-response pairs, seven (29.2%) of them had a mean inter-grader score of 3 or less. The mean score of the original seven question-response pairs was 2.96 which rose to 3.58 after an opportunity to self-correct (z-score - 3.27, p = 0.001, Mann-Whitney U). The seven out of 24 question-response pairs which performed poorly were given a chance to self-correct. After self-correction, the proportion of responses obtaining a full score increased from 22/72 (30.6%) to 12/21 (57.1%), (p = 0.026, χ2 test). Conclusion: LLMs show great promise in the realm of glaucoma with additional capabilities of self-correction. The application of LLMs in glaucoma is still in its infancy, and still requires further research and validation.

Incorporating AI into cardiovascular diseases prevention-insights from Singapore.

Improved upstream primary prevention of cardiovascular disease (CVD) would enable more individuals to lead lives free of CVD. However, there remain limitations in the current provision of CVD primary prevention, where artificial intelligence (AI) may help to fill the gaps. Using the data informatics capabilities at the National University Health System (NUHS), Singapore, empowered by the Endeavour AI system, and combined large language model (LLM) tools, our team has created a real-time dashboard able to capture and showcase information on cardiovascular risk factors at both individual and geographical level- CardioSight. Further insights such as medication records and data on area-level socioeconomic determinants allow a whole-of-systems approach to promote healthcare delivery, while also allowing for outcomes to be tracked effectively. These are paired with interventions, such as the CHronic diseAse Management Program (CHAMP), to coordinate preventive cardiology care at a pilot stage within our university health system. AI tools in synergy allow the identification of at-risk patients and actionable steps to mitigate their health risks, thereby closing the gap between risk identification and effective patient care management in a novel CVD prevention workflow.

Public acceptance of using artificial intelligence-assisted weight management apps in high-income southeast Asian adults with overweight and obesity: a cross-sectional study.

Introduction: With in increase in interest to incorporate artificial intelligence (AI) into weight management programs, we aimed to examine user perceptions of AI-based mobile apps for weight management in adults with overweight and obesity. Methods: 280 participants were recruited between May and November 2022. Participants completed a questionnaire on sociodemographic profiles, Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), and Self-Regulation of Eating Behavior Questionnaire. Structural equation modeling was performed using R. Model fit was tested using maximum-likelihood generalized unweighted least squares. Associations between influencing factors were analyzed using correlation and linear regression. Results: 271 participant responses were analyzed, representing participants with a mean age of 31.56 ± 10.75 years, median (interquartile range) BMI, and waist circumference of 27.2 kg/m2 (24.2-28.4 kg/m2) and 86.4 (80.0-94.0) cm, respectively. In total, 188 (69.4%) participants intended to use AI-assisted weight loss apps. UTAUT2 explained 63.3% of the variance in our intention of the sample to use AI-assisted weight management apps with satisfactory model fit: CMIN/df = 1.932, GFI = 0.966, AGFI = 0.954, NFI = 0.909, CFI = 0.954, RMSEA = 0.059, SRMR = 0.050. Only performance expectancy, hedonic motivation, and the habit of using AI-assisted apps were significant predictors of intention. Comparison with existing literature revealed vast variabilities in the determinants of AI- and non-AI weight loss app acceptability in adults with and without overweight and obesity. UTAUT2 produced a good fit in explaining the acceptability of AI-assisted apps among a multi-ethnic, developed, southeast Asian sample with overweight and obesity. Conclusion: UTAUT2 model is recommended to guide the development of AI-assisted weight management apps among people with overweight and obesity.

Performance of a multigene genomic classifier and clinical parameters in predicting malignancy in a Southeast Asian cohort of patients with cytologically indeterminate thyroid nodules.

BACKGROUND: Most thyroid nodules are benign. It is important to determine the likelihood of malignancy in such nodules to avoid unnecessary surgery. The primary objective of this study was to characterize the genetic landscape and the performance of a multigene genomic classifier in fine-needle aspiration (FNA) biopsies of cytologically indeterminate thyroid nodules in a Southeast Asian cohort. The secondary objective was to assess the predictive contribution of clinical characteristics to thyroid malignancy. METHODS: This prospective, multicenter, blinded study included 132 patients with 134 nodules. Molecular testing (MT) with ThyroSeq v3 was performed on clinical or ex-vivo FNA samples. Centralized pathology review also was performed. RESULTS: Of 134 nodules, consisting of 61% Bethesda category III, 20% category IV, and 19% category V cytology, and 56% were histologically malignant. ThyroSeq yielded negative results in 37.3% of all FNA samples and in 42% of Bethesda category III-IV cytology nodules. Most positive samples had RAS-like (41.7%), followed by BRAF-like (22.6%), and high-risk (17.9%) alterations. Compared with North American patients, the authors observed a higher proportion of RAS-like mutations, specifically NRAS, in Bethesda categories III and IV and more BRAF-like mutations in Bethesda category III. The test had sensitivity, specificity, negative predictive value, and positive predictive value of 89.6%, 73.7%, 84.0%, and 82.1%, respectively. The risk of malignancy was predicted by positive MT and high-suspicion ultrasound characteristics according to American Thyroid Association criteria. CONCLUSIONS: Even in the current Southeast Asian cohort with nodules that had a high pretest cancer probability, MT could lead to potential avoidance of diagnostic surgery in 42% of patients with Bethesda category III-IV nodules. MT positivity was a stronger predictor of malignancy than clinical parameters.

Colonic architectural change on colonoscopy in patients taking psychotropic medications.

BACKGROUND: Patients on psychotropic medications have been clinically observed to have higher rates of abnormal colonic architecture resulting in difficult colonoscopies. This study aims to determine if a correlation between use of psychotropic medications and colonic architectural change seen on colonoscopy exists. METHODS: A retrospective case-control study was undertaken with 252 adults selected from the hospital endoscopy database between January 2006 and July 2008. Cases were selected if they had 'capacious', 'megacolon', 'redundant' and/or 'featureless' colonic architecture reported in their first completed colonoscopy (n = 63). Demographic information and medication records were collected for both cases and controls. Logistic regression analysis was performed for each of the medication groups. RESULTS: Medication groups associated with increased incidence for colonic architectural changes observed during colonoscopy include: antipsychotic medications [odds ratio (OR) 7.79, confidence interval (CI) 2.59-23.41], benzhexol (OR 23.50, CI 2.83-195.08) and iron tablets (OR 2.97, CI 1.39-6.33). Antidepressants, laxatives, benzodiazepines, gastroprotective medications and antihypertensive medications were not found to have any significant effect on changes to colonic architecture. CONCLUSIONS: Use of antipsychotic medications is associated with changes to colonic architecture. This could predispose such a patient to difficult colonoscopy and therefore increase colonoscopy-associated risks. Medication history should be elicited prior to colonoscopy.

How data science and AI-based technologies impact genomics.

Advancements in high-throughput sequencing have yielded vast amounts of genomic data, which are studied using genome-wide association study (GWAS)/phenome-wide association study (PheWAS) methods to identify associations between the genotype and phenotype. The associated findings have contributed to pharmacogenomics and improved clinical decision support at the point of care in many healthcare systems. However, the accumulation of genomic data from sequencing and clinical data from electronic health records (EHRs) poses significant challenges for data scientists. Following the rise of artificial intelligence (AI) technology such as machine learning and deep learning, an increasing number of GWAS/PheWAS studies have successfully leveraged this technology to overcome the aforementioned challenges. In this review, we focus on the application of data science and AI technology in three areas, including risk prediction and identification of causal single-nucleotide polymorphisms, EHR-based phenotyping and CRISPR guide RNA design. Additionally, we highlight a few emerging AI technologies, such as transfer learning and multi-view learning, which will or have started to benefit genomic studies.