Effectiveness of a web-based program on self-care behaviors and glycated hemoglobin in patients with type 2 diabetes: Study protocol of a randomized controlled trial.

BACKGROUND: Type 2 diabetes (T2DM) decreases the life expectancy and quality of life of diabetics and causes economic and societal problems. For this purpose, diabetes self-management education and support (DSMES) has been designed for many years, which is recently provided through technology-assisted education. Therefore, we developed a web-based program in accordance with DSMES to assess its effect on self-care behaviors and glycated hemoglobin (HbA1c) for patients with T2DM during the coronavirus disease (COVID-19) pandemic, which is described in detail in this paper. MATERIALS AND METHODS: This randomized controlled trial (RCT) was performed on 70 diabetic patients in Al-Zahra Hospital for three months. After random allocation, web-based educational content (including videos, lectures, educational motion graphics, text files, educational posters, and podcasts) according to DSMES was provided for the intervention group to improve self-care behaviors and HbA1c levels. The control group received routine educational pamphlets. A diabetes self-management questionnaire (21 questions) with a Likert scale was completed to assess self-care behaviors scores before and after intervention and three months later. Also, HbA1c was determined before and after the intervention. Analysis of variance with repeated measurements will be applied to compare mean scores of self-care behaviors components three times, and an independent t-test analyzed mean differences of HbA1c values. CONCLUSION: The obtained results of this study might be useful for promoting self-care behaviors and assessing HbA1c in diabetic patients.

Comparison of humans versus mobile phone-powered artificial intelligence for the diagnosis and management of pigmented skin cancer in secondary care: a multicentre, prospective, diagnostic, clinical trial.

BACKGROUND: Diagnosis of skin cancer requires medical expertise, which is scarce. Mobile phone-powered artificial intelligence (AI) could aid diagnosis, but it is unclear how this technology performs in a clinical scenario. Our primary aim was to test in the clinic whether there was equivalence between AI algorithms and clinicians for the diagnosis and management of pigmented skin lesions. METHODS: In this multicentre, prospective, diagnostic, clinical trial, we included specialist and novice clinicians and patients from two tertiary referral centres in Australia and Austria. Specialists had a specialist medical qualification related to diagnosing and managing pigmented skin lesions, whereas novices were dermatology junior doctors or registrars in trainee positions who had experience in examining and managing these lesions. Eligible patients were aged 18-99 years and had a modified Fitzpatrick I-III skin type; those in the diagnostic trial were undergoing routine excision or biopsy of one or more suspicious pigmented skin lesions bigger than 3 mm in the longest diameter, and those in the management trial had baseline total-body photographs taken within 1-4 years. We used two mobile phone-powered AI instruments incorporating a simple optical attachment: a new 7-class AI algorithm and the International Skin Imaging Collaboration (ISIC) AI algorithm, which was previously tested in a large online reader study. The reference standard for excised lesions in the diagnostic trial was histopathological examination; in the management trial, the reference standard was a descending hierarchy based on histopathological examination, comparison of baseline total-body photographs, digital monitoring, and telediagnosis. The main outcome of this study was to compare the accuracy of expert and novice diagnostic and management decisions with the two AI instruments. Possible decisions in the management trial were dismissal, biopsy, or 3-month monitoring. Decisions to monitor were considered equivalent to dismissal (scenario A) or biopsy of malignant lesions (scenario B). The trial was registered at the Australian New Zealand Clinical Trials Registry ACTRN12620000695909 (Universal trial number U1111-1251-8995). FINDINGS: The diagnostic study included 172 suspicious pigmented lesions (84 malignant) from 124 patients and the management study included 5696 pigmented lesions (18 malignant) from the whole body of 66 high-risk patients. The diagnoses of the 7-class AI algorithm were equivalent to the specialists' diagnoses (absolute accuracy difference 1·2% [95% CI -6·9 to 9·2]) and significantly superior to the novices' ones (21·5% [13·1 to 30·0]). The diagnoses of the ISIC AI algorithm were significantly inferior to the specialists' diagnoses (-11·6% [-20·3 to -3·0]) but significantly superior to the novices' ones (8·7% [-0·5 to 18·0]). The best 7-class management AI was significantly inferior to specialists' management (absolute accuracy difference in correct management decision -0·5% [95% CI -0·7 to -0·2] in scenario A and -0·4% [-0·8 to -0·05] in scenario B). Compared with the novices' management, the 7-class management AI was significantly inferior (-0·4% [-0·6 to -0·2]) in scenario A but significantly superior (0·4% [0·0 to 0·9]) in scenario B. INTERPRETATION: The mobile phone-powered AI technology is simple, practical, and accurate for the diagnosis of suspicious pigmented skin cancer in patients presenting to a specialist setting, although its usage for management decisions requires more careful execution. An AI algorithm that was superior in experimental studies was significantly inferior to specialists in a real-world scenario, suggesting that caution is needed when extrapolating results of experimental studies to clinical practice. FUNDING: MetaOptima Technology.

Banff 2016 Global Assessment and Quantitative Scoring for T Cell-Mediated Liver Transplant Rejection are Interchangeable.

Introduction: Histopathological assessment of liver biopsies is the current "gold standard" for diagnosing graft dysfunction after liver transplantation (LT), as graft dysfunction can have nonspecific clinical presentations and inconsistent patterns of liver biochemical dysfunction. Most commonly, post-LT, graft dysfunction within the first year, is due to acute T-cell mediated rejection (TCMR) which is characterised histologically by the degree of portal inflammation (PI), bile duct damage (BDD), and venous endothelial inflammation (VEI). This study aimed to establish the relationship between global assessment, which is the global grading of rejection using a "gestalt" approach, and the rejection activity index (RAI) of each component of TCMR as described in revised Banff 2016 guidelines. Methods: Liver biopsies (n = 90) taken from patients who underwent LT in 2015 and 2016 at the Australian National Liver Transplant Unit were identified from the electronic medical records. All biopsy slides were microscopically graded by at least two assessors independently using the revised 2016 Banff criteria. Data were analysed using IBM SPSS v21. A Fisher-Freeman-Halton test was performed to assess the correlation between the global assessment and the RAI scores for each TCMR biopsy. Results: Within the cohort, 60 (37%, n = 164) patients underwent at least 1 biopsy within 12 months after LT. The most common biopsy outcome (total n = 90) was acute TCMR (64, 71.1%). Global assessment of TCMR slides strongly positively correlated with PI (p value <0.001), BDD (p value <0.001), VEI (p value <0.001), and total RAI (p value <0.001). Liver biochemistry of patients with TCMR significantly improved within 4 to 6 weeks post-biopsy compared to the day of the biopsy. Conclusion: In acute TCMR, global assessment and total RAI are strongly correlated and can be used interchangeably to describe the severity of TCMR.

Is Web-Based Program Effective on Self-Care Behaviors and Glycated Hemoglobin in Patients with Type 2 Diabetes: A Randomized Controlled Trial.

Background: Diabetes Self-Management Education and Support (DSMES) as a framework focuses on seven self-care behaviors. Moreover, technology-assisted self-care education is increasingly suggested for patients with Type 2 Diabetes Mellitus (T2DM). Therefore, we examined the effect of a web-based program on self-care behaviors and glycated hemoglobin values in patients with diabetes mellitus. Materials and Methods: This randomized controlled clinical trial was conducted at Alzahra Hospital in Isfahan, Iran, between April and November 2020 and included 70 patients with T2DM. Data were collected using a questionnaire that included a demographic information section and a diabetes self-management section with 21 questions on a Likert scale. Fasting blood samples (2.50 ml) were collected before and after the interventions to measure HbA1c levels. The study intervention involved a web-based program that included multimedia educational content (such as videos, lectures, educational motion graphics, text files, posters, and podcasts) presented in seven sections based on DSMES over a 21-day period with monitoring by an instructor. Results: The mean scores for healthy eating (F = 3.48, p = 0.034) and medication adherence (F = 6.70, p < 0.001) significantly increased in the interventional group, while the mean scores for being active, monitoring, reducing risks, problem-solving, and healthy coping did not significantly change. Additionally, the mean differences in HbA1c values significantly improved in the interventional group compared to the control (F = 5,1, p = 0.026). Conclusions: A web-based program in accordance with DSMES improved HbA1c levels and increased scores for healthy eating and medication adherence in patients with T2DM. However, further research with larger sample sizes and qualitative interviews is needed.

Analysis of Multifocal Visual Evoked Potentials Using Artificial Intelligence Algorithms.

Purpose: Clinical trials for remyelination in multiple sclerosis (MS) require an imaging biomarker. The multifocal visual evoked potential (mfVEP) is an accurate technique for measuring axonal conduction; however, it produces large datasets requiring lengthy analysis by human experts to detect measurable responses versus noisy traces. This study aimed to develop a machine-learning approach for the identification of true responses versus noisy traces and the detection of latency peaks in measurable signals. Methods: We obtained 2240 mfVEP traces from 10 MS patients using the VS-1 mfVEP machine, and they were classified by a skilled expert twice with an interval of 1 week. Of these, 2025 (90%) were classified consistently and used for the study. ResNet-50 and VGG16 models were trained and tested to produce three outputs: no signal, up-sloped signal, or down-sloped signal. Each model ran 1000 iterations with a stochastic gradient descent optimizer with a learning rate of 0.0001. Results: ResNet-50 and VGG16 had false-positive rates of 1.7% and 0.6%, respectively, when the testing dataset was analyzed (n = 612). The false-negative rates were 8.2% and 6.5%, respectively, against the same dataset. The latency measurements in the validation and testing cohorts in the study were similar. Conclusions: Our models efficiently analyze mfVEPs with <2% false positives compared with human false positives of <8%. Translational Relevance: mfVEP, a safe neurophysiological technique, analyzed using artificial intelligence, can serve as an efficient biomarker in MS clinical trials and signal latency measurement.