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Key Points: Question: Can novice clinicians accurately estimate gestational age using a low-cost, battery-powered ultrasonography probe with integrated artificial intelligence (AI) image interpretation? Findings: This prospective study enrolled 400 pregnant individuals with due dates confirmed by first-trimester ultrasonography. At follow-up visits randomly assigned throughout gestation, novice clinicians using an AI-enabled device estimated gestational age as accurately as credentialed sonographers using traditional ultrasonography devices (difference, 0.2 days). Meaning: Obstetrical care in low-resource settings may benefit from reliable gestational age assessment using AI integration with point-of-care ultrasonography. Importance: Accurate assessment of gestational age (GA) is essential to good pregnancy care but often requires ultrasonography, which may not be available in low-resource settings. This study developed a deep learning artificial intelligence (AI) model to estimate GA from blind ultrasonography sweeps and incorporated it into the software of a low-cost, battery-powered device. Objective: To evaluate GA estimation accuracy of an AI-enabled ultrasonography tool when used by novice users with no prior training in sonography. Design, Setting, and Participants: This prospective diagnostic accuracy study enrolled 400 individuals with viable, single, nonanomalous, first-trimester pregnancies in Lusaka, Zambia, and Chapel Hill, North Carolina. Credentialed sonographers established the "ground truth" GA via transvaginal crown-rump length measurement. At random follow-up visits throughout gestation, including a primary evaluation window from 14 0/7 weeks' to 27 6/7 weeks' gestation, novice users obtained blind sweeps of the maternal abdomen using the AI-enabled device (index test) and credentialed sonographers performed fetal biometry with a high-specification machine (study standard). Main Outcomes and Measures: The primary outcome was the mean absolute error (MAE) of the index test and study standard, which was calculated by comparing each method's estimate to the previously established GA and considered equivalent if the difference fell within a prespecified margin of ±2 days. Results: In the primary evaluation window, the AI-enabled device met criteria for equivalence to the study standard, with an MAE (SE) of 3.2 (0.1) days vs 3.0 (0.1) days (difference, 0.2 days [95% CI, −0.1 to 0.5]). Additionally, the percentage of assessments within 7 days of the ground truth GA was comparable (90.7% for the index test vs 92.5% for the study standard). Performance was consistent in prespecified subgroups, including the Zambia and North Carolina cohorts and those with high body mass index. Conclusions and Relevance: Between 14 and 27 weeks' gestation, novice users with no prior training in ultrasonography estimated GA as accurately with the low-cost, point-of-care AI tool as credentialed sonographers performing standard biometry on high-specification machines. These findings have immediate implications for obstetrical care in low-resource settings, advancing the World Health Organization goal of ultrasonography estimation of GA for all pregnant people. Trial Registration: ClinicalTrials.gov Identifier: NCT05433519 This prospective diagnostic study examines the accuracy of an AI-enabled ultrasonography tool in estimating gestational age when used by those with no prior training in sonography. [ABSTRACT FROM AUTHOR]

Objective: Low‐cost devices have made obstetric sonography possible in settings where it was previously unfeasible, but ensuring quality and consistency at scale remains a challenge. In the present study, we sought to create a tool to reduce substandard fetal biometry measurement while minimizing care disruption. Methods: We developed a deep learning artificial intelligence (AI) model to estimate gestational age (GA) in the second and third trimester from fly‐to cineloops—brief videos acquired during routine ultrasound biometry—and evaluated its performance in comparison to expert sonographer measurement. We then introduced random error into fetal biometry measurements and analyzed the ability of the AI model to flag grossly inaccurate measurements such as those that might be obtained by a novice. Results: The mean absolute error (MAE) of our model (±standard error) was 3.87 ± 0.07 days, compared to 4.80 ± 0.10 days for expert biometry (difference −0.92 days; 95% CI: −1.10 to −0.76). Based on simulated novice biometry with average absolute error of 7.5%, our model reliably detected cases where novice biometry differed from expert biometry by 10 days or more, with an area under the receiver operating characteristics curve of 0.93 (95% CI: 0.92, 0.95), sensitivity of 81.0% (95% CI: 77.9, 83.8), and specificity of 89.9% (95% CI: 88.1, 91.5). These results held across a range of sensitivity analyses, including where the model was provided suboptimal truncated fly‐to cineloops. Conclusions: Our AI model estimated GA more accurately than expert biometry. Because fly‐to cineloop videos can be obtained without any change to sonographer workflow, the model represents a no‐cost guardrail that could be incorporated into both low‐cost and commercial ultrasound devices to prevent reporting of most gross GA estimation errors. Synopsis: Our model predicts gestational age (GA) more accurately from routinely available fly‐to cineloops than expert sonographer acquired biometry and can act as a guardrail to prevent gross GA estimation errors. [ABSTRACT FROM AUTHOR]

Aims: Co‐management of weight and glycaemia is critical yet challenging in type 1 diabetes (T1D). We evaluated the effect of a hypocaloric low carbohydrate, hypocaloric moderate low fat, and Mediterranean diet without calorie restriction on weight and glycaemia in young adults with T1D and overweight or obesity. Materials and Methods: We implemented a 9‐month Sequential, Multiple Assignment, Randomized Trial pilot among adults aged 19‐30 years with T1D for ≥1 year and body mass index 27‐39.9 kg/m2. Re‐randomization occurred at 3 and 6 months if the assigned diet was not acceptable or not effective. We report results from the initial 3‐month diet period and re‐randomization statistics before shutdowns due to COVID‐19 for primary [weight, haemoglobin A1c (HbA1c), percentage of time below range <70 mg/dl] and secondary outcomes [body fat percentage, percentage of time in range (70‐180 mg/dl), and percentage of time below range <54 mg/dl]. Models adjusted for design, demographic and clinical covariates tested changes in outcomes and diet differences. Results: Adjusted weight and HbA1c (n = 38) changed by −2.7 kg (95% CI −3.8, −1.5, P <.0001) and −0.91 percentage points (95% CI −1.5, −0.30, P =.005), respectively, while adjusted body fat percentage remained stable, on average (P =.21). Hypoglycaemia indices remained unchanged following adjustment (n = 28, P >.05). Variability in all outcomes, including weight change, was considerable (57.9% were re‐randomized primarily due to loss of <2% body weight). No outcomes varied by diet. Conclusions: Three months of a diet, irrespective of macronutrient distribution or caloric restriction, resulted in weight loss while improving or maintaining HbA1c levels without increasing hypoglycaemia in adults with T1D. [ABSTRACT FROM AUTHOR]

Objective: To estimate difference in population-level glycemic control and the emergence of diabetes complications given a theoretical scenario in which non-White youth and young adults (YYA) with type 1 diabetes (T1D) receive and follow an equivalent distribution of diabetes treatment regimens as non-Hispanic White YYA.Research Design and Methods: Longitudinal data from YYA diagnosed 2002-2005 in the SEARCH for Diabetes in Youth Study were analyzed. Based on self-reported race/ethnicity, YYA were classified as non-White race or Hispanic ethnicity (non-White subgroup) versus non-Hispanic White race (White subgroup). In the White versus non-White subgroups, the propensity score models estimated treatment regimens, including patterns of insulin modality, self-monitored glucose frequency, and continuous glucose monitoring use. An analysis based on policy evaluation techniques in reinforcement learning estimated the effect of each treatment regimen on mean hemoglobin A1c (HbA1c) and the prevalence of diabetes complications for non-White YYA.Results: The study included 978 YYA. The sample was 47.5% female and 77.5% non-Hispanic White, with a mean age of 12.8 ± 2.4 years at diagnosis. The estimated population mean of longitudinal average HbA1c over visits was 9.2% and 8.2% for the non-White and White subgroup, respectively (difference of 0.9%). Within the non-White subgroup, mean HbA1c across visits was estimated to decrease by 0.33% (95% CI -0.45, -0.21) if these YYA received the distribution of diabetes treatment regimens of the White subgroup, explaining ∼35% of the estimated difference between the two subgroups. The non-White subgroup was also estimated to have a lower risk of developing diabetic retinopathy, diabetic kidney disease, and peripheral neuropathy with the White youth treatment regimen distribution (P < 0.05), although the low proportion of YYA who developed complications limited statistical power for risk estimations.Conclusions: Mathematically modeling an equalized distribution of T1D self-management tools and technology accounted for part of but not all disparities in glycemic control between non-White and White YYA, underscoring the complexity of race and ethnicity-based health inequity. [ABSTRACT FROM AUTHOR]

Background: Each year, nearly 300,000 women and 5 million fetuses or neonates die during childbirth or shortly thereafter, a burden concentrated disproportionately in low- and middle-income countries. Identifying women and their fetuses at risk for intrapartum-related morbidity and death could facilitate early intervention. Methods: The Limiting Adverse Birth Outcomes in Resource-Limited Settings (LABOR) Study is a multi-country, prospective, observational cohort designed to exhaustively document the course and outcomes of labor, delivery, and the immediate postpartum period in settings where adverse outcomes are frequent. The study is conducted at four hospitals across three countries in Ghana, India, and Zambia. We will enroll approximately 12,000 women at presentation to the hospital for delivery and follow them and their fetuses/newborns throughout their labor and delivery course, postpartum hospitalization, and up to 42 days thereafter. The co-primary outcomes are composites of maternal (death, hemorrhage, hypertensive disorders, infection) and fetal/neonatal adverse events (death, encephalopathy, sepsis) that may be attributed to the intrapartum period. The study collects extensive physiologic data through the use of physiologic sensors and employs medical scribes to document examination findings, diagnoses, medications, and other interventions in real time. Discussion: The goal of this research is to produce a large, sharable dataset that can be used to build statistical algorithms to prospectively stratify parturients according to their risk of adverse outcomes. We anticipate this research will inform the development of new tools to reduce peripartum morbidity and mortality in low-resource settings., Competing Interests: No competing interests were disclosed., (Copyright: © 2022 Adu-Amankwah A et al.)