A deep learning model detected and classified lumbar spinal stenosis at MRI comparable with radiologists for central canal and lateral recess stenosis, with slightly lower agreement for neural fora... Background Assessment of lumbar spinal stenosis at MRI is repetitive and time consuming. Deep learning (DL) could improve ­productivity and the consistency of reporting. Purpose To develop a DL mod...

Radiologists assisted by deep learning for interpretation of lumbar spinal stenosis on MRI scans showed a marked reduction in reporting time and superior or equivalent interobserver agreement for a... Background Lumbar spine MRI studies are widely used for back pain assessment. Interpretation involves grading lumbar spinal stenosis, which is repetitive and time consuming. Deep learning (DL) coul...

Background: Mobile health apps are increasingly used as early intervention to support behavior change for diabetes prevention and control, with the overarching goal of lowering the overall disease burden. Objective: This prospective cohort study conducted in Singapore aimed to investigate app engagement features and their association with weight loss and improved glycemic control among adults with diabetes and prediabetes from the intervention arm of the Diabetes Lifestyle Intervention using Technology Empowerment randomized controlled trial. Methods: Diabetes and prediabetes participants (N=171) with a median age of 52 years, BMI of 29.3 kg/m2, and glycated hemoglobin (HbA1c) level of 6.5% and who were being assigned the Nutritionist Buddy Diabetes app were included. Body weight and HbA1c were measured at baseline, 3 months, and 6 months. A total of 476,300 data points on daily app engagement were tracked via the backend dashboard and developer’s report. The app engagement data were analyzed by quartiles and weekly means expressed in days per week. Linear mixed model analysis was used to determine the associations between the app engagements with percentage weight and HbA1c change. Results: The median overall app engagement rate was maintained above 90% at 6 months. Participants who were actively engaged in ≥5 app features were associated with the greatest overall weight reduction of 10.6% from baseline (mean difference −6, 95% CI −8.9 to −3.2; P<.001) at 6 months. Adhering to the carbohydrate limit of >5.9 days per week and choosing healthier food options for >4.3 days per week had the most impact, eliciting weight loss of 9.1% (mean difference −5.2, 95% CI −8.2 to −2.2; P=.001) and 8.8% (mean difference −4.2, 95% CI −7.1 to −1.3; P=.005), respectively. Among the participants with diabetes, those who had a complete meal log for >5.1 days per week or kept within their carbohydrate limit for >5.9 days per week each achieved greater HbA1c reductions of 1.2% (SD 1.3%; SD 1.5%), as compared with 0.2% (SD 1%; SD 0.6%). in the reference groups who used the features <1.1 or ≤2.5 days per week, respectively. Conclusions: Higher app engagement led to greater weight loss and HbA1c reduction among adults with overweight or obesity with type 2 diabetes or prediabetes. Trial Registration: Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12617001112358; https://anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12617001112358

Gallstones are a common public health problem, especially in developed countries. There are an increasing number of patients who are diagnosed with gallstones due to increasing awareness and liberal use of imaging, with 22.6%-80% of gallstone patients being asymptomatic at the time of diagnosis. Despite being asymptomatic, this group of patients are still at life-long risk of developing symptoms and complications such as acute cholangitis and acute biliary pancreatitis. Hence, while early prophylactic cholecystectomy may have some benefits in selected groups of patients, the current standard practice is to recommend cholecystectomy only after symptoms or complications occur. After reviewing the current evidence about the natural course of asymptomatic gallstones, complications of cholecystectomy, quality of life outcomes, and economic outcomes, we recommend that the option of cholecystectomy should be discussed with all asymptomatic gallstone patients. Disclosure of material information is essential for patients to make an informed choice for prophylactic cholecystectomy. It is for the patient to decide on watchful waiting or prophylactic cholecystectomy, and not for the medical community to make a blanket policy of watchful waiting for asymptomatic gallstone patients. For patients with high-risk profiles, it is clinically justifiable to advocate cholecystectomy to minimize the likelihood of morbidity due to complications.

Background: Metastatic epidural spinal cord compression (MESCC) is a disastrous complication of advanced malignancy. Deep learning (DL) models for automatic MESCC classification on staging CT were developed to aid earlier diagnosis. Methods: This retrospective study included 444 CT staging studies from 185 patients with suspected MESCC who underwent MRI spine studies within 60 days of the CT studies. The DL model training/validation dataset consisted of 316/358 (88%) and the test set of 42/358 (12%) CT studies. Training/validation and test datasets were labeled in consensus by two subspecialized radiologists (6 and 11-years-experience) using the MRI studies as the reference standard. Test sets were labeled by the developed DL models and four radiologists (2–7 years of experience) for comparison. Results: DL models showed almost-perfect interobserver agreement for classification of CT spine images into normal, low, and high-grade MESCC, with kappas ranging from 0.873–0.911 (p < 0.001). The DL models (lowest κ = 0.873, 95% CI 0.858–0.887) also showed superior interobserver agreement compared to two of the four radiologists for three-class classification, including a specialist (κ = 0.820, 95% CI 0.803–0.837) and general radiologist (κ = 0.726, 95% CI 0.706–0.747), both p < 0.001. Conclusion: DL models for the MESCC classification on a CT showed comparable to superior interobserver agreement to radiologists and could be used to aid earlier diagnosis.