Background: Core stability is influential in the incidence of lower extremity injuries, including anterior cruciate ligament (ACL) injuries, but the effects of core strength training on the risk for ACL injury remain unclear. Hypothesis: Core muscle strength training increases the knee flexion angle, hamstring to quadriceps (H:Q) coactivation ratio, and vastus medialis to vastus lateralis (VM:VL) muscle activation ratio, as well as decreases the hip adduction, knee valgus, and tibial internal rotation angles. Study Design: Controlled laboratory study. Methods: A total of 48 male participants were recruited and randomly assigned to either the intervention group (n = 32) or the control group (n = 16). Three-dimensional trunk, hip, knee, and ankle kinematic data and muscle activations of selected trunk and lower extremity muscles were obtained while the participants performed side-step cutting. The core endurance scores were measured before and after training. Two-way analyses of variance were conducted for each dependent variable to determine the effects of 10 weeks of core strength training. Results: The trunk endurance scores in the intervention group significantly increased after training ( P < .05 for all comparisons). The intervention group showed decreased knee valgus ( P = .038) and hip adduction angles ( P = .032) but increased trunk flexion angle ( P = .018), rectus abdominis to erector spinae coactivation ratio ( P = .047), H:Q coactivation ratio ( P = .021), and VM:VL activation ratio ( P = .016). In addition, the knee valgus angle at initial contact was negatively correlated with the VM:VL activation ratio in the precontact phase ( R2 = 0.188; P < .001) but was positively correlated with the hip adduction angle ( R2 = 0.120; P < .005). No statistically significant differences were observed in the trunk endurance scores, kinematics, and muscle activations for the control group. Conclusion: Core strength training altered the motor control strategies and joint kinematics for the trunk and the lower extremity by increasing the trunk flexion angle, VM:VL activation ratio, and H:Q activation ratio and reducing the knee valgus and hip adduction angles. Clinical Relevance: Training core muscles can modify the biomechanics associated with ACL injuries in a side-step cutting task; thus, core strength training might be considered in ACL injury prevention programs to alter the lower extremity alignment in the frontal plane and muscle activations during sports-related tasks.

We developed and validated a deep-learning algorithm for polyp detection. We used a YOLOv2 to develop the algorithm for automatic polyp detection on 8,075 images (503 polyps). We validated the algorithm using three datasets: A: 1,338 images with 1,349 polyps; B: an open, public CVC-clinic database with 612 polyp images; and C: 7 colonoscopy videos with 26 polyps. To reduce the number of false positives in the video analysis, median filtering was applied. We tested the algorithm performance using 15 unaltered colonoscopy videos (dataset D). For datasets A and B, the per-image polyp detection sensitivity was 96.7% and 90.2%, respectively. For video study (dataset C), the per-image polyp detection sensitivity was 87.7%. False positive rates were 12.5% without a median filter and 6.3% with a median filter with a window size of 13. For dataset D, the sensitivity and false positive rate were 89.3% and 8.3%, respectively. The algorithm detected all 38 polyps that the endoscopists detected and 7 additional polyps. The operation speed was 67.16 frames per second. The automatic polyp detection algorithm exhibited good performance, as evidenced by the high detection sensitivity and rapid processing. our algorithm may help endoscopists improve polyp detection. Colonoscopy is an important colorectal cancer (CRC) screening test worldwide. Colonoscopy has several advantages, such as the removal of lesions and visualization in a single test. Recent studies indicated that having a colonoscopy was associated with a 60% reduction in CRC mortality 1 and a 70% reduction in the incidence of late-stage CRCs 2. Colonoscopy quality assurance is of paramount importance for effective prevention of CRC and reduction of mortality due to CRC. Accurate detection of adenomas is the most critical issue during a colonoscopy. The adenoma detection rate is an essential quality indicator during colonoscopy. Evidence suggests that a 1.0% increase in the adenoma detection rate leads to a 3.0% decrease in the risk of interval CRC 3. The adenoma detection rate varies from 17% to 47% because the characteristics of colonoscopy are highly operator-dependent 4. Therefore, it is important to increase the adenoma detection rate for adequate CRC screening via colonoscopy. Although many efforts have been directed toward improving the detection of adenoma, such as improving the bowel preparation, spending enough time to inspect the colonic mucosa, and developing several novel technologies, such as wide-angle cameras and cap-assisted techniques to flatten colonic folds 5 , the problem of missing polyps remains. A previous study indicated that endoscopists with wider visual gaze patterns achieved a higher polyp detection rate than those with centralized visual gaze patterns 6. Several studies have indicated that the participation of an experienced nurse during the colonoscopy examination as a "second observer" increased the adenoma detection rates by up to 30-50% 7,8 and increased the detection performance of inexperienced endoscopists 7. A real-time automatic pol...

ObjectiveWe developed and investigated the feasibility of a machine learning–based automated rating for the 2 cardinal symptoms of Parkinson disease (PD): resting tremor and bradykinesia.MethodsUsing OpenPose, a deep learning–based human pose estimation program, we analyzed video clips for resting tremor and finger tapping of the bilateral upper limbs of 55 patients with PD (110 arms). Key motion parameters, including resting tremor amplitude and finger tapping speed, amplitude, and fatigue, were extracted to develop a machine learning–based automatic Unified Parkinson's Disease Rating Scale (UPDRS) rating using support vector machine (SVM) method. To evaluate the performance of this model, we calculated weighted κ and intraclass correlation coefficients (ICCs) between the model and the gold standard rating by a movement disorder specialist who is trained and certified by the Movement Disorder Society for UPDRS rating. These values were compared to weighted κ and ICC between a nontrained human rater and the gold standard rating.ResultsFor resting tremors, the SVM model showed a very good to excellent reliability range with the gold standard rating (κ 0.791; ICC 0.927), with both values higher than that of nontrained human rater (κ 0.662; ICC 0.861). For finger tapping, the SVM model showed a very good reliability range with the gold standard rating (κ 0.700 and ICC 0.793), which was comparable to that for nontrained human raters (κ 0.627; ICC 0.797).ConclusionMachine learning–based algorithms that automatically rate PD cardinal symptoms are feasible, with more accurate results than nontrained human ratings.Classification of EvidenceThis study provides Class II evidence that machine learning–based automated rating of resting tremor and bradykinesia in people with PD has very good reliability compared to a rating by a movement disorder specialist.