Using Artificial Intelligence to Gauge Competency on a Novel Laparoscopic Training System.

OBJECTIVE: Laparoscopic surgical skill assessment and machine learning are often inaccessible to low-and-middle-income countries (LMIC). Our team developed a low-cost laparoscopic training system to teach and assess psychomotor skills required in laparoscopic salpingostomy in LMICs. We performed video review using AI to assess global surgical techniques. The objective of this study was to assess the validity of artificial intelligence (AI) generated scoring measures of laparoscopic simulation videos by comparing the accuracy of AI results to human-generated scores. DESIGN: Seventy-four surgical simulation videos were collected and graded by human participants using a modified OSATS (Objective Structured Assessment of Technical Skills). The videos were then analyzed via AI using 3 different time and distance-based calculations of the laparoscopic instruments including path length, dimensionless jerk, and standard deviation of tool position. Predicted scores were generated using 5-fold cross validation and K-Nearest-Neighbors to train classifiers. SETTING: Surgical novices and experts from a variety of hospitals in Ethiopia, Cameroon, Kenya, and the United States contributed 74 laparoscopic salpingostomy simulation videos. RESULTS: Complete accuracy of AI compared to human assessment ranged from 65-77%. There were no statistical differences in rank mean scores for 3 domains, Flow of Operation, Respect for Tissue, and Economy of Motion, while there were significant differences in ratings for Instrument Handling, Overall Performance, and the total summed score of all 5 domains (Summed). Estimated effect sizes were all less than 0.11, indicating very small practical effect. Estimated intraclass correlation coefficient (ICC) of Summed was 0.72 indicating moderate correlation between AI and Human scores. CONCLUSIONS: Video review using AI technology of global characteristics was similar to that of human review in our laparoscopic training system. Machine learning may help fill an educational gap in LMICs where direct apprenticeship may not be feasible.

Early childhood social disadvantage is associated with poor health behaviours in adulthood.

BACKGROUND: Individual health behaviours are considered important risk factors for cardiometabolic diseases. These behaviours may be socially patterned by early exposure to social disadvantage, but few studies have prospectively tested this hypothesis empirically. AIM: This study investigated whether childhood social disadvantage was associated with likelihood of engaging in less healthy behaviours 40 years later. SUBJECTS AND METHODS: Prospective data were analysed from the New England Family Study, a 2005-2007 adult follow-up of a cohort initiated in 1959-1966 (n = 565). Childhood social environment (age 7 years) was assessed using a cumulative index of socioeconomic and family stability factors. Logistic regression models evaluated associations between social disadvantage and each health-related behaviour and obesity in adulthood. RESULTS: Relative to low disadvantage, higher disadvantage was associated with 3.6-fold greater odds of smoking (95% CI = 1.9-7.0), 4.8-fold greater odds (in women only) of excess alcohol consumption (95% CI = 1.6-14.2) and 2.7-fold greater odds of obesity (95% CI = 1.3-5.5), but was not associated with unhealthy diet or physical inactivity. CONCLUSION: These findings suggest childhood social disadvantage may contribute to adult cardiometabolic disease by predisposing children to adopt certain unhealthy behaviours. If replicated, such findings may support intervention strategies that target social environmental factors and behavioural pathways that are established early in life.