Computer-Controlled Virtual Humans in Patient-Facing Systems: Systematic Review and Meta-Analysis.

BACKGROUND: Virtual humans (VH) are computer-generated characters that appear humanlike and simulate face-to-face conversations using verbal and nonverbal cues. Unlike formless conversational agents, like smart speakers or chatbots, VH bring together the capabilities of both a conversational agent and an interactive avatar (computer-represented digital characters). Although their use in patient-facing systems has garnered substantial interest, it is unknown to what extent VH are effective in health applications. OBJECTIVE: The purpose of this review was to examine the effectiveness of VH in patient-facing systems. The design and implementation characteristics of these systems were also examined. METHODS: Electronic bibliographic databases were searched for peer-reviewed articles with relevant key terms. Studies were included in the systematic review if they designed or evaluated VH in patient-facing systems. Of the included studies, studies that used a randomized controlled trial to evaluate VH were included in the meta-analysis; they were then summarized using the PICOTS framework (population, intervention, comparison group, outcomes, time frame, setting). Summary effect sizes, using random-effects models, were calculated, and the risk of bias was assessed. RESULTS: Among the 8,125 unique records identified, 53 articles describing 33 unique systems, were qualitatively, systematically reviewed. Two distinct design categories emerged - simple VH and VH augmented with health sensors and trackers. Of the 53 articles, 16 (26 studies) with 44 primary and 22 secondary outcomes were included in the meta-analysis. Meta-analysis of the 44 primary outcome measures revealed a significant difference between intervention and control conditions, favoring the VH intervention (SMD = .166, 95% CI .039-.292, P=.012), but with evidence of some heterogeneity, I2=49.3%. There were more cross-sectional (k=15) than longitudinal studies (k=11). The intervention was delivered using a personal computer in most studies (k=18), followed by a tablet (k=4), mobile kiosk (k=2), head-mounted display (k=1), and a desktop computer in a community center (k=1). CONCLUSIONS: We offer evidence for the efficacy of VH in patient-facing systems. Considering that studies included different population and outcome types, more focused analysis is needed in the future. Future studies also need to identify what features of virtual human interventions contribute toward their effectiveness.

The efficacy of machine learning models in forecasting treatment failure in thoracolumbar burst fractures treated with short-segment posterior spinal fixation.

BACKGROUND: Although short-segment posterior spinal fixation (SSPSF) has shown promising clinical outcomes in thoracolumbar burst fractures, the treatment may be prone to a relatively high failure rate. This study aimed to assess the effectiveness of machine learning models (MLMs) in predicting factors associated with treatment failure in thoracolumbar burst fractures treated with SSPSF. METHODS: A retrospective review of 332 consecutive patients with traumatic thoracolumbar burst fractures who underwent SSPSF at our institution between May 2016 and May 2023 was conducted. Patients were categorized into two groups based on treatment outcome (failure or non-failure). Potential risk factors for treatment failure were compared between the groups. Four MLMs, including random forest (RF), logistic regression (LR), support vector machine (SVM), and k-nearest neighborhood (k-NN), were employed to predict treatment failure. Additionally, LR and RF models were used to assess factors associated with treatment failure. RESULTS: Of the 332 included patients, 61.4% were male (n = 204), and treatment failure was observed in 44 patients (13.3%). Logistic regression analysis identified Load Sharing Classification (LSC) score, lack of index level instrumentation, and interpedicular distance (IPD) as factors associated with treatment failure (P < 0.05). All models demonstrated satisfactory performance. RF exhibited the highest accuracy in predicting treatment failure (accuracy = 0.948), followed by SVM (0.933), k-NN (0.927), and LR (0.917). Moreover, the RF model outperformed other models in terms of sensitivity and specificity (sensitivity = 0.863, specificity = 0.959). The area under the curve (AUC) for RF, LR, SVM, and k-NN was 0.911, 0.823, 0.844, and 0.877, respectively. CONCLUSIONS: This study demonstrated the utility of machine learning models in predicting treatment failure in thoracolumbar burst fractures treated with SSPSF. The findings support the potential of MLMs to predict treatment failure in this patient population, offering valuable prognostic information for early intervention and cost savings.

Analysis of factors associated with the failure of treatment in thoracolumbar burst fractures treated with short-segment posterior spinal fixation.

BACKGROUND: The treatment of thoracolumbar burst fractures continues to pose challenges. Although short-segment posterior spinal fixation (SSPSF) has shown satisfactory clinical outcomes, it is accompanied by a relatively high rate of treatment failure. This study aimed to assess factors associated with treatment failure in thoracolumbar burst fractures treated with SSPSF. METHODS: The clinical data of 241 consecutive patients with a traumatic thoracolumbar burst fracture who underwent SSPSF at our center between Apr 2016 and Apr 2021 were retrospectively reviewed. Patients were divided into two groups (failure of the treatment group and non-failure of the treatment group). We compared potential risk factors for the failure of treatment including age, gender, body mass index, smoking, diabetes, vertebral body compression rate, use of crosslinks, percentage of anterior height compression, presence of index level instrumentation, Cobb angle, interpedicular distance (IPD), canal compromise, Load Sharing Classification (LSC) score, use of posterolateral fusion, and pain intensity between the two groups. RESULTS: A sum of 137 (56.8%) males and 104 (43.2%) females were enrolled where the mean age and follow-up of the participants were 48.34 ± 10.23 years and 18.67 ± 5.23 months, respectively. Treatment failure was observed in 34 cases (14.1%). The results of the binary logistic regression analysis revealed that the lack of index level instrumentation (OR 2.21; 95% CI 1.78-3.04; P = 0.014), LSC score (odds ratio [OR] 2.64; 95% confidence interval [95% CI], 1.34-3.77; P = 0.007), and IPD (OR 1.77; 95% CI 1.51-2.67; P = 0.023) were independently associated with a higher rate of failure of treatment. CONCLUSIONS: The findings of this study revealed that increased rates of treatment failure in thoracolumbar burst fractures treated with SSPSF were associated with factors such as the absence of index level instrumentation, higher LSC scores, and larger IPD. These findings could be helpful in the proper management of patients with unstable thoracolumbar burst fractures.