Three-dimensional virtual planning in mandibular advancement surgery: Soft tissue prediction based on deep learning

The study aimed at developing a deep-learning (DL)-based algorithm to predict the virtual soft tissue profile after mandibular advancement surgery, and to compare its accuracy with the mass tensor model (MTM).Subjects who underwent mandibular advancement surgery were enrolled and divided into a training group and a test group. The DL model was trained using 3D photographs and CBCT data based on surgically achieved mandibular displacements (training group). Soft tissue simulations generated by DL and MTM based on the actual surgical jaw movements (test group) were compared with soft-tissue profiles on postoperative 3D photographs using distance mapping in terms of mean absolute error in the lower face, lower lip, and chin regions.133 subjects were included — 119 in the training group and 14 in the test group. The mean absolute error for DL-based simulations of the lower face region was 1.0 ± 0.6 mm and was significantly lower (p = 0.02) compared with MTM-based simulations (1.5 ± 0.5 mm).ConclusionThe DL-based algorithm can predict 3D soft tissue profiles following mandibular advancement surgery. With a clinically acceptable mean absolute error. Therefore, it seems to be a relevant option for soft tissue prediction in orthognathic surgery. Therefore, it seems to be a relevant options.     

Impact of environmental factors in predicting daily severity scores of atopic dermatitis

BackgroundAtopic dermatitis (AD) is a chronic inflammatory skin disease that affects 20% of children worldwide. Environmental factors including weather and air pollutants have been shown to be associated with AD symptoms. However, the time‐dependent nature of such a relationship has not been adequately investigated. This paper aims to assess whether real‐time data on weather and air pollutants can make short‐term prediction of AD severity scores.MethodsUsing longitudinal data from a published panel study of 177 paediatric patients followed up daily for 17 months, we developed a statistical machine learning model to predict daily AD severity scores for individual study participants. Exposures consisted of daily meteorological variables and concentrations of air pollutants, and outcomes were daily recordings of scores for six AD signs. We developed a mixed‐effect autoregressive ordinal logistic regression model, validated it in a forward‐chaining setting and evaluated the effects of the environmental factors on the predictive performance.ResultsOur model successfully made daily prediction of the AD severity scores, and the predictive performance was not improved by the addition of measured environmental factors. Potential short‐term influence of environmental exposures on daily AD severity scores was outweighed by the underlying persistence of preceding scores.ConclusionsOur data does not offer enough evidence to support a claim that weather or air pollutants can make short‐term prediction of AD signs. Inferences about the magnitude of the effect of environmental factors on AD severity scores require consideration of their time‐dependent dynamic nature.     

Neural underpinnings of superior action prediction abilities in soccer players

The ability to form anticipatory representations of ongoing actions is crucial for effective interactions in dynamic environments. In sports, elite athletes exhibit greater ability than novices in predicting other players’ actions, mainly based on reading their body kinematics. This superior perceptual ability has been associated with a modulation of visual and motor areas by visual and motor expertise. Here, we investigated the causative role of visual and motor action representations in experts’ ability to predict the outcome of soccer actions. We asked expert soccer players (outfield players and goalkeepers) and novices to predict the direction of the ball after perceiving the initial phases of penalty kicks that contained or not incongruent body kinematics. During the task, we applied repetitive transcranial magnetic stimulation (rTMS) over the superior temporal sulcus (STS) and the dorsal premotor cortex (PMd). Results showed that STS-rTMS disrupted performance in both experts and novices, especially in those with greater visual expertise (i.e. goalkeepers). Conversely, PMd-rTMS impaired performance only in expert players (i.e. outfield players and goalkeepers), who exhibit strong motor expertise into facing domain-specific actions in soccer games. These results provide causative evidence of the complimentary functional role of visual and motor action representations in experts’ action prediction.     

Prognostic Tools for Early Mortality in Hemorrhagic Stroke: Systematic Review and Meta-Analysis

Background and Purpose

Several risk scores have been developed to predict mortality in intracerebral hemorrhage (ICH). We aimed to systematically determine the performance of published prognostic tools.

Methods

We searched MEDLINE and EMBASE for prognostic models (published between 2004 and April 2014) used in predicting early mortality (<6 months) after ICH. We evaluated the discrimination performance of the tools through a random-effects meta-analysis of the area under the receiver operating characteristic curve (AUC) or c-statistic. We evaluated the following components of the study validity: study design, collection of prognostic variables, treatment pathways, and missing data.

Results

We identified 11 articles (involving 41,555 patients) reporting on the accuracy of 12 different tools for predicting mortality in ICH. Most studies were either retrospective or post-hoc analyses of prospectively collected data; all but one produced validation data. The Hemphill-ICH score had the largest number of validation cohorts (9 studies involving 3,819 patients) within our systematic review and showed good performance in 4 countries, with a pooled AUC of 0.80 [95% confidence interval (CI)=0.77-0.85]. We identified several modified versions of the Hemphill-ICH score, with the ICH-Grading Scale (GS) score appearing to be the most promising variant, with a pooled AUC across four studies of 0.87 (95% CI=0.84-0.90). Subgroup testing found statistically significant differences between the AUCs obtained in studies involving Hemphill-ICH and ICH-GS scores (p=0.01).

Conclusions

Our meta-analysis evaluated the performance of 12 ICH prognostic tools and found greater supporting evidence for 2 models (Hemphill-ICH and ICH-GS), with generally good performance overall.     

Birthweight, early passive respiratory system mechanics, and ventilator requirements as predictors of outcome in premature infants with respiratory failure.

Early respiratory mechanics have been reported to predict outcome in newborns with respiratory failure. However, it remains unknown whether measurements of pulmonary function add significantly to the predictive value of more readily available variables The present study was designed to answer this question. Passive respiratory system mechanics were measured by an airway occlusion technique in 104 ventilator-dependent premature infants between 6 and 48 hours of life and corrected for infant size. A ventilation index [FiO2 x mean airway pressure (MAP)] was calculated at the time of pulmonary function testing. Poor outcome was defined as death from respiratory failure or need for supplemental oxygen at 28 days. Stepwise logistic function regression examined whether ventilation index and respiratory mechanics added predictive power over and above birthweight. Five infants died, and 45 patients required supplemental oxygen at 28 days. Birthweight was a strong predictor and would have entered the logistic model first in any case. Ventilation index added significantly to the predictive model (P = 0.038). Respiratory system conductance (P = 0.15) and compliance (P = 0.93) entered on the third and last step, respectively. We conclude that in premature infants with respiratory failure, birthweight is a strong predictor of outcome. Early ventilator requirements but not respiratory system mechanics, add significantly to this predictive model.