Automated 3D Perioral Landmark Detection Using High-Resolution Network: Artificial Intelligence-Based Anthropometric Analysis.

BACKGROUND: 3D facial stereophotogrammetry, as a convenient, non-invasive and highly reliable evaluation tool, has shown great potential in pre-operative planning and treatment efficacy evaluation of plastic surgery in recent years. However, it requires manual identification of facial landmarks by trained evaluators to obtain anthropometric data, which consumes large amount of time and effort. Automatic 3D facial landmark localization may facilitate fast data acquisition and eliminate evaluator error. OBJECTIVES: In this paper, we propose a novel deep-learning method based on dimension-transformation and key-point detection for automated 3D perioral landmark annotation. METHODS: The 3D facial model is transformed into 2D images on which High-Resolution Network is implemented for key point detection. The 2D coordinates of key points are then mapped back to the 3D model using mathematical methods to obtain the 3D landmark coordinates. This program was trained with 120 facial models and validated in 50 facial models. RESULTS: Our approach achieved satisfactory accuracy of 1.30 ± 0.68 mm error in landmark detection with an average processing time of 5.2 ± 0.21 seconds per model. And subsequent analysis based on these landmarks showed an error of 0.87 ± 1.02 mm for linear measurements and 5.62 ± 6.61° for angular measurements. CONCLUSIONS: This automated 3D perioral landmarking method could serve as an effective tool that enables fast and accurate anthropometric analysis of lip morphology for plastic surgery and aesthetic procedures.

Tissue Recognition Based on Electrical Impedance Classified by Support Vector Machine in Spinal Operation Area.

OBJECTIVE: One of the major difficulties in spinal surgery is the injury of important tissues caused by tissue misclassification, which is the source of surgical complications. Accurate recognization of the tissues is the key to increase safety and effect as well as to reduce the complications of spinal surgery. The study aimed at tissue recognition in the spinal operation area based on electrical impedance and the boundaries of electrical impedance between cortical bone, cancellous bone, spinal cord, muscle, and nucleus pulposus. METHODS: Two female white swines with body weight of 40 kg were used to expose cortical bone, cancellous bone, spinal cord, muscle, and nucleus pulposus under general anesthesia and aseptic conditions. The electrical impedance of these tissues at 12 frequencies (in the range of 10-100 kHz) was measured by electrochemical analyzer with a specially designed probe, at 22.0-25.0°C and 50%-60% humidity. Two types of tissue recognition models - one combines principal component analysis (PCA) and support vector machine (SVM) and the other combines combines SVM and ensemble learning - were constructed, and the boundaries of electrical impedance of the five tissues at 12 frequencies of current were figured out. Linear correlation, two-way ANOVA, and paired T-test were conducted to analyze the relationship between the electrical impedance of different tissues at different frequencies. RESULTS: The results suggest that the differences of electrical impedance mainly came from tissue type (p < 0.0001), the electrical impedance of five kinds of tissue was statistically different from each other (p < 0.0001). The tissue recognition accuracy of the algorithm based on principal component analysis and support vector machine ranged from 83%-100%, and the overall accuracy was 95.83%. The classification accuracy of the algorithm based on support vector machine and ensemble learning was 100%, and the boundaries of electrical impedance of five tissues at various frequencies were calculated. CONCLUSION: The electrical impedance of cortical bone, cancellous bone, spinal cord, muscle, and nucleus pulposus had significant differences in 10-100 kHz frequency. The application of support vector machine realized the accurate tissue recognition in the spinal operation area based on electrical impedance, which is expected to be translated and applied to tissue recognition during spinal surgery.

Systemic immune inflammation index and system inflammation response index are potential biomarkers of atrial fibrillation among the patients presenting with ischemic stroke.

BACKGROUND: Chronic inflammatory disorders in atrial fibrillation (AF) contribute to the onset of ischemic stroke. Systemic immune inflammation index (SIII) and system inflammation response index (SIRI) are the two novel and convenient measurements that are positively associated with body inflammation. However, little is known regarding the association between SIII/SIRI with the presence of AF among the patients with ischemic stroke. METHODS: A total of 526 ischemic stroke patients (173 with AF and 353 without AF) were consecutively enrolled in our study from January 2017 to June 2019. SIII and SIRI were measured in both groups. Logistic regression analysis was used to analyse the potential association between SIII/SIRI and the presence of AF. Finally, the correlation between hospitalization expenses, changes in the National Institutes of Health Stroke Scale (NIHSS) scores and SIII/SIRI values were measured. RESULTS: In patients with ischemic stroke, SIII and SIRI values were significantly higher in AF patients than in non-AF patients (all p < 0.001). Moreover, with increasing quartiles of SIII and SIRI in all patients, the proportion of patients with AF was higher than that of non-AF patients gradually. Logistic regression analyses demonstrated that log-transformed SIII and log-transformed SIRI were independently associated with the presence of AF in patients with ischemic stroke (log-transformed SIII: odds ratio [OR]: 1.047, 95% confidence interval CI = 0.322-1.105, p = 0.047; log-transformed SIRI: OR: 6.197, 95% CI = 2.196-17.484, p = 0.001). Finally, a positive correlation between hospitalization expenses, changes in the NIHSS scores and SIII/SIRI were found, which were more significant in patients with AF (all p < 0.05). CONCLUSIONS: Our study suggests SIII and SIRI are convenient and effective measurements for predicting the presence of AF in patients with ischemic stroke. Moreover, they were correlated with increased financial burden and poor short-term prognosis in AF patients presenting with ischemic stroke.