Automatic modeling of pectus excavatum corrective prosthesis using artificial neural networks

Study on quantitative diagnosis model of TCM syndromes of post-stroke depression based on combination of disease and syndrome

BACKGROUND

Post-stroke depression (PSD) is one of the most common stroke complications with high morbidity. Researchers have done much clinical research on Traditional Chinese Medicine (TCM) treatment, but very little research on diagnosis. Based on the thought of combination of disease and syndrome, this study will establish a unified and objective quantitative diagnosis model of TCM syndromes of PSD, so as to improve the clinical diagnosis and treatment of PSD.

OBJECTIVE

First: To establish a unified and objective quantitative diagnosis model of TCM syndromes in PSD under different disease courses, and identify the corresponding main, secondary, and concurrent symptoms, which are based on the weighting factor of each TCM symptom. Second: To find out the relationship between different stages of PSD and TCM syndromes. Clarify the main syndrome types of PSD under different stages of disease. Reveal the evolution and progression mechanism of TCM syndromes of PSD.

METHODS AND ANALYSIS

This is a retrospective study of PSD TCM diagnosis. Three hundred patients who were hospitalized in the First Teaching Hospital of Tianjin University of TCM with complete cases from January 2014 to January 2019 are planned to be recruited. The study will mainly collect the diagnostic information from the cases, find the related indicators of TCM and Western medicine in PSD, and clarify the relationship between different disease stages and TCM syndromes. Finally, the PSD TCM syndrome quantitative diagnosis model will be established based on the operation principle of Back Propagation (BP) artificial neural network.

CONCLUSION

To collect sufficient medical records and establish models to speed up the process of TCM diagnosis.

Feasibility and Clinical Effectiveness of Three-Dimensional Printed Model-Assisted Nuss Procedure

BACKGROUND

The Nuss procedure is a minimally invasive technique for correcting pectus excavatum. We hypothesized that three-dimensional (3D) simulation may shorten operation time and provide better morphologic outcome. This study aimed to demonstrate the feasibility of the 3D model-assisted Nuss procedure and to compare its potential benefits with those of the traditional Nuss procedure.

METHODS

We simulated the targeted curvature, length, and planned intercostal space of a metallic bar, based on the preoperative chest computed tomographic images. After the use of a 3D printing technique, a plastic template bar was produced and sterilized. The metallic bar was bent and placed at the planned intercostal space accordingly. The patients' characteristics, total number of pectus bar placement, total operation time, and improvement percentage of Haller indices were compared with patients who underwent the traditional Nuss procedure.

RESULTS

A total of 419 patients underwent the Nuss procedure from January 2010 to July 2017 in our hospital, and 357 patients were eligible and enrolled for the following analysis. Fifteen patients underwent 3D simulation. After performing propensity-score matching analysis, the 3D printing group had a shorter operative time (60.36 versus 74.34 minutes, p < 0.001), fewer metallic bar placements (1.00 versus 1.36 bars, p < 0.001), and better improvement percentages in the Haller indices (20.34% versus 10.06%, p < 0.001) compared with the traditional Nuss procedure.

CONCLUSIONS

In this preliminary study, 3D-printed model-assisted Nuss procedure may provide benefits of shorter operative time, fewer metallic bar insertions, and comparable morphologic outcome by preoperative simulation.

The superior fault tolerance of artificial neural network training with a fault/noise injection-based genetic algorithm

Artificial neural networks (ANNs) are powerful computational tools that are designed to replicate the human brain and adopted to solve a variety of problems in many different fields. Fault tolerance (FT), an important property of ANNs, ensures their reliability when significant portions of a network are lost. In this paper, a fault/noise injection-based (FIB) genetic algorithm (GA) is proposed to construct fault-tolerant ANNs. The FT performance of an FIB-GA was compared with that of a common genetic algorithm, the back-propagation algorithm, and the modification of weights algorithm. The FIB-GA showed a slower fitting speed when solving the exclusive OR (XOR) problem and the overlapping classification problem, but it significantly reduced the errors in cases of single or multiple faults in ANN weights or nodes. Further analysis revealed that the fit weights showed no correlation with the fitting errors in the ANNs constructed with the FIB-GA, suggesting a relatively even distribution of the various fitting parameters. In contrast, the output weights in the training of ANNs implemented with the use the other three algorithms demonstrated a positive correlation with the errors. Our findings therefore indicate that a combination of the fault/noise injection-based method and a GA is capable of introducing FT to ANNs and imply that the distributed ANNs demonstrate superior FT performance.