Carlos Palacio-Lascano

Introduction: The utility of pancreaticoduodenectomy (PD) for high-grade traumatic injuries remains unclear and data surrounding its use are limited. We hypothesized that PD does not result in improved outcomes when compared with non-PD surgical management of grade IV–V pancreaticoduodenal injuries. Methods: This is a retrospective, multicenter analysis from 35 level 1 trauma centers from January 2010 to December 2020. Included patients were ≥15 years of age with the American Association for the Surgery of Trauma grade IV–V duodenal and/or pancreatic injuries. The study compared operative repair strategy: PD versus non-PD. Results: The sample (n=95) was young (26 years), male (82%), with predominantly penetrating injuries (76%). There was no difference in demographics, hemodynamics, or blood product requirement on presentation between PD (n=32) vs non-PD (n=63). Anatomically, PD patients had more grade V duodenal, grade V pancreatic, ampullary, and pancreatic ductal injuries compared with non-PD patients (all p=0.028). Conclusion: While PD patients did not have worse hemodynamics or blood product requirements on admission, they sustained more complex anatomic injuries and had more GI complications and longer LOS than non-PD patients. We suggest that the role of PD should be limited to cases of massive destruction of the pancreatic head and ampullary complex, given the likely procedure-related morbidity and adverse outcomes when compared with non-PD management. Level of evidence: IV, Multicenter retrospective comparative study.

Background: Gait recognition has been applied in the prediction of the probability of elderly flat ground fall, functional evaluation during rehabilitation, and the training of patients with lower extremity motor dysfunction. Gait distinguishing between seemingly similar kinematic patterns associated with different pathological entities is a challenge for the clinician. How to realize automatic identification and judgment of abnormal gait is a significant challenge in clinical practice. The long-term goal of our study is to develop a gait recognition computer vision system using artificial intelligence (AI) and machine learning (ML) computing. This study aims to find an optimal ML algorithm using computer vision techniques and measure variables from lower limbs to classify gait patterns in healthy people. The purpose of this study is to determine the feasibility of computer vision and machine learning (ML) computing in discriminating different gait patterns associated with flat-ground falls. Methods: We used the Kinect^® Motion system to capture the spatiotemporal gait data from seven healthy subjects in three walking trials, including normal gait, pelvic-obliquity-gait, and knee-hyperextension-gait walking. Four different classification methods including convolutional neural network (CNN), support vector machine (SVM), K-nearest neighbors (KNN), and long short-term memory (LSTM) neural networks were used to automatically classify three gait patterns. Overall, 750 sets of data were collected, and the dataset was divided into 80% for algorithm training and 20% for evaluation. Results: The SVM and KNN had a higher accuracy than CNN and LSTM. The SVM (94.9 ± 3.36%) had the highest accuracy in the classification of gait patterns, followed by KNN (94.0 ± 4.22%). The accuracy of CNN was 87.6 ± 7.50% and that of LSTM 83.6 ± 5.35%. Conclusions: This study revealed that the proposed AI machine learning (ML) techniques can be used to design gait biometric systems and machine vision for gait pattern recognition. Potentially, this method can be used to remotely evaluate elderly patients and help clinicians make decisions regarding disposition, follow-up, and treatment.