Authorship Correction: International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study (Preprint)

UNSTRUCTURED Authorship Correction: International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study In “International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study” (J Med Internet Res 2021 Oct 11;23(10):e31400. doi: 10.2196/31400), two errors were noted. Due to a system error, the equal contribution of the last three authors was not noted. To correct this under the JMIR parameters allowing only one equal contribution footnote, we are implementing the following changes. In the originally published paper, equal contribution was noted as: “Griffin M Weber MD, PhD1*, Harrison G Zhang1*, Sehi L'Yi PhD1*,… *These authors contributed equally” This has been corrected to: “Griffin M Weber MD, PhD1*, Harrison G Zhang1*, Sehi L'Yi PhD1*,… Tianxi Cai ScD1*‡, Andrew M South MD, MS36*, Gabriel A Brat MD, MPH1*… *These authors contributed equally” Additionally, the Authors’ Contribution section has been updated to include: “These authors contributed equally: Griffin M Weber MD, PhD, Harrison G Zhang, Sehi L’Yi PhD. These authors jointly supervised the work: Tianxi Cai ScD, Andrew M South MD, MS, Gabriel A Brat MD.” The correction will appear in the online version of the paper on the JMIR Publications website on November 2, 2021, together with the publication of this correction notice.

Development of Infrared-Guided Missile Precision Detection Simulator

In order to carry out various detections of system indicators during the research and development phase of infrared guided missiles, the article first analyzes several main design schemes of the infrared guided missile detection simulator and finds that it has the disadvantages of difficult processing technology and low detection accuracy. The overall structure of the detection device was designed, including the design of the rotation and swing mechanism, lens mechanism, optical system and control system. The optical system error analysis is performed on the infrared guided missile detection simulator. The position of the receiving light source is obtained by analyzing the mechanism characteristics of the detection simulator and the kinematics model of the device. The phase difference analysis of the eccentricity and tilt system is obtained. The image quality was evaluated by the optical transfer function (MTF), and the system error was found to meet the requirements of imaging quality. The experiments show that the simulation of 1.7~4.9 um medium wave infrared dynamic target signals provides an accurate and reasonable experimental environment for the missile and the verification of the light source target and meets the experimental requirements.

Development of novel artificial intelligence systems to predict facial morphology after orthognathic surgery and orthodontic treatment in Japanese patients

From a socio-psychological standpoint, improving the morphology of the facial soft-tissues is regarded as an important therapeutic goal in modern orthodontic treatment. Currently, many of the algorithms used in commercially available software programs that are said to provide the function of performing profile prediction are based on the false assumption that the amount of movement of hard-tissue and soft-tissue has a proportional relationship. The specification of the proportionality constant value depends on the operator, and there is little evidence to support the validity of the prediction result. Thus, the present study attempted to develop artificial intelligence (AI) systems that predict the three-dimensional (3-D) facial morphology after orthognathic surgery and orthodontic treatment based on the results of previous treatment. This was a retrospective study in a secondary adult care setting. A total of 137 patients who underwent orthognathic surgery (n = 72) and orthodontic treatment with four premolar extraction (n = 65) were enrolled. Lateral cephalograms and 3-D facial images were obtained before and after treatment. We have developed two AI systems to predict facial morphology after orthognathic surgery (System S) and orthodontic treatment (System E) using landmark-based geometric morphometric methods together with deep learning methods; where cephalometric changes during treatment and the coordinate values of the faces before treatment were employed as predictive variables. Eleven-fold cross-validation showed that the average system errors were 0.94 mm and 0.69 mm for systems S and E, respectively. The total success rates, when success was defined by a system error of < 1 mm, were 54% and 98% for systems S and E, respectively. The total success rates when success was defined by a system error of < 2 mm were both 100%. AI systems to predict facial morphology after treatment were therefore confirmed to be clinically acceptable.