As the number of robotic surgery procedures has increased, so has the importance of evaluating surgical skills in these techniques. It is difficult, however, to automatically and quantitatively evaluate surgical skills during robotic surgery, as these skills are primarily associated with the movement of surgical instruments. This study proposes a deep learning-based surgical instrument tracking algorithm to evaluate surgeons’ skills in performing procedures by robotic surgery. This method overcame two main drawbacks: occlusion and maintenance of the identity of the surgical instruments. In addition, surgical skill prediction models were developed using motion metrics calculated from the motion of the instruments. The tracking method was applied to 54 video segments and evaluated by root mean squared error (RMSE), area under the curve (AUC), and Pearson correlation analysis. The RMSE was 3.52 mm, the AUC of 1 mm, 2 mm, and 5 mm were 0.7, 0.78, and 0.86, respectively, and Pearson’s correlation coefficients were 0.9 on the x-axis and 0.87 on the y-axis. The surgical skill prediction models showed an accuracy of 83% with Objective Structured Assessment of Technical Skill (OSATS) and Global Evaluative Assessment of Robotic Surgery (GEARS). The proposed method was able to track instruments during robotic surgery, suggesting that the current method of surgical skill assessment by surgeons can be replaced by the proposed automatic and quantitative evaluation method.

A deep learning model to predict incident lung cancer from chest radiographs showed added value to the 2021 U.S. Preventive Services Task Force recommendations for low-dose CT lung cancer screening... Background A deep learning (DL) model to identify lung cancer screening candidates based on their chest radiographs requires external validation with a recent real-world non–U.S. sample. Purpose To...

We adopted a vision-based tracking system for augmented reality (AR), and evaluated whether it helped surgeons to localize the recurrent laryngeal nerve (RLN) during robotic thyroid surgery. We constructed an AR image of the trachea, common carotid artery, and RLN using CT images. During surgery, an AR image of the trachea and common carotid artery were overlaid on the physical structures after they were exposed. The vision-based tracking system was activated so that the AR image of the RLN followed the camera movement. After identifying the RLN, the distance between the AR image of the RLN and the actual RLN was measured. Eleven RLNs (9 right, 4 left) were tested. The mean distance between the RLN AR image and the actual RLN was 1.9 ± 1.5 mm (range 0.5 to 3.7). RLN localization using AR and vision-based tracking system was successfully applied during robotic thyroidectomy. There were no cases of RLN palsy. This technique may allow surgeons to identify hidden anatomical structures during robotic surgery.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Cardiorespiratory fitness (CRF), an important index of physical fitness, is the ability to take in and provide oxygen to the exercising muscle. However, despite its importance, the current gold standard for measuring CRF is impractical, requiring a maximal exercise from the participants. Objective: This study aimed to develop a convenient and practical estimation model for CRF using data collected from daily life with a wristwatch-type device. Methods: A total of 191 subjects, aged 20 to 65 years old, participated in this study. Maximal oxygen uptake (VO2max), a standard measure of CRF, was measured with a maximal exercise test. Heart rate (HR) and physical activity data were collected using a commercial wristwatch-type fitness tracker (Fitbit) for 3 consecutive days. Maximal activity energy expenditure (aEEmax) and slope between HR and physical activity were calculated by a linear regression. A VO2max estimation model was built using multiple linear regression with data on age, sex, height, percentage body fat, aEEmax, and the slope. The result was validated with two different cross-validation methods. Results: aEEmax showed a moderate correlation with VO2max (r = 0.50). The correlation coefficient for the multiple linear regression model was 0.81, and the standard error of estimate (SEE) was 3.518 mL/kg/min. The regression model was cross-validated through the predicted residual error sum of square (PRESS). The PRESS correlation coefficient was 0.79, and the PRESS SEE was 3.667 mL/kg/min. The model was further validated by dividing into different subgroups and calculating the constant error (CE) where, the low CE showed that the model does not significantly overestimate or underestimate VO2max. Conclusions: This study proposes a CRF estimation method using data collected by a wristwatch-type fitness tracker without any specific protocol for a wide range of population.