## 4 Discussion  
The use of the LESS to assess injury risk is common in sport science and clinical practice [9; 22], but scoring is time consuming, clinician-dependent, and generally inaccessible for large-scale screening outside of elite sports. This study provides evidence that the LESS can be automated using deep-learning-based computer vision combined with machine learning methods without the need for 3D motion analysis or depth sensor cameras. A clear benefit of automating LESS scoring is immediate feedback to end-users. The successful application of this method paves the way for the automatic detection of individuals at high risk of injury using smartphone-based applications of LESS videos (Video S1: [[https://youtu.be/q1](https://youtu.be/q1) wiGt4K8 MU]([https://youtu.be/q1](https://youtu.be/q1) wiGt4K8 MU)). The characteristics of an ideal injury risk screening tool are good reliability, validity, and predictive value for injury incidence. In practical or field settings, an ideal screening method is easy to administer without an expert, and has minimal financial, spatial, and temporal requirements. Ideally, the screening tool provides immediate results and is accessible to everyone, from the recreational to elite athlete, as well as novice to expert rater. Overall, the LESS responds to most of these stated requirements. The test demonstrates acceptable reliability and validity [10; 11; 23], as well as predictive value for non-contact ACL injury using a threshold of 5 errors [12]. The inter-rater reliability of the total LESS score is good to excellent, with ICC ranging from 0.83 to 0.92 [10; 11; 23] and typical errors at 0.71 LESS errors [10]. The results from the current study indicate that the typical errors from the automated processing and scoring of the LESS through computer vision when applying the random forest model (Table 2) are less than half an error greater than scores taken from two expert clinicians. In fact, certain individual LESS items yield suboptimal psychometric properties between raters and 3D motion analysis [23]. More specifically, no significant agreement between raters was found for knee and trunk flexion at IC, and poor agreement between rater and 3D motion capture analysis was found for knee flexion at IC, lateral trunk flexion at IC, and symmetric foot contact at IC [23]. As such, a certain level of disagreement between clinical ratings and computerised ratings is expected. As seen in Figures 4 and 5, the estimated error is not uniform across the range of LESS scores, but depends on the target. For example, trials with a low actual LESS score tend to have a positive error (the prediction is an overestimation) and trials with a higher actual LESS score tend to have a negative error (the prediction is an underestimation). If these biases stemmed from the over representation of the mid-range LESS values (i.e., majority of LESS scores falling in the range 4-6), the balanced dataset should have provided more accurate predictions, which was not the case. It might be possible  
Figure 5: Bland-Altman [21] plots depicting the difference in predicted (automated) and actual (clinical) LESS scores versus the mean scores with (**A**) conventional 95% limits of agreement (mean difference \(\pm\) 1.96 standard deviation), and (**B**) regression-based limits of agreement (regressed difference between methods on the mean of the two methods \(\pm\) 2.46 standard deviation of the residual). to attempt correcting predictions to improve accuracy in future work using probability calibration methods, such as Platt Scaling and Isotonic Regression. The large errors in LESS score predictions were attributed to inaccurate foot and IC key frame detection. The newest body model in OpenPose (Body 25) contains 25 points, including coordinates that define the feet and enable computations of angles at the ankles [24]. Improving the LESS score automation relies on either refining body part detection or training a new system specifically to solve this problem.