ArticleSports medicine - open2026
Camera-Based AI-Assisted Scoring for the Balance Error Scoring System: A Cross-sectional Validity and Agreement Study.
Article in Sports medicine - open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
backgroundThe Balance Error Scoring System (BESS) is widely used to assess static postural control in sports medicine and rehabilitation. However, conventional real-time scoring is observer dependent and may be influenced by rater experience, viewing angle, attention, and the interpretation of transient or overlapping errors. This study evaluated the criterion-related validity and agreement of a camera-based artificial intelligence (AI)-assisted BESS relative to consensus video-review manual scoring and compared its error magnitude with on-site manual scoring.
methodsThis single-center cross-sectional study included 80 of 84 recruited participants: 40 healthy controls, 20 with chronic ankle instability (CAI), and 20 after anterior cruciate ligament reconstruction (ACLR). All participants completed the standard six-condition BESS protocol. Scores were obtained using AI-assisted, on-site manual, and consensus video-review manual scoring; the latter was prespecified as the pragmatic manual reference standard. Primary total-score analyses included Pearson correlation, the intraclass correlation coefficient (ICC) with 95% confidence intervals (CIs), Bland-Altman mean difference and 95% limits of agreement (LoA), mean absolute error (MAE), and root mean square error (RMSE). Condition- and group-specific analyses were exploratory.
resultsFor the total BESS score, AI-assisted scoring versus the reference showed r = 0.989, ICC = 0.989 (95% CI 0.982-0.993), a mean difference of 0.188 errors, 95% LoA from -2.063 to 2.438 errors, an MAE of 0.787 errors, and an RMSE of 1.157 errors. On-site manual scoring versus the reference showed r = 0.922, ICC = 0.920 (95% CI 0.877-0.948), a mean difference of -0.612 errors, 95% LoA from -6.680 to 5.455 errors, an MAE of 2.212 errors, and an RMSE of 3.136 errors. Condition-specific discrepancies were larger in the single-leg and tandem-stance conditions. Exploratory group comparisons showed higher total BESS scores in individuals with CAI than in healthy controls.
conclusionsCamera-based AI-assisted BESS scoring showed strong total-score agreement with the pragmatic manual reference standard and smaller discrepancies than on-site manual scoring. This approach may support more standardized BESS assessment; however, external validation and longitudinal reliability testing are required before interchangeability with manual scoring can be considered.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.