Significance: This report illustrates the potential uses of vision data in helping teams select players during the draft.
Purpose: Visual performance has gradually gained recognition in baseball as a tool that can optimize on-field performance. It also may be useful in player development programs that gradually move players toward the major league.
Methods: Recently, over the past 5 years, vision data from six different major league teams were used by the authors to assess prospective players before the annual Major League Baseball (MLB) draft. One thousand three hundred forty-three vision forms were evaluated representing 759 different players. Their vision data were retrospectively analyzed using a novel grading method to advise teams on the visual readiness of prospects for success in MLB.
Results: On a one (best)-to-six (worst) vision scale, the average vision score was 2.080 ± 1.171. Sixty-eight percent (320/473) of the players with good vision scores were drafted, 66% (185/281) of the players with moderate vision scores were drafted, and only 1 player with a poor vision score was drafted. There was a statistically significant difference in the amount of signing bonus received by draftees with better vision scores compared with those with lower vision scores (P < .003 to P < .001). Draftees with the highest vision scores also received the highest signing bonuses as they entered MLB.
Conclusions: For both potential draftees and teams, the vision score seems to be a valuable tool in selecting players for the MLB draft. Adding the pre-draft visual assessment score to a team's projection model could help reduce the uncertainty surrounding the player draft and future service to the team.
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http://dx.doi.org/10.1097/OPX.0000000000001736 | DOI Listing |
J Imaging Inform Med
January 2025
College of Engineering, Department of Computer Engineering, Koç University, Rumelifeneri Yolu, 34450, Sarıyer, Istanbul, Turkey.
This study explores a transfer learning approach with vision transformers (ViTs) and convolutional neural networks (CNNs) for classifying retinal diseases, specifically diabetic retinopathy, glaucoma, and cataracts, from ophthalmoscopy images. Using a balanced subset of 4217 images and ophthalmology-specific pretrained ViT backbones, this method demonstrates significant improvements in classification accuracy, offering potential for broader applications in medical imaging. Glaucoma, diabetic retinopathy, and cataracts are common eye diseases that can cause vision loss if not treated.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, USA.
The Sharp-van der Heijde score (SvH) is crucial for assessing joint damage in rheumatoid arthritis (RA) through radiographic images. However, manual scoring is time-consuming and subject to variability. This study proposes a multistage deep learning model to predict the Overall Sharp Score (OSS) from hand X-ray images.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Data Science and Artificial Intelligence, Sunway University, 47500, Petaling Jaya, Selangor Darul Ehsan, Malaysia.
Precise segmentation of retinal vasculature is crucial for the early detection, diagnosis, and treatment of vision-threatening ailments. However, this task is challenging due to limited contextual information, variations in vessel thicknesses, the complexity of vessel structures, and the potential for confusion with lesions. In this paper, we introduce a novel approach, the MSMA Net model, which overcomes these challenges by replacing traditional convolution blocks and skip connections with an improved multi-scale squeeze and excitation block (MSSE Block) and Bottleneck residual paths (B-Res paths) with spatial attention blocks (SAB).
View Article and Find Full Text PDFClin Optom (Auckl)
January 2025
Research Department, Southern College of Optometry, Memphis, TN, USA.
Purpose: To determine the performance of TOTAL30 for Astigmatism (T30fA; Alcon; Fort Worth, TX, USA) contact lenses (CLs) in existing CL wearers who are also frequent digital device users.
Methods: This 1-month, 3-visit study recruited adult, 18- to 40-year-old subjects who were required to use daily digital devices for at least 8 hours per day. All subjects were refit into T30fA CLs.
Ophthalmol Sci
November 2024
Casey Eye Institute, Oregon Health & Science University, Portland, Oregon.
Objective: This study develops and evaluates multimodal machine learning models for differentiating bacterial and fungal keratitis using a prospective representative dataset from South India.
Design: Machine learning classifier training and validation study.
Participants: Five hundred ninety-nine subjects diagnosed with acute infectious keratitis at Aravind Eye Hospital in Madurai, India.
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