Using Deep Learning Techniques as an Attempt to Create the Most Cost-Effective Screening Tool for Cognitive Decline.

OBJECTIVE: This study aimed to use deep learning (DL) to develop a cost-effective and accessible screening tool to improve the detection of cognitive decline, a precursor of Alzheimer's disease (AD). This study integrating a comprehensive battery of neuropsychological tests adjusted for individual demographic variables such as age, sex, and education level. METHODS: A total of 2,863 subjects with subjective cognitive complaints who underwent a comprehensive neuropsychological assessment were included. A random forest classifier was used to discern the most predictive test combinations to distinguish between dementia and nondementia cases. The model was trained and validated on this dataset, focusing on feature importance to determine the cognitive tests that were most indicative of decline. RESULTS: Subjects had a mean age of 72.68 years and an average education level of 7.62 years. The DL model achieved an accuracy of 82.42% and an area under the curve of 0.816, effectively classifying dementia. Feature importance analysis identified significant tests across cognitive domains: attention was gauged by the Trail Making Test Part B, language by the Boston Naming Test, memory by the Rey Complex Figure Test delayed recall, visuospatial skills by the Rey Complex Figure Test copy score, and frontal function by the Stroop Test Word reading time. CONCLUSION: This study showed the potential of DL to improve AD diagnostics, suggesting that a wide range of cognitive assessments could yield a more accurate diagnosis than traditional methods. This research establishes a foundation for future broader studies, which could substantiate the approach and further refine the screening tool.

Review of Visualization Approaches in Deep Learning Models of Glaucoma.

Glaucoma is a major cause of irreversible blindness worldwide. As glaucoma often presents without symptoms, early detection and intervention are important in delaying progression. Deep learning (DL) has emerged as a rapidly advancing tool to help achieve these objectives. In this narrative review, data types and visualization approaches for presenting model predictions, including models based on tabular data, functional data, and/or structural data, are summarized, and the importance of data source diversity for improving the utility and generalizability of DL models is explored. Examples of innovative approaches to understanding predictions of artificial intelligence (AI) models and alignment with clinicians are provided. In addition, methods to enhance the interpretability of clinical features from tabular data used to train AI models are investigated. Examples of published DL models that include interfaces to facilitate end-user engagement and minimize cognitive and time burdens are highlighted. The stages of integrating AI models into existing clinical workflows are reviewed, and challenges are discussed. Reviewing these approaches may help inform the generation of user-friendly interfaces that are successfully integrated into clinical information systems. This review details key principles regarding visualization approaches in DL models of glaucoma. The articles reviewed here focused on usability, explainability, and promotion of clinician trust to encourage wider adoption for clinical use. These studies demonstrate important progress in addressing visualization and explainability issues required for successful real-world implementation of DL models in glaucoma.

Amblyopia and strabismus by monocular corneal opacity following suspected epidemic keratoconjunctivitis in infancy.

PURPOSE: To identify the long term clinical course of amblyopia and strabismus that developed secondary to a monocular corneal opacity following suspected epidemic keratoconjunctivitis (EKC) in infancy. METHODS: This was a retrospective study analyzing the medical records of seven patients, treated in our clinic, who were followed for more than five years. RESULTS: Four patients in our clinic underwent a corneal ulcer treatment following suspected EKC. Each developed a monocular corneal opacity. Three patients with a chief complaint of corneal opacity were transferred to our clinic from other clinics. These patients had documented histories of treatment for EKC in infancy. All patients were treated with early occlusion therapy, but amblyopia persisted in four patients. Furthermore, all patients had strabismus and showed a significant reduction of stereoscopic vision. CONCLUSIONS: Although infants with EKC are not always cooperative, slit lamp examination should be performed as early as possible, and appropriate medical treatment should be performed, thus reducing the development of corneal opacity. Careful follow up should be regularly performed, and the occurrence of amblyopia or strabismus should be verified at an early stage using visual acuity or ocular alignment examination. Ophthalmologic treatments, including active occlusion therapy, should also be pursued.