Research Articles featuring Altris Inc.
Authors: Tahm Spitznagel, Arin Sheikh, David Isztl, Rui Santos, Blaise Thomson, Matthias Dieter Becker1, Gábor Márk Somfai
Department of Ophthalmology, Stadtspital Zürich, Zurich, Switzerland; Spross Research Institute, Zurich, Switzerland; University of Zurich, Zurich; Bitfount Ltd, London, UK; Department of Ophthalmology, University of Heidelberg, Heidelberg, Germany; Department of Ophthalmology, Semmelweis University, Budapest, Hungary
Purpose: To quantify the accuracy of geographic atrophy (GA) documentation in electronic health records (EHR) by comparing it with an OCT-based manual grading and AI-assisted screening, and to identify discrepancies between documented and OCT-confirmed GA in clinical practice.
Result: AI-assisted OCT screening combined with manual validation revealed substantial underdocumentation of OCT-confirmed GA in the EHR. Although the AI model demonstrated high specificity, a relevant proportion of GA cases remained undetected by both automated screening and clinical documentation. These findings highlight the need for improved GA recognition and documentation workflows and support the incorporation of AI tools into clinical practice.
Authors: Joel Pearlman, Roger Goldberg, Albert Edwards, Margaret Chang, Mark Barakat, Nicholas Fuerst, Luis Monsalve, Ryan Lebien, Neali Austin, Yasmin McQuinlan, Dipti Rao, Pearse Keane, Franziska Bucher, Blaise Thomson.
Retina Consultants Medical Group, Bay Area Retina Associates, Sterling Vision, Retina Macula Institute of Arizona, Ora Clinical, Bitfount, Moorfields Eye Hospital, and Boehringer Ingelheim.
Purpose: Artificial Intelligence promises to revolutionize clinical trials via increased patient yields, reduced screen failure rates and accelerated timelines. Despite the promise, there is limited published research on the idea in real trial settings.
Result: This pilot study showed that an AI analysis tool limited to image analysis was able to roughly double the number of subjects enrolled by participating sites. Further, the AI tool may have reduced screen failure rates. We expect that joining image analysis with the review of inclusion and exclusion criteria available in medical records would further accelerate recruitment. The study also identified several hurdles in implementation – the approach requires sites to adjust their recruitment practices and sponsors need to invest in translating their inclusion/exclusion criteria for AI models.
M. Lukic, MD, Retina Specialist, Chief Medical officer at Ascento-CDA, Oculomics Startup
Texture analysis of OCT phantoms
We investigated texture analysis of optical coherence tomography (OCT) phantom images. Semi-automated classifier, based on principal component analysis (PCA) and support vector machines (SVM), enables the classification of phantoms with various contents of medium and scatterers.
M Kulmaganbetov, J Albon, N White, JE Morgan, Clinical and Translational Biophotonics, JTu3A. 23
Among neuroretinal degenerations, glaucoma and age-related macular degeneration (AMD) have become the most frequent reasons for irreversible blindness globally. Among the causes of the elderly and senile dementia, Alzheimer’s disease (AD) has the leading position, the early ocular symptoms of which can potentially be a prognostic factor. The aim of this thesis was the early in vivo ligand-free detection of degenerative changes in the inner and outer retinal layers, which was possible using high-resolution optical coherence tomography (OCT) with the machine learning (ML) algorithms: support vector machine (SVM) and principal component analysis (PCA).
M Kulmaganbetov – 2021, Cardiff University