Publications by authors named "Melanie Lubrano"
The integration of Artificial Intelligence (AI) algorithms into pathology practice presents both opportunities and challenges. Although it can improve accuracy and inter-rater reliability, it is not infallible and can produce erroneous diagnoses, hence the need for pathologists to always check predictions. This critical judgment is particularly important when algorithm errors could lead to high-impact negative clinical outcomes, such as missing an invasive carcinoma.
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- The study addresses the challenges of diagnosing head and neck (HN) squamous dysplasias and carcinomas, particularly the variability in grading these lesions, which remains an issue for many pathologists despite updated grading systems.
- Researchers developed a deep learning model trained on a large dataset of over 2000 histological samples to assist pathologists in classifying HN lesions based on the 2022 WHO classification system.
- The model showed high accuracy in classification and introduced a confidence score to help identify reliable predictions, suggesting it could effectively reduce variability in diagnosis and support the integration of AI tools in pathology.
Introduction: Glaucoma and non-arteritic anterior ischemic optic neuropathy (NAION) are optic neuropathies that can both lead to irreversible blindness. Several studies have compared optical coherence tomography angiography (OCTA) findings in glaucoma and NAION in the presence of similar functional and structural damages with contradictory results. The goal of this study was to use a deep learning system to differentiate OCTA in glaucoma and NAION.
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