Publicação
OCTeAi: integration of artificial intelligence algorithms in the domain of optical coherence tomography (OCT)
| Resumo: | OCT exams are increasingly used for the screening of clinical pathologies in ophthalmology. With an aging population, yet with its associated longevity, these exams are becoming more frequent as a way to assess the health of the retina and surrounding tissues. These exams are expensive, and therefore, are much more common in hospital centres, as they are the ones that serve the majority of the Portuguese population. Doctors sometimes see up to 24 patients in a morning, which is significant when considering that for each patient there may be a request for this exam, along with the time required for its prior or precise execution. These numbers contribute to making the whole process a “burden” for both patients and clinicians. One way to attempt to optimize the ophthalmology field, particularly regarding OCT, is the introduction of Artificial Intelligence, bringing advantages for both doctors and patients, especially by reducing potential omissions by clinicians and decreasing patient wait times. This dissertation aims to study the applicability of an algorithm or set of algorithms and Machine Learning (ML) techniques in the medical field of ophthalmology, specifically in the execution of OCT and the subsequent identification of pathologies that are accepted by the medical community. This work also seeks to develop a model or models that meet the desired conditions and can be applied in real-world situations. The project was divided into five key parts: establishing a cooperation protocol with a hospital entity to gather a dataset based on real and local patients; surveying the state of the art regarding applied algorithms and optimization in published datasets, and identifying the relevant data for feeding/training the model; implementing a model for comparison and establishing a reference scenario; applying the model to the mentioned scenario with an evaluation of the impact of possible parameter adjustments on model performance and estimating potential gains. |
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| Autores principais: | Duarte, Tiago José Martins |
| Assunto: | Artificial intelligence Optical coherence tomography (OCT) Transfer learning Convolutional neural networks (CNN) Computer-aided diagnosis Ensemble learning Medical image classification Ophthalmology Retina |
| Ano: | 2025 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade de Aveiro |
| Idioma: | inglês |
| Origem: | RIA - Repositório Institucional da Universidade de Aveiro |
| Resumo: | OCT exams are increasingly used for the screening of clinical pathologies in ophthalmology. With an aging population, yet with its associated longevity, these exams are becoming more frequent as a way to assess the health of the retina and surrounding tissues. These exams are expensive, and therefore, are much more common in hospital centres, as they are the ones that serve the majority of the Portuguese population. Doctors sometimes see up to 24 patients in a morning, which is significant when considering that for each patient there may be a request for this exam, along with the time required for its prior or precise execution. These numbers contribute to making the whole process a “burden” for both patients and clinicians. One way to attempt to optimize the ophthalmology field, particularly regarding OCT, is the introduction of Artificial Intelligence, bringing advantages for both doctors and patients, especially by reducing potential omissions by clinicians and decreasing patient wait times. This dissertation aims to study the applicability of an algorithm or set of algorithms and Machine Learning (ML) techniques in the medical field of ophthalmology, specifically in the execution of OCT and the subsequent identification of pathologies that are accepted by the medical community. This work also seeks to develop a model or models that meet the desired conditions and can be applied in real-world situations. The project was divided into five key parts: establishing a cooperation protocol with a hospital entity to gather a dataset based on real and local patients; surveying the state of the art regarding applied algorithms and optimization in published datasets, and identifying the relevant data for feeding/training the model; implementing a model for comparison and establishing a reference scenario; applying the model to the mentioned scenario with an evaluation of the impact of possible parameter adjustments on model performance and estimating potential gains. |
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