FERNANDO MITSUO SUMIYA
Projetos de Pesquisa
Unidades Organizacionais
LIM/23 - Laboratório de Psicopatologia e Terapêutica Psiquiátrica, Hospital das Clínicas, Faculdade de Medicina
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- Autism Spectrum Disorder diagnosis based on trajectories of eye tracking data(2021) V, Thiago Cardoso; MICHELASSI, Gabriel C.; FRANCO, Felipe O.; SUMIYA, Fernando M.; PORTOLESE, Joana; BRENTANI, Helena; MACHADO-LIMA, Ariane; NUNES, Fatima L. S.The use of Eye Tracking (ET) has been investigated as an auxiliary mechanism to diagnose Autism Spectrum Disorder (ASD). One of the paradigms investigated using ET is Joint Attention (JA), which refers to moments when two individuals are focused on the same object/event so that both are aware that the focus of attention is shared. The computational tools that assist in the diagnosis of ASD have used Image Processing and Machine Learning techniques to process images, videos and ET signals. However, the JA paradigm is still little explored and presents challenges, as it requires analyzing the gaze trajectory and needs innovative approaches. The purpose of this article is to propose a model capable of extracting features from a video used as a stimulus to capture ET signals in order to verify JA and classify individuals as belonging to the ASD or Typical Development (TD) group. The main differential in relation to the approaches in the literature is the definition and implementation of the concept of floating Regions of Interest, which allows monitoring the gaze in relation to an object, considering its semantics, even if the object presents different characteristics throughout the video. A model based on ensembles of Random Forest classifiers was implemented to classify individuals as ASD or TD using the trajectory features extracted from the ET signals. The method reached 0.75 accuracy and 0.82 F1-score, indicating that the proposed approach, based on trajectory and JA, has the potential to be applied to assist in the diagnosis of ASD.
- Computer-aided autism diagnosis using visual attention models and eye-tracking: replication and improvement proposal(2023) FRANCO, Felipe O.; OLIVEIRA, Jessica S.; PORTOLESE, Joana; SUMIYA, Fernando M.; SILVA, Andreia F.; MACHADO-LIMA, Ariane; NUNES, Fatima L. S.; BRENTANI, HelenaBackgroundAutism Spectrum Disorder (ASD) diagnosis can be aided by approaches based on eye-tracking signals. Recently, the feasibility of building Visual Attention Models (VAMs) from features extracted from visual stimuli and their use for classifying cases and controls has been demonstrated using Neural Networks and Support Vector Machines. The present work has three aims: 1) to evaluate whether the trained classifier from the previous study was generalist enough to classify new samples with a new stimulus; 2) to replicate the previously approach to train a new classifier with a new dataset; 3) to evaluate the performance of classifiers obtained by a new classification algorithm (Random Forest) using the previous and the current datasets.MethodsThe previously approach was replicated with a new stimulus and new sample, 44 from the Typical Development group and 33 from the ASD group. After the replication, Random Forest classifier was tested to substitute Neural Networks algorithm.ResultsThe test with the trained classifier reached an AUC of 0.56, suggesting that the trained classifier requires retraining of the VAMs when changing the stimulus. The replication results reached an AUC of 0.71, indicating the potential of generalization of the approach for aiding ASD diagnosis, as long as the stimulus is similar to the originally proposed. The results achieved with Random Forest were superior to those achieved with the original approach, with an average AUC of 0.95 for the previous dataset and 0.74 for the new dataset.ConclusionIn summary, the results of the replication experiment were satisfactory, which suggests the robustness of the approach and the VAM-based approaches feasibility to aid in ASD diagnosis. The proposed method change improved the classification performance. Some limitations are discussed and additional studies are encouraged to test other conditions and scenarios.