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    Predicting Particulate Matter Values in Metropolitan Areas Using Machine Learning
    (IEEE, 2024-12-18)
    Andrés Leiva-Araos
    ;
    Tushya Vemuri
    ;
    David Mena
    ;
    Xudong Liu
      3
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    Interpretable multimodal classification for age-related macular degeneration diagnosis
    (2024)
    Carla Vairetti
    ;
    Sebastián Maldonado
    ;
    Loreto Cuitino
    ;
    URZUA SALINAS, CRISTHIAN  
    ;
    Xu Yanwu
    <jats:p>Explainable Artificial Intelligence (XAI) is an emerging machine learning field that has been successful in medical image analysis. Interpretable approaches are able to “unbox” the black-box decisions made by AI systems, aiding medical doctors to justify their diagnostics better. In this paper, we analyze the performance of three different XAI strategies for medical image analysis in ophthalmology. We consider a multimodal deep learning model that combines optical coherence tomography (OCT) and infrared reflectance (IR) imaging for the diagnosis of age-related macular degeneration (AMD). The classification model is able to achieve an accuracy of 0.94, performing better than other unimodal alternatives. We analyze the XAI methods in terms of their ability to identify retinal damage and ease of interpretation, concluding that grad-CAM and guided grad-CAM can be combined to have both a coarse visual justification and a fine-grained analysis of the retinal layers. We provide important insights and recommendations for practitioners on how to design automated and explainable screening tests based on the combination of two image sources.</jats:p>
    Scopus© Citations 7
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    ISOM 2023 research Panel 4 - Diagnostics and microbiology of otitis media
    (2023)
    Sharon Ovnat Tamir
    ;
    Seweryn Bialasiewicz
    ;
    Christopher G. Brennan-Jones
    ;
    DER MUSSA, CAROLINA FATHME  
    ;
    Liron Kariv
    Scopus© Citations 10  6
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    Computer-Aided Ear Diagnosis System Based on CNN-LSTM Hybrid Learning Framework for Video Otoscopy Examination
    (2021)
    Michelle Viscaino
    ;
    Juan C. Maass
    ;
    Paul H. Delano
    ;
    Fernando Auat Cheein
    Scopus© Citations 15  3
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    Color Dependence Analysis in a CNN-Based Computer-Aided Diagnosis System for Middle and External Ear Diseases
    (2022)
    Michelle Viscaino
    ;
    Matias Talamilla
    ;
    Juan Cristóbal Maass
    ;
    Pablo Henríquez
    ;
    Paul H. Délano
    <jats:p>Artificial intelligence-assisted otologic diagnosis has been of growing interest in the scientific community, where middle and external ear disorders are the most frequent diseases in daily ENT practice. There are some efforts focused on reducing medical errors and enhancing physician capabilities using conventional artificial vision systems. However, approaches with multispectral analysis have not yet been addressed. Tissues of the tympanic membrane possess optical properties that define their characteristics in specific light spectra. This work explores color wavelengths dependence in a model that classifies four middle and external ear conditions: normal, chronic otitis media, otitis media with effusion, and earwax plug. The model is constructed under a computer-aided diagnosis system that uses a convolutional neural network architecture. We trained several models using different single-channel images by taking each color wavelength separately. The results showed that a single green channel model achieves the best overall performance in terms of accuracy (92%), sensitivity (85%), specificity (95%), precision (86%), and F1-score (85%). Our findings can be a suitable alternative for artificial intelligence diagnosis systems compared to the 50% of overall misdiagnosis of a non-specialist physician.</jats:p>
    Scopus© Citations 9  18