New paradigms for unorthodox artificial intelligence and machine learning solutions for cancer, oral and infectious diseases

My research creates new paradigms for low-cost biomarker imaging and clinical diagnoses by obviating need for specialized medical devices and biological processes at the point-of-care. I have demonstrated a novel deep learning and classification approach to obtain medical diagnostic information of an organ using photographs captured by mobile phones and cameras. We have successfully demonstrated utility of this approach to predict fluorescent porphyrin biomarkers (associated with tumors and periodontal diseases) from standard white-light photographs of the mouth vs. fluorescent images. This work was selected for an Oral presentation at the 2017 IEEE Bioengineering and Bioinformatics conference, highlighted by Agence France-Presse and at TED.


  1. 2017. Convolutional neural network for combined classification of fluorescent biomarkers and expert annotations using white light images [Abstract] [Full Paper]

    Gregory Yauney, Angelino K, Edlund D, Shah P*

    (*Senior author supervising research, Selected for oral presentation)

    17th annual IEEE International Conference on BioInformatics and BioEngineering

  2. 2017. Automated segmentation of gingival diseases from oral images [Abstract] [Full Paper]

    Aman Rana, Yauney G, Wong L, Muftu A, Shah P*

    (*Senior author supervising research)

    IEEE-NIH 2017 Special Topics Conference on Healthcare Innovations and Point-of-Care Technologies

Other MIT Research Areas