Unorthodox machine learning solutions for cancer, oral, and infectious diseases

My research creates new paradigms for low-cost biomarker imaging and clinical diagnoses by obviating the need for specialized medical devices and biological processes at the point-of-care. I have demonstrated a novel deep learning and classification approach for obtaining the medical diagnostic information of an organ using photographs captured by mobile phones and cameras. We have successfully demonstrated the utility of this approach in predicting 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 International Conference on Bioinformatics and Bioengineering, and highlighted by Agence France-Presse and at TED.


  1. 2018. Machine learning algorithms for classification of microcirculation images from septic and non-septic patients

    Perikumar Javia, Rana A, Shapiro NI, Shah P*

    (*Senior author supervising research)

    17th IEEE International Conference of Machine Learning and Applications-(Accepted for publication)

  2. 2018. Automated process incorporating machine learning segmentation and correlation of periodontal diseases with systemic health of patients

    Gregory Yauney, Rana A, Javia P, Wong L, Muftu A, Shah P*

    (*Senior author supervising research)

    Journal of Biomedical Health and Informatics-(Under review)

  3. 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

  4. 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