Development and Evaluation of a Machine Learning Approach to Interpret Optical Coherence Tomography Images of the Middle Ear to Improve Antibiotic Management

Information

  • Research Project
  • 9847606
  • ApplicationId
    9847606
  • Core Project Number
    R43DC017422
  • Full Project Number
    1R43DC017422-01A1
  • Serial Number
    017422
  • FOA Number
    PA-18-574
  • Sub Project Id
  • Project Start Date
    8/1/2019 - 5 years ago
  • Project End Date
    1/31/2020 - 5 years ago
  • Program Officer Name
    MILLER, ROGER
  • Budget Start Date
    8/1/2019 - 5 years ago
  • Budget End Date
    1/31/2020 - 5 years ago
  • Fiscal Year
    2019
  • Support Year
    01
  • Suffix
    A1
  • Award Notice Date
    7/29/2019 - 5 years ago
Organizations

Development and Evaluation of a Machine Learning Approach to Interpret Optical Coherence Tomography Images of the Middle Ear to Improve Antibiotic Management

PROJECT SUMMARY Introduction: PhotoniCare, Inc. is a medical device company developing the TOMi Scope, a handheld, optical imaging device for improved diagnosis of middle ear health. The purpose of this proposal is to establish and evaluate a machine learning approach to interpret TOMi Scope depth-resolved images using a set of ear models with human middle ear effusion (MEE; fluid) to enable improved diagnostic accuracy and, ultimately, antibiotic stewardship for ear health. Significance: Ear infections affect 95% of all children, yet they are one of the most poorly diagnosed and managed diseases in all of medicine, resulting in high antibiotic over-prescription and antibiotic resistance development. Correctly identifying the absence or presence/type of MEE through the non-transparent eardrum is critical to accurate diagnosis, and the limited current diagnostic tools suffer poor diagnostic accuracy (50- 70%) due to inherent subjectivity and dependence on user experience. Therefore, objective image classification metrics to enable improved diagnostic accuracy is sorely needed to finally provide children afflicted by this disease with the correct treatment the first time. Hypothesis: Applying a machine learning approach to TOMi Scope image classification of a set of ear models with human MEE will facilitate detection of the presence or absence of effusion (?90% accuracy), as well as classification by the type of effusion samples (?80% accuracy), regardless of user experience. Specific Aims: (1) Collect robust datasets of ex vivo human MEE, sufficient for machine learning image analysis. (2) Develop a neural network model based on the MEE dataset and apply the model to a representative test clinical dataset to determine classification feasibility. Commercial Opportunity: The TOMi Scope will provide physicians with new, objective information, enabling better decision-making for antibiotic prescription and surgical intervention. This has the potential to impact the standard of care for ~1B children worldwide that experience ear infections, representing a multi-billion-dollar commercial opportunity.

IC Name
NATIONAL INSTITUTE ON DEAFNESS AND OTHER COMMUNICATION DISORDERS
  • Activity
    R43
  • Administering IC
    DC
  • Application Type
    1
  • Direct Cost Amount
  • Indirect Cost Amount
  • Total Cost
    223899
  • Sub Project Total Cost
  • ARRA Funded
    False
  • CFDA Code
    173
  • Ed Inst. Type
  • Funding ICs
    NIDCD:223899\
  • Funding Mechanism
    SBIR-STTR RPGs
  • Study Section
    ZRG1
  • Study Section Name
    Special Emphasis Panel
  • Organization Name
    PHOTONICARE, INC.
  • Organization Department
  • Organization DUNS
    078873691
  • Organization City
    CHAMPAIGN
  • Organization State
    IL
  • Organization Country
    UNITED STATES
  • Organization Zip Code
    618207460
  • Organization District
    UNITED STATES