# Clinical Development and Evaluation of a Deep Learning Approach to Improve Diagnostic Accuracy

> **NIH NIH R44** · PHOTONICARE, INC. · 2020 · $1,136,886

## Abstract

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 facilitate both: (1) ease and reliability of quality data capture in a
pediatric population from users with a range of otscopy expertise, and; (2) assist interpretation of the TOMi
Scope’s correlated otoscopy and depth-resolved images in order to enable improved diagnostic accuracy and,
ultimately, effective management.
Significance: Ear infections affect 93% of all children, yet they are one of the most poorly diagnosed (~50%
accuracy) and managed diseases in all of medicine, resulting in high antimicrobial over-prescription and
resistance development. Correctly identifying the absence or presence/type of middle ear effusion (MEE; fluid)
through the non-transparent eardrum is critical to accurate diagnosis, and the limited current diagnostic tools
suffer poor diagnostic adoption (7-38% reported use) and accuracy (50-70%) due to inherent subjectivity and
dependence on user expertise. Therefore, there is a clear and unmet need for superior, objective screening,
starting with a definitive yet easily and reliably usable diagnostic tool for this extremely prevalent yet poorly
managed disease.
Hypothesis: Applying a machine learning approach to TOMi Scope imaging guidance and diagnostic
classification will facilitate both: 1) ease-of-use and reliable quality data collection improvement, and 2)
accurate detection of the presence or absence of MEE, as well as classification of the type of infection,
regardless of user experience.
Specific Aims: (1) Collect labeled TOMi Scope data (otoscopy and depth-scan images) from 268 patients at
pediatric offices affiliated with UPMC Children’s Hospital of Pittsburgh, (2) Achieve reliable usability of the TOMi
Scope by guiding image capture using TOMi-net, a deep learning model, (3) Develop a multimodal deep learning
model to provide diagnostic assistance using TOMi Scope otoscopy and depth-scan data.
Commercial Opportunity: The TOMi Scope will provide physicians with a superior user experience and 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.

## Key facts

- **NIH application ID:** 10156035
- **Project number:** 2R44DC017422-02
- **Recipient organization:** PHOTONICARE, INC.
- **Principal Investigator:** Ryan L Shelton
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $1,136,886
- **Award type:** 2
- **Project period:** 2019-08-01 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10156035

## Citation

> US National Institutes of Health, RePORTER application 10156035, Clinical Development and Evaluation of a Deep Learning Approach to Improve Diagnostic Accuracy (2R44DC017422-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10156035. Licensed CC0.

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