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Session: Multi-Disciplinary General ePoster Viewing [Return to Session]

An IOS Surface Audiovisual Biofeedback Smartphone Application for Respiratory Monitoring in Radiation Oncology

T Nano1, E Hirata1, E Blomain2, L Skinner2, N Prionas1, B Loo2, D Capaldi2*, (1) University of California, San Francisco, San Francisco, CA, (2) Stanford University, Stanford, CA


PO-GePV-M-156 (Sunday, 7/25/2021)   [Eastern Time (GMT-4)]

Purpose: Radiation dose delivered to a target located near the upper abdomen or thorax is significantly affected by respiratory motion, necessitating large margins and limiting dose escalation. Currently, normal tissue sparing can be improved by using motion monitoring systems. The amalgamation of these systems with audiovisual biofeedback can further improve the quality and delivery of radiation as the patient is able to see their breathing pattern and correct for variations leading to consistent and more predictable target motion. Alternative to current solutions, we have developed and evaluated the feasibility of an audiovisual feedback system that leverages depth information from the depth sensor onboard iOS devices to provide visual coaching to improve patient compliance and reduce treatment delivery time.

Methods: The iOS application, coined iOS Surface Audiovisual Biofeedback (iSAVB), was developed in Swift (Apple Inc.) and implemented on an iPhone® with the TrueDepth camera system. Validation was performed using the QUASAR™ Respiratory Motion Phantom (Modus Medical Devices); previously recorded motion traces during free-breathing and breath-hold treatments were programmed into the motion platform and played while recording depth information using iSAVB. iSAVB measurement of displacement was compared to the input signal trace using linear regressions and Bland-Altman analysis.

Results: Free-breathing and breath-hold traces show excellent agreement between the input signal trace from the motion phantom and iSAVB. iSAVB and QUASAR™ motion phantom traces were significantly related (free-breathing: r=0.999, p<0.0001; bias=0.00±0.04cm, 95%-confidence-interval=−0.07cm,0.07cm; breath-hold: r=0.991, p<0.0001; bias=0.00±0.08cm, 95%-confidence-interval=−0.16cm,0.16cm).

Conclusion: Feasibility of an iOS application to provide depth information for real-time respiratory motion monitoring is demonstrated. With the ubiquity of smartphone devices, this work has the potential to provide audiovisual biofeedback to patients in a low-cost platform that will improve the efficacy of radiation therapy.



    Surface Matching, Respiration, Gating


    TH- External Beam- Photons: Motion management - intrafraction

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