Interpreting Audiograms with Multi-stage Neural Networks
This addresses a domain-specific problem for audiologists by enabling automated interpretation of audiograms, which is incremental as it builds on prior detection and classification efforts.
The paper tackles the problem of extracting hearing level data from audiogram images, which existing models cannot do, and proposes a Multi-stage Audiogram Interpretation Network (MAIN) that directly reads this data from photos, showing it is feasible and reliable.
Audiograms are a particular type of line charts representing individuals' hearing level at various frequencies. They are used by audiologists to diagnose hearing loss, and further select and tune appropriate hearing aids for customers. There have been several projects such as Autoaudio that aim to accelerate this process through means of machine learning. But all existing models at their best can only detect audiograms in images and classify them into general categories. They are unable to extract hearing level information from detected audiograms by interpreting the marks, axis, and lines. To address this issue, we propose a Multi-stage Audiogram Interpretation Network (MAIN) that directly reads hearing level data from photos of audiograms. We also established Open Audiogram, an open dataset of audiogram images with annotations of marks and axes on which we trained and evaluated our proposed model. Experiments show that our model is feasible and reliable.