Low-cost and convenient screening of disease using analysis of physical measurements and recordings

Abstract

In recent years, there has been substantial work in low-cost medical diagnostics based on the physical manifestations of disease. This is due to advancements in data analysis techniques and classification algorithms and the increased availability of computing power through smart devices. Smartphones and their ability to interface with simple sensors such as inertial measurement units (IMUs), microphones, piezoelectric sensors, etc., or with convenient attachments such as lenses have revolutionized the ability collect medically relevant data easily. Even if the data has relatively low resolution or signal to noise ratio, newer algorithms have made it possible to identify disease with this data. Many low-cost diagnostic tools have been created in medical fields spanning from neurology to dermatology to obstetrics. These tools are particularly useful in low-resource areas where access to expensive diagnostic equipment may not be possible. The ultimate goal would be the creation of a "diagnostic toolkit" consisting of a smartphone and a set of sensors and attachments that can be used to screen for a wide set of diseases in a community healthcare setting. However, there are a few concerns that still need to be overcome in low-cost diagnostics: lack of incentives to bring these devices to market, algorithmic bias, "black box" nature of the algorithms, and data storage/transfer concerns.

Fig 1. Data processing.

This figure shows how the data is first collected either directly from the patient or from a healthcare worker. Features are extracted from the data to reduce the dimensionality for input into a machine learning model. The raw signal can also be directly inputted into a neural network for analysis. Lastly, the model provides a diagnosis, risk score, or disease severity that can be acted upon. Figure created with biorender.com.

Fig 2. The Diagnostic Toolkit.

This is an example of the possible components of a diagnostics toolkit. The centerpiece is a smartphone that can take pictures and receive data from the other sensors. Lens attachments can be used to visualize the eye and the skin better. A microphone can pick up breathing sounds and bowel sounds. IMUs or a smartwatch can monitor patient movements. A skin patch can communicate electrochemical information about sweat and hormone levels. Lastly, a stylus will be useful in collecting fine motor information based on patient drawings. Figure created with biorender.com.