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

## TL;DR

This paper discusses how smartphones and low-cost sensors can be used to screen for diseases in low-resource areas, though challenges remain in bringing these tools to market.

## Contribution

The paper highlights the potential of low-cost, smartphone-based diagnostic tools and identifies key challenges for their adoption.

## Key findings

- Smartphones with sensors can collect medically relevant data for disease screening.
- Low-cost tools are useful in areas with limited access to expensive diagnostic equipment.
- Challenges include algorithmic bias and data storage/transfer issues.

## 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.