0000000001079174
AUTHOR
Gregory Shuster
Overview on SNIFFPHONE:A portable device for disease diagnosis
We present SNIFFPHONE, a handy and easy-To-use device that allows the non-invasive detection of gastric diseases. It analyzes the user's exhaled breath using specifically developed gas sensors. The device is coupled to a smartphone, which governs the breath analysis process, sends the data measurements to an external data analysis server, and finally gives feedback to the user. In this work, we describe the SNIFFPHONE device and the general platform under development.
Non-contact breath sampling for sensor-based breath analysis
Breath analysis holds great promise for real-time and non-invasive medical diagnosis. Thus, there is a considerable need for simple-in-use and portable analyzers for rapid detection of breath indicators for different diseases in their early stages. Sensor technology meets all of these demands. However, miniaturized breath analyzers require adequate breath sampling methods. In this context, we propose non-contact sampling; namely the collection of breath samples by exhalation from a distance into a miniaturized collector without bringing the mouth into direct contact with the analyzing device. To evaluate this approach different breathing maneuvers have been tested in a real-time regime on a…
Sensing gastric cancer via point‐of‐care sensor breath analyzer
Background Detection of disease by means of volatile organic compounds from breath samples using sensors is an attractive approach to fast, noninvasive and inexpensive diagnostics. However, these techniques are still limited to applications within the laboratory settings. Here, we report on the development and use of a fast, portable, and IoT-connected point-of-care device (so-called, SniffPhone) to detect and classify gastric cancer to potentially provide new qualitative solutions for cancer screening. Methods A validation study of patients with gastric cancer, patients with high-risk precancerous gastric lesions, and controls was conducted with 2 SniffPhone devices. Linear discriminant an…