Academics from various fields at our university have developed artificial intelligence-powered software that can diagnose healthy coughs, in addition to six different illnesses, from cough sounds.
Two biostatisticians, a computer engineer, a pulmonologist, and a gastroenterologist from our university have collaborated to develop software that will enable the diagnosis of respiratory diseases by converting human voices into mathematical data and images. Our department's faculty member, Doç. Dr. Ömer Faruk Akmeşe, is also involved in this comprehensive study.
In over a year of research, cough sounds obtained from 75 volunteers were converted into mathematical data and visual graphics. This data was processed using deep learning methods, and the AI was taught the characteristics of cough sounds resulting from diseases such as COPD, asthma, bronchitis, upper respiratory infections, pneumonia, and reflux.
The system, which can also successfully distinguish cough sounds from healthy individuals, provides diagnoses with an accuracy rate of over 91 percent. This software project has been deemed worthy of support by TÜBİTAK.
The project will be developed with TÜBİTAK support.
Doç. Dr. Emre Demir, Head of the Department of Biostatistics at Hitit University, stated that they have obtained many parameters related to cough sounds through this study and have made them suitable for analysis.
Demir stated that they have taught the obtained data to artificial intelligence, saying, "We can diagnose diseases such as COPD, asthma, bronchitis, upper respiratory tract diseases, pneumonia, and reflux from cough sounds."
Demir noted that they can increase the 91 percent success rate they achieved in the pilot program to over 95 percent with advanced equipment to be procured with TÜBİTAK support. He said, "We will obtain three sounds from each patient. We will obtain at least 100 sounds from each disease group. We plan to obtain over 2,000 sound data points. We will convert each sound into 10 different data points. We will have perhaps 5,000 parameters. This will enable us to obtain much more comprehensive data."
Patients will be able to diagnose themselves with their mobile phones.
Demir, emphasizing that they aim to develop mobile software in the third phase of the project, continued:
"Our ultimate goal with the project is to develop a model using deep learning and artificial intelligence from thousands of audio datasets. We believe this model will successfully diagnose respiratory diseases with a 95% success rate. In the third phase of the project, we want to develop a mobile application that will allow patients to directly diagnose their respiratory system using cough sounds recorded on their mobile phones."
Dr. Büşra Durak, from the Department of Chest Diseases at the Faculty of Medicine, also emphasized that cough is one of the most common reasons for visits to the chest diseases clinic. She said, "Cough provides us with very important information about diseases. There are different types of coughs, such as dry cough, productive cough, and wheezing cough. In patients presenting with a dry cough, we consider conditions such as asthma and reflux. In patients presenting with a productive cough, we consider conditions such as COPD, bronchitis, and pneumonia."
Durak stated that various laboratory and imaging tests are needed to diagnose these diseases, continuing:
"These tests can be exhausting for patients and create a significant burden on the healthcare system. With this project, we aim to diagnose respiratory diseases solely from cough sounds using sound analysis. We believe this will allow us to diagnose patients more quickly, facilitate faster treatment, and prevent significant burden on the healthcare system."
Gastroenterology specialist İbrahim Durak stated that reflux is the third cause of chronic cough, saying, "We normally have to perform serious procedures like upper gastrointestinal endoscopy and pH metering to diagnose reflux. Being able to diagnose cough sounds will be a great convenience for us. It will reduce both the significant financial burden on the healthcare system and patient volume. I've been excited about the project since I first heard about it."
