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As part of its efforts to promote innovation in the healthcare sector, Qatar University (QU) has developed AI technologies to improve healthcare.
According to an official statement from QU, a team of students from the Department of Electrical Engineering at the College of Engineering, including Hamad al-Yafei, Mohammed Nour and Abdulla al-Obaidan, under the supervision of Dr Muhammad Salman Khan, associate professor at QU, successfully designed and developed advanced AI models for classifying adventitious lung sounds, contributing to more accurate diagnosis of respiratory diseases.
Lung sounds, also referred to as respiratory or breath sounds, are fundamental indicators for diagnosing respiratory diseases. Traditional auscultation using a stethoscope requires significant skill and experience to accurately identify abnormalities. However, human errors or time constraints may lead to delayed or inaccurate diagnoses. This is where AI plays a transformative role, enabling the analysis of lung sounds through machine learning and deep learning algorithms to provide rapid and precise diagnoses of abnormalities.
The project began with a comprehensive review of scientific literature to identify key challenges and solutions in the field. The students utilised an internationally recognised dataset, widely recognised by the research community, which contained both normal and abnormal lung sounds. They processed and analysed this dataset using advanced signal processing and acoustic feature extraction techniques. The outcome was AI models trained to classify lung sounds with high accuracy, paving the way for wide applications in healthcare systems.
The project featured a unique collaboration between the College of Engineering and the College of Medicine, co-mentored by Dr Maha Desouki, section head of Pre-Clinical Education.
Through this initiative, the engineering students had the opportunity to visit the clinical skills lab at the College of Medicine, where they trained on using medical manikins to record and analyse both normal and abnormal lung sounds. This initiative aligns with QU's vision to support the nation’s knowledge-based economy and strengthen its research capabilities in vital fields.
“This collaboration between students and researchers represents a significant step toward enhancing healthcare outcomes and advancing medical technologies. The project reflects QU's commitment to excellence in research and innovation and its role in fostering cross-sector collaboration to achieve tangible outcomes that improve the lives of individuals and communities,” the statement said.
© Gulf Times Newspaper 2022 Provided by SyndiGate Media Inc. (Syndigate.info).According to an official statement from QU, a team of students from the Department of Electrical Engineering at the College of Engineering, including Hamad al-Yafei, Mohammed Nour and Abdulla al-Obaidan, under the supervision of Dr Muhammad Salman Khan, associate professor at QU, successfully designed and developed advanced AI models for classifying adventitious lung sounds, contributing to more accurate diagnosis of respiratory diseases.
Lung sounds, also referred to as respiratory or breath sounds, are fundamental indicators for diagnosing respiratory diseases. Traditional auscultation using a stethoscope requires significant skill and experience to accurately identify abnormalities. However, human errors or time constraints may lead to delayed or inaccurate diagnoses. This is where AI plays a transformative role, enabling the analysis of lung sounds through machine learning and deep learning algorithms to provide rapid and precise diagnoses of abnormalities.
The project began with a comprehensive review of scientific literature to identify key challenges and solutions in the field. The students utilised an internationally recognised dataset, widely recognised by the research community, which contained both normal and abnormal lung sounds. They processed and analysed this dataset using advanced signal processing and acoustic feature extraction techniques. The outcome was AI models trained to classify lung sounds with high accuracy, paving the way for wide applications in healthcare systems.
The project featured a unique collaboration between the College of Engineering and the College of Medicine, co-mentored by Dr Maha Desouki, section head of Pre-Clinical Education.
Through this initiative, the engineering students had the opportunity to visit the clinical skills lab at the College of Medicine, where they trained on using medical manikins to record and analyse both normal and abnormal lung sounds. This initiative aligns with QU's vision to support the nation’s knowledge-based economy and strengthen its research capabilities in vital fields.
“This collaboration between students and researchers represents a significant step toward enhancing healthcare outcomes and advancing medical technologies. The project reflects QU's commitment to excellence in research and innovation and its role in fostering cross-sector collaboration to achieve tangible outcomes that improve the lives of individuals and communities,” the statement said.
