Interdisciplinary studies of obstetrics and IEEE in MeMeA 2024

The 19th edition of the IEEE International Symposium on Medical Measurements and Applications (IEEE MeMeA 2024) was held from 26 Jun to 29 Jun in Eindhoven, the Netherlands.

IEEE MeMeA 2024 is an international academic conference organized by the IEEE Institute of Instrumentation and Measurement. IEEE (Institute of Electrical and Electronic Engineers) is the largest professional technical society in the world, committed to promoting technological innovation and outstanding development for the benefit of humanity. And IEEE Institute of Instrumentation and Measurement is one society of IEEE, which was founded 75 years ago. With the updating of medical measurement and examination methods, the widespread application of computer processing systems for biological signals and medical data, and the rapid development of artificial intelligence, the IEEE Institute of Instrumentation and Measurement has held the IEEE Annual Conference on Medical Measurement and Applications since 2006 and it was the 19th edition.

This symposium focuses on all aspects of interactions relating to instrumentation and measurement, bio-engineering, material science, chemical and biological measurements, and leveraging Signal/Data Processing and Artificial Intelligence for accelerating solutions in the medical field. The symposium enables researchers, healthcare professionals, technicians, and engineers to exchange ideas and information and make connections and collaborations towards advancing innovation on health care systems and diagnostics in medicine.

Dr. Qiong Luo was invited to co-chair the session "Computational biology inspired physiological signal processing and clinical application", and gave a talk on "The Development of Clinical Application of Electrophysiological Signal". She also introduced our hospital to the symposium, and improved the international influence of our hospital.

Dr. Yan Feng gave an oral presentation on “Predicting the Success of Oxytocin-Induced Labor Using TOCO Signals with Machine-Learning Modeling”. Traditional TOCO signals before induction of labor seems nonsense to us, but after features extraction and machine-learning modeling, the predictive sensitivity could be 0.8309, the specificity could be 0.8115 and the accuracy could be 0.8299. This study gives us new insights on mining valuable information on traditional clinical data via computational biology inspired physiological signal processing.

After the session, they also listened to the reports of various experts especially “Advances in pregnancy and neonatal monitoring”, “Recent advances in sensor systems” and “Challenges with novel wearable sensor technologies”, which are closely related to OB/Gyn. They even discussed future international collaborations on clinical trials.