King Mongkut’s Institute of Technology Ladkrabang 1 article published in JoVE Bioengineering Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model Veerayuth Kittichai1, Morakot Kaewthamasorn2, Suchansa Thanee2, Thanyathep Sasisaowapak3, Kaung Myat Naing3, Rangsan Jomtarak4, Teerawat Tongloy3, Santhad Chuwongin3, Siridech Boonsang5 1Faculty of Medicine, King Mongkut’s Institute of Technology Ladkrabang, 2Veterinary Parasitology Research Unit, Faculty of Veterinary Science, Chulalongkorn University, 3College of Advanced Manufacturing Innovation, King Mongkut’s Institute of Technology Ladkrabang, 4Faculty of Science and Technology, Suan Dusit University, 5Department of Electrical Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang Worldwide medical blood parasites were automatically screened using simple steps on a low-code AI platform. The prospective diagnosis of blood films was improved by using an object detection and classification method in a hybrid deep learning model. The collaboration of active monitoring and well-trained models helps to identify hotspots of trypanosome transmission.
