This research project aims to establish an intelligent agricultural cultivation support system, by integrating unmanned aerial vehicle (UAV) surveillanceartificial intelligent (AI) analytical techniques.

This research project aims to establish an intelligent agricultural cultivation support system, by integrating unmanned aerial vehicle (UAV) surveillanceartificial intelligent (AI) analytical techniques.

This research project aims to establish an intelligent agricultural cultivation support system, by integrating unmanned aerial vehicle (UAV) surveillanceartificial intelligent (AI) analytical techniques.

This integrated research project follows the Taiwan's 2030 Science & Technology Vision and takes LOHAS community and inclusive technology as the major research direction. We aim to develop trustable AI technologies, and introduce them to future smart services. That will realize the development of human-centric smart technology, and strengthen the governance and application of emerging technologies. The integrated project consists of 7 sub-projects led by PIs from National Taiwan University, National Tsing-Hua Universiy and Academia Sinica and composed of top AI technological teams. These sub-projects are divided into 3 clusters, including machine learning (sub-projects 1 and 2), computer vision (sub-projects 3 and 4), and human-centric computing (sub-projects 5, 6 and 7). We will deal with the issues of bias, fairness, transparency, explainability, traceability, and so on, from the aspects of data collection, technology, and application landing. Each sub-project will implement specific smart services to reflect the benefits and practical applications of the developed technologies. The NTU Joint Research Center for AI Technology and All Vista Healthcare, an AI Innovation Research Center supported by MOST, is responsible for management, planning, and execution of the integrated research project. We will propose a plan that can be generalized and applied to the intelligent service industry.

Intelligent Portable Neurofeedback Training System: Highlights of our product: 1. High accurate EEG value (with rigorous hardware and software design and verification) 2. real-time feedback by computer screen or APP 3. can be used at home (easy to ware, easy to use)

Artificial Intelligence Automatic Sputum Microscopy System AIASMS: AIASMS can automatically shoot images from sputum smear samples and identify Mycobacterium tuberculosis(MTB). AIASMS can find the most suitable depth for shooting images and then automatically shoot images under the microscope through auto-focusing. These images will be stored in the cloud database. Meanwhile, AIASMS detect whether the sputum smear contains MTB. AIASMS can accurately label the location of the TB. The sensitivity and specificity can reach 90% and 99% respectively. We also cooperate with three hospitals in the northern, central, and southern regions of Taiwan to conduct blind testing, and the sensitivity can be more than 90%. AIASMS can also distinguish between MTB and Non-tuberculous mycobacteria (NTM). Finally, n the expansion of technology, we have extended the identification of the acid-resistant staining to the identification of gram staining, so that our system can widely identify a variety of bacteria.

ALOVAS Platform: ALOVAS acts as an A.I. Pathology Platform which provides high resolution pathology image viewer. Even Giga-pixel-level original images can be viewed online in real time. ALOVAS platform can be used not only on computer, but also on iPad. Users can upload images on the platform and select AI models for automated detection, and browse the detection results on the platform. ALOVAS also provides commonly used annotation tools, including hand-drawing, dots, rectangles, etc., which can be used to mark areas of interest. Embedded with the ALOVAS platform also provided several detection algorithms. We hope ALOVAS can assist physicians in rapid diagnosis, in related pathological research, and reduce the workload of pathologists in the future.

ECG STEMI AI Model: In the past, most AI systems gave people the feeling of a black box and couldn't be trusted. The team designed a mechanism that allows doctors to adjust and observe the AI ​​model, so that the AI ​​model can be customized to the functions the doctor wants. We use LINE, the most commonly used communication software for doctors, to design an EKG Line Bot. Medical staff can upload an electrocardiogram to the EKG Line Bot to instantly identify whether the electrocardiogram is Stemi, so as to help doctors determine whether the patient has signs of myocardial infarction. We use this Line Bot to cooperate with doctors and ask them to communicate with the Line Bot. According to the heat map provided by the system, we can check whether it is consistent with the medical concept, and then help us correct the accuracy of our model. The system will train the correct data again.

NTU Medical Genie Precision Health Platform: The product is mainly composed of wearable devices, IoT environmental sensors, deep learning, personal health app and case management platform. It can collect and monitor user's lifestyle and environment automatically, and predict the possibility of emergency to assist medical staff in making decisions. In addition, we opened source the project to solve most clinical studies that require lots of time to build data collection tools and processes.

AI-based Brain Assessment System: Mental illness is a critical health issue in Taiwan. Psychiatric diagnosis is largely based on self-reported or symptomatic criteria without objective findings. This state-of-the-art psychiatric diagnostic platform using standardized brain imaging data, however, improves the efficiency and accuracy of psychiatric diagnosis. Our platform can easily identify the deficiency in brain regions associated with schizophrenia, provides a novel way to evaluate mental illness and its progression, with powerful visualization. This web-based diagnostic platform has international multicenter validation, scientific publication, patent-pending as well as awarded by the 17th National Innovation Award in 2020, etc. This evidence-based, AI-assisted psychiatric diagnosis platform, validated with large-scale standardized brain imaging. We believe this state-of-the-art diagnostic tool can be a new light on modern psychiatric medicine, and promote mental health in the general population.