The dialogue system is the main subsystem of the visually impaired navigation system, which provides destinations for the navigation system through multiple dialogues. We use the knowledge graph as the basis for reasoning. In terms of close-range navigation, deep learning technology is used to develop RGB camera detection depth algorithm, indoor semantic cutting algorithm, integrated detection depth estimation and indoor semantic cutting in indoor obstacle avoidance, etc. The whole system uses the CellS software design framework to integrate distributed AIoT systems.

The IHC staining intensity grading algorithm combines a series of complementary processes of deep learning and image processing in order to overcome the difficulties in cell boundary segmentation and grade definition, and provides a cell-based IHC staining intensity grading and quantification capability. The proposed method serves as a useful assistive tool for physicians in performing accurate staining quantification in tissue microscopic images.

div style="text-align: justify"According to the statistics of World Health Organization (WHO) in 2018, Chronic Obstruction Pulmonary Disease (COPD) has killed 3 million peoplebecome the 3rd place among the top 10 leading causes of death in the world, which means one person dies for this fatal disease every 10 seconds. In Taiwan,chronic lower respiratory disease is at the 7th place among the top 10 leading cause of death. There are over 5000 people died for obstructive pulmonary every year.

Use NLP techniques to realize the automatic coding of ICD10. According to the input of the patient’s age, gender, medical order, admissions, progress note, surgical records, discharge, ICD-10 diagnostic code and ICD-10 disposal code, perform machine learning model training and code prediction.

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)

The present invention can be applied to many wearable devices. Its technologies include wireless signal transmission control systems, airbag pneumatic methods, and airbag analogue variants. Its various system controls are designed to be modularized so that users can repair and replace them.

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.

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.

As artificial intelligence rapidly develops and enhances human’s quality of life, another cause for concern is the dangers and possible threats to humanity. “Moral judgement” will be a big issue that needs to be addressed as these intelligent machines become more and more prevalent in society. As with any new technologies, the topic of applied ethics often arises. So, the extension of that is AI ethics issues.

Similar to the critical point tracking technology of human vision, the split attention and spatial grouping enhancement module, combining the multi-path grouping architecture and spatial attention technology, can accurately extract important information from the image and improve the network performance. Moreover, adopting neural architecture search technology to automatically search for the most suitable network architecture based on current modules and hardware devices can balance diagnosis speed and high accuracy.

The core concept of the decision support system is to establish machine learning-based predictive models and enable their interpretability to support physicians making clinical decisions in practice.

Assisted by in-house AI deep compression toolchain (ezLabel, ezModel, ezQUANT, ezHybrid-M), the proposed technology supports automatic AI model design and optimization with the integrated performance of 120x model size reduction and 70x power reduction in 2D CNN model, and develops a world-first 1/2/4/8-bit CNN model realized by the developed high efficiency Hybrid fixed point CNN NPU (Hybrid-NPU), which has been verified in Xilinx ZCU102 FPGA and achieves the performance up to 2.5 TOPS(8-b)/ 20TOPS(1-b)＠28nm technology running at 550MHz and 4TOPS/W energy efficiency.

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.

In response to the global COVID-19 pandemic, Prof. Jinn-Moon Yang, the director of the Department of Biological Science & Technology at the National Yang Ming Chiao Tung University (NYCU),also leading an AI project as a principal investigator within MOST Joint Research Center for AI TechnologyAll Vista Healthcare (AINTU), created an intercollegiateinterdisciplinary smart dynamic system-based biologicalmedicine development team.

Infertility is a serious globe issue, especially in the developed countries, over 20infertile male has sperm problems, that include instance immotile, abnormal morphologylow sperm quantity. Therefore, how to isolate high quality sperm for clinical use is extremely required. Up to date, activity sperm sorting is usually using Swiming-upDensity gradient centrifugation approaches. Unfortunately, both methods are detrimental to the sperm viabilityelicits production of reactive oxygen species. Separation sperm by microfluidics has been put emphasis onstudied however, it faced some challenge such as low throughputan unacceptable recover rate.

Taiwan is rapidly entering the era of "aging"simultaneously having "less children". Last year, it officially became a "senior society." How do we cope with the constant aging of those around us? What medicalcare needs will be faced? How will the younger generation juggle long-term care for the aging, along with lifework?

“MeDA OXR: Real-time Hazard Recognition System,” which can automatically screen and interpret the medical images in real-time, is designed for emergency rooms to assist physicians in diagnosis, reducing medical risks, and improving overall efficiency. Combining portable X-Ray and AI algorithms, the system performs real-time and accurate preliminary screening and diagnoses of diseases, such as pneumothorax, pneumonia, and tuberculosis. It can also locate the nasogastric tube, endotracheal tube, and central venous catheter, while misplacement of that is sent to the physicians when detected.

PCA-BM includes two models: “Automatic BMs Segmentation AI Model” and " Distant Brain Failure Prediction Model". The former one uses C2FNAS (coarse to fine network architecture search) to detect the location, size, and the number of brain metastases. The latter uses radiomics to extract numerous radiographic features and employs machine learning methods such as XGBoost to establish a prognostic model of brain metastases. PCA-BM provides more precision treatment decisions for patients and improves personalized and accurate overall stereotactic radiosurgery planning.

We aim to build EEG databasesapply artificial intelligence (AI) to clinical, healthcare,educational applications.

The RL model develops a control policy directly from experience to predict states and rewards during a learning procedure. Hence, we designed a medical image environment including US images, different actions, and rewards, agent learns in this environment to extract the ALN region and evaluates the status. The performance of our proposed method achieves an accuracy of 83.6％, a sensitivity of 88.6％, and a specificity of 89.0％.

The primary goal of this project is to establish a complete hospital-based liver cancer database, profiles for data feature extraction, and develop different cancer, prediction models.

By collecting the changes of kinds of vital signs(including heart beat rate, blood pressure, steps, and etc.), we combined the results with the testers’ fatigue scores which belonged to the matched fatigue types and their basic profiles. We extracted 780 types of structural features sets by cleaning raw data, then separating into training set, validation set and testing set. With machine learning algorithm to auto-optimize regression analysis, finally we come out the correlative features between vital signs and fatigue, and the overwork model as our deliverables.

e propose an AI dynamics drug repurposing platform applicate on COVID-19. This platform analyzed a large number of structures of SARS-CoV, MERS, and cross-species coronavirus 3CL protease-ligand complexes to construct uncovering six flexible active site conformations and pharmacophore clusters for SARS-CoV-2 3CL protease screening all FDA drugs and found four inhibitors within three months. Among them, JM206 had even demonstrated ten times better efficacy than Remdesivir in-vitro assay and also show the effect on the in-vivo hamster model to alleviate the symptoms caused by COVID-19.

PANCREASaver contains a “Pancreas Cancer (PC) automatic segmentation model” (image segmentation) and a “Pancreas Cancer (PC) analysis AI model” (image classification) that can read the DICOM format of postcontrast CT images directly for the automatic analysis process. After conducting prep-processing with image processing algorithms, C2FNAS is employed to illustrate the tumor position prior to the diagnosis conducted by CNN. The results can be provided to the physician for diagnostic reference so as to reduce early omissions and increase the detection rate of pancreatic cancer.

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.

1.Real-time monitoring of air quality in indooroutdoor environments: Sensors based on laser light scatteringelectrochemistry are used for monitoring PM2.5other air pollutants in indooroutdoor environments. The collected data from the sensors are transferred to the data center at NTU PHRC by 4G LTE. The residents can obtain the real-time air quality information through the website.2. Precision Prevention Application: an APP of CVD Risk Prediction:We developed an application for predicting CVD risk based on a Bayesian predicting model with personalized factors. With the precision risk of CVD, the health care workers can provide personalized suggestion for patients.

Infertility is a serious globe issue, especially in the developed countries, over 20infertile male has sperm problems, that include instance immotile, abnormal morphologylow sperm quantity. Therefore, how to isolate high quality sperm for clinical use is extremely required. Up to date, activity sperm sorting is usually using Swiming-upDensity gradient centrifugation approaches. Unfortunately, both methods are detrimental to the sperm viabilityelicits production of reactive oxygen species. Separation sperm by microfluidics has been put emphasis onstudied however, it faced some challenge such as low throughputan unacceptable recover rate.

With the development of photonics, there has been a push towards optoelectronic devices that are flexible, rollable, wearable, user-friendly,robust to improve human-machine interfaces. To be integrated onto human body, these devices must be biocompatiblebe able to withstand mechanical deformationdifferent bending curvatures. Figure 1 shows the rollablesoft photonic meta-device.

We present pen-probe analyzers based on ion-mobility spectrometry and mass spectrometry for in-situ detection of volatile organic compounds (VOCs) or mapping of VOCs emanating from surfaces. User approaches probe to the analyzed object, and presses a button on the probe. The collected data are uploaded to the Internet cloud for retrieval and processing. Every data file contains the exact time and geographical co-ordinates of sample collection site. In another embodiment, the pen-shaped probe has been robotized to enable open-space sampling and mapping of VOCs emanating from solid specimens.