2020Exhibitor List
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Advantech provide the iward solution and telehealth solution. The intelligent ward allow faster communication of patient's needs to the nursing staff and facilitates an innovative nursing care SOP that improves serve quality as well as patient satisfaction through the system.


Our team members include: Center Director Hsin-Hsi Chen, Co-Director Li-Chen Fu, CEO Edward Duh, Associate Executive Officer Po-Yuan Tseng, and project principal investigators and co-principal investigators. The center has more than 130 principal and co-principal investigators, as well as more than 800 graduate students handling the projects.

The steering committee includes: Jhing-Fa Wang, President of Tajen University ; Ann-Shyn Chiang, Academician of Academia Sinica / Dean of the College of Life Sciences of National Tsing Hua University ; Yi-Chin Du, founder of Taiwan AI Labs; Chin-Yew Lin, Principal Investigator of Microsoft Research Asia ; Frank Hung, General Manager of Taiwan Shin Kong Security Co, Ltd. ; Min-Huei Hsu, Technical Supervisor of the Ministry of Health and Welfare of the Republic of China ; San-Cheng Chang, Chairman of the Taiwan Mobile Foundation ; Chih-Han Yu, co-founder of Appier Interactive Technology ; Guan-Tarn Huang, General Counsel of China Medical University ; Jong-Tsun Huang, Professor of the Biomedical Research Institute, College of Medicine, China Medical University ; and Lee-Feng Chien, Google Chairman and General Manager.

AIntu - AHEAD Medicine

AHEAD Medicine develops AI-based diagnostic and clinical decision tools for blood cancer management. We gathered expertise from hematology, data science, and machine learning for developing tailor-made solutions for the blood cancer diagnosis. We have completed the proof of concept studies and presented them in multiple congresses and peer-review journals. We are asking 2M USD to complete our FDA pre-submission meetings and kick of external validation studies.
Cooperation projects and needs
1. Co-development: oncology drug response prediction model using longitudinal data
2. Co-development: cell therapy response prediction model using longitudinal data
3. Strategic partnership: develop new analysis module for new instrument models

AIntu- Deep-learning-algorithm assisted cellular-resolution tomography

1. Cellular-resolution optical coherence tomography (OCT)
High-speed optical tissue tomography with cellular resolution is promising to replace the excision biopsy for cancer diagnosis. We have developed a near-isotropic micron-resolution OCT system to image human skin and cancer cell for both morphological recognition as well as parametric analysis, which not only provide in vivo cellular-resolution image but also delineate subcellular structure (i.e., the nucleus). Technically transferred from NTU in 2014, a biomedical startup, Apollo Medical Optics (AMO), has developed a GMP-certificated prototype, and has clinical collaborations with major medical centers worldwide.
2. Deep-learning-algorithm assisted cancer diagnosis
With the fast advancement of OCT toward cellular resolution, very few physicians can comprehend the grey-level OCT images because its lacking of specificity. Therefore, there is an unmet need to assist physicians in reading the OCT image features. We present an approach based on the introduction of biomedical knowledge to build an image conversion model with the aim of converting in vivo human skin tomographic images to H&E stained-liked images.
Cooperation projects and needs
1. Drug development preclinical test: 3D cell culture inspection, drug toxicity test using 3D organelle model


In an Aging society, it is common for people and their parents do not live together. However, if their parents have an accident at home, they often cannot provide assistance upon knowing the accident immediately. Therefore, we develop CarePLUS. The CarePLUS encrypts the personal information and uploads image to the cloud through the network. After analyzed by the AI ​​model on the cloud, the AI model returns the result to the device and interacts with the user.

AIntu-Far Eastern Memorial Hospital, Yuan Ze University, Academia Sinica, National Yang Ming Univers

Correct diagnosis of voice disorders require specific endoscopy performed by experience doctors. With the progress of computation capability, various machine learning algorithms have been applied to interpret complex clinical and laboratorial data. Our research group combined expert of phoniatrics, electric engineering, signal processing and artificial intelligence. We published the first research using deep learning approach to detect pathological voice in 2018. Our results exceeds all the existing classifiers, reached an accuracy of 99.14%. We also hosted the FEMH Voice Disorder Detection Competition on 2018 IEEE International Conference on Big Data, which was the first of its kind. More than 100 teams from 27 different countries participated in our competition. To breakthrough current research limitations of classification of voice disorders, we proposed multi-modal approaches to integrate demographic feature, acoustic signals, and clinical symptoms. Our results achieved an accuracy of 87.26% for the classification between phonotraumatic lesions, glottic neoplasm, and vocal palsy.

Cooperation projects and needs
1. IC design and cloud computation
2. IoT integrated system
3. Computer-aided diagnosis

AIntu-NTU Medical Genie Lifestyle and Environment Platform

NTU Medical Genie Lifestyle and Environment Platform
The product is mainly composed of wearable devices, IoT environmental sensors, deep learning technology and healthcare platform. It can collect and monitor the user's lifestyle and environment automatically, and assist medical staff in making decisions when an emergency occurs. Currently, chronic obstructive pulmonary disease (COPD) is the first success story. COPD is the fourth leading cause of death in the world. The frequency and severity of acute exacerbation are highly correlated with mortality. Our AECOPD prediction model can predict the possibility of acute exacerbation in the next 7 days based on daily physical activity data and environmental data. We have achieved 93.5 % on accuracy for the task of predicting whether a patient will suffer an acute exacerbation within the next 7 days.

Cooperation projects and needs
1. Wearable device provider
2. Healthcare service provider

AIntu-Prof. Ruey-Feng Chang

Fast ABUS CADe/CADx system and thorax x-ray CADx system with Clinical values

ProductI: Fast ABUS CADe/CADx system

The "Fast ABUS CADe/CADx system" designed with the deep learning technology is developed to locate the breast tumor in automatic whole breast ultrasound image (ABUS). Through the one-take and the one-stage detection architecture, our CADe can completely review an ABUS image within 1 second. Our system has properties of high detection rate, low false positive (FP), robustness (tumor size < 1 cm), and high diagnosis accuracy. In detection, it has only 2 FPs in 95% sensitivity, and significantly reduces the detection time, which indicates the system is of great worth in clinical breast examination. In diagnosis, the accuracy rate is up to 89.2%.

Product II: Multi-disease Thorax x-ray CADx system

The "Thorax x-ray CADx system" designed with the deep learning technology can diagnose 14 common thorax diseases in chest x-ray images, including Atelectasis, Consolidation, Infiltration, Pneumothorax, Edema, Emphysema, Fibrosis, Effusion, Pneumonia, Pleural thickening, Cardiomegaly, Nodule, Mass, and Hernia. Efficient image features extractor and enhanced disease correlation information can help analyze x-ray images into different thorax diseases. The CADx achieves better performance (mean AUC = 0.8266) than the state-of-the-art technique (SOTA). It reduces viewer variances, which provide diagnosis results objectively.

Cooperation projects and needs
1. Collaboration: Agents, healthcare providers, and AI hospital. Welcome to consult and collaboration.