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188 results on '"Yao-Hong Liu"'

1. Artificial intelligence in the anterior segment of eye diseases

Author

Yao-Hong Liu, Lin-Yu Li, Si-Jia Liu, Li-Xiong Gao, Yong Tang, Zhao-Hui Li, and Zi Ye

Subjects
artificial intelligence, anterior segment ocular disease, ametropia, glaucoma, Ophthalmology, RE1-994
Abstract

Ophthalmology is a subject that highly depends on imaging examination. Artificial intelligence (AI) technology has great potential in medical imaging analysis, including image diagnosis, classification, grading, guiding treatment and evaluating prognosis. The combination of the two can realize mass screening of grass-roots eye health, making it possible to seek medical treatment in the mode of “first treatment at the grass-roots level, two-way referral, emergency and slow treatment, and linkage between the upper and lower levels”. On the basis of summarizing the AI technology carried out by scholars and their teams all over the world in the field of ophthalmology, quite a lot of studies have confirmed that machine learning can assist in diagnosis, grading, providing optimal treatment plans and evaluating prognosis in corneal and conjunctival diseases, ametropia, lens diseases, glaucoma, iris diseases, etc. This paper systematically shows the application and progress of AI technology in common anterior segment ocular diseases, the current limitations, and prospects for the future.

Published
2024
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4. Migraine classification by machine learning with functional near-infrared spectroscopy during the mental arithmetic task

Author

Wei-Ta Chen, Cing-Yan Hsieh, Yao-Hong Liu, Pou-Leng Cheong, Yi-Min Wang, and Chia-Wei Sun

Subjects
Medicine, Science
Abstract

Abstract Migraine is a common and complex neurovascular disorder. Clinically, the diagnosis of migraine mainly relies on scales, but the degree of pain is too subjective to be a reliable indicator. It is even more difficult to diagnose the medication-overuse headache, which can only be evaluated by whether the symptom is improved after the medication adjustment. Therefore, an objective migraine classification system to assist doctors in making a more accurate diagnosis is needed. In this research, 13 healthy subjects (HC), 9 chronic migraine subjects (CM), and 12 medication-overuse headache subjects (MOH) were measured by functional near-infrared spectroscopy (fNIRS) to observe the change of the hemoglobin in the prefrontal cortex (PFC) during the mental arithmetic task (MAT). Our model shows the sensitivity and specificity of CM are 100% and 75%, and that of MOH is 75% and 100%.The results of the classification of the three groups prove that fNIRS combines with machine learning is feasible for the migraine classification.

Published
2022
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5. Benchmarking of hardware-efficient real-time neural decoding in brain–computer interfaces

Author

Paul Hueber, Guangzhi Tang, Manolis Sifalakis, Hua-Peng Liaw, Aurora Micheli, Nergis Tomen, and Yao-Hong Liu

Subjects
neural decoding, brain computer interfaces, closed-loop neuromodulation, spiking neural networks, neuromorphic computing, Electronic computers. Computer science, QA75.5-76.95
Abstract

Designing processors for implantable closed-loop neuromodulation systems presents a formidable challenge owing to the constrained operational environment, which requires low latency and high energy efficacy. Previous benchmarks have provided limited insights into power consumption and latency. However, this study introduces algorithmic metrics that capture the potential and limitations of neural decoders for closed-loop intra-cortical brain–computer interfaces in the context of energy and hardware constraints. This study benchmarks common decoding methods for predicting a primate’s finger kinematics from the motor cortex and explores their suitability for low latency and high energy efficient neural decoding. The study found that ANN-based decoders provide superior decoding accuracy, requiring high latency and many operations to effectively decode neural signals. Spiking neural networks (SNNs) have emerged as a solution, bridging this gap by achieving competitive decoding performance within sub-10 ms while utilizing a fraction of computational resources. These distinctive advantages of neuromorphic SNNs make them highly suitable for the challenging closed-loop neural modulation environment. Their capacity to balance decoding accuracy and operational efficiency offers immense potential in reshaping the landscape of neural decoders, fostering greater understanding, and opening new frontiers in closed-loop intra-cortical human-machine interaction.

Published
2024
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14. A 3-10GHz 21.5mW/Channel RX and 8.9mW TX IR-UWB 802.15.4a/z 1T3R Transceiver.

Author

Elbert Bechthum, Minyoung Song, Gaurav Singh 0005, Erwin Allebes, Charis Basetas, Pepijn Boer, Arjan Breeschoten, Stefan Cloudt, Johan Dijkhuis, Ming Ding 0003, Sherwin Gatchalian, Yuming He, Johan H. C. van den Heuvel, Martijn Hijdra, Paul Mateman, Bernard Meyer, Gert-Jan van Schaik, Mohieddine El Soussi, Bart Thijssen, Stefano Traferro, Evgenii Turin, Peter Vis, Nick Winkel, Peng Zhang 0028, Yao-Hong Liu, and Christian Bachmann

Published
2022
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16. An 8.7 mW/TX, 21 mW/RX 6-to-9GHz IEEE 802.15.4a/4z Compliant IR-UWB Transceiver with Pulse Pre-Emphasis achieving 14mm Ranging Precision.

Author

Minyoung Song, Erwin Allebes, Chris Marshall, Anoop Narayan Bhat, Elbert Bechthum, Johan Dijkhuis, Stefano Traferro, Evgenii Tiurin, Peter Vis, Johan H. C. van den Heuvel, Mohieddine El Soussi, Pepijn Boer, Alireza Sheikh, Bernard Meyer, Jiang Liu 0001, Stan van der Ven, Nick Winkel, Martijn Hijdra, Gururaja Kasanadi Ramachandra, Yunus Baykal, Huib Visser, Peng Zhang 0028, Arjan Breeschoten, Yao-Hong Liu, and Christian Bachmann

Published
2023
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