Mr. Xiaohui Shan Profile

Xiaohui Shan

at Tsinghua Univ

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Publications (2)

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Proceedings Article | 15 October 2021 Presentation + Paper

A non-invasive diabetes diagnosis method based on novel scleral imaging instrument and AI

Wenqi Lv, Rongxin Fu, Xue Lin, Ya Su, Xiangyu Jin, Han Yang, Xiaohui Shan, Wenli Du, Kai Jiang, Yuanhua Lin, Guoliang Huang

Proceedings Volume 11900, 1190013 (2021) https://doi.org/10.1117/12.2601222

KEYWORDS: Artificial intelligence, Glucose, Image segmentation, Blood, Eye, Sclera, Performance modeling, Imaging systems, Eye models

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Type 2 diabetes mellitus is one of the most common metabolic diseases in the world. However, frequent blood glucose testing causes continual harm to diabetics, which cannot meet the needs of early diagnosis and long-term tracking of diabetes. Thus non-invasive adjuvant diagnosis methods are urgently needed, enabling early screening of the population for diabetes, the evaluation of diabetes risk, and assessment of therapeutic effects. The human eye plays an important role in painless and non-invasive approaches, because it is considered an internal organ but can be easily be externally observed. We developed an AI model to predict the probability of diabetes from scleral images taken by a specially developed instrument, which could conveniently and quickly collect complete scleral images in four directions and perform artificial intelligence (AI) analysis in 3 min without any reagent consumption or the need for a laboratory. The novel optical instrument could adaptively eliminate reflections and collected shadow-free scleral images. 177 subjects were recruited to participate in this experiment, including 127 benign subjects and 50 malignant subjects. The blood sample and sclera images from each subject was obtained. The scleral image classification model achieved a mean AUC over 0.85, which indicates great potential for early screening of practical diabetes during periodic physical checkups or daily family health monitoring. With this AI scleral features imaging and analysis method, diabetic patients’ health conditions can be rapidly, noninvasively, and accurately analyzed, which offers a platform for noninvasive forecasting, early diagnosis, and long-term monitoring for diabetes and its complications.

Proceedings Article | 9 October 2021 Presentation + Paper

Quantitative and long-term cell imaging with computational hyperspectral interferometry

Rongxin Fu, Ya Su, Ruliang Wang, Xue Lin, Xiangyu Jin, Han Yang, Wenli Du, Xiaohui Shan, Wenqi Lv, Guoliang Huang

Proceedings Volume 11900, 119000Q (2021) https://doi.org/10.1117/12.2600858

KEYWORDS: Refractive index, Interferometry, Imaging systems, Microscopy, Reflectivity, Phase imaging, Microfluidics, Scattering, Hyperspectral imaging, Cancer

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Quantitative phase imaging (QPI) has quickly emerged as a powerful tool for label-free living cell morphology and metabolism monitoring. However, for current QPI techniques, interference signals from different layers overlay with each other and impede nanoscale optical sectioning. This phenomenon leads to unsatisfactory performances for optically thick or complex scattering biological samples. To address this challenge, we have developed an alternative quantitative phase microscopy with computational hyperspectral interferometry. Nanoscale optical sectioning could be achieved with Fourier domain spectral decomposition. Morphological fluctuations and refractive index distribution could be reconstructed simultaneously with 89.2 nm axial resolution and 1.91 nm optical path difference sensitivity. With this method, we established a label-free cell imaging system for long-term cellular dry mass measurement and in-situ dynamic single cell monitoring. Different intrinsic cell growth characteristics of dry mass between HeLa cells and Human Cervical Epithelial Cells (HCerEpiC) were studied. The dry mass of HeLa cells consistently increased before M phase, whereas that of HCerEpiC increased and then decreased. The maximum growth rate of HeLa cells was 11.7% higher than that of HCerEpiC. We also use the proposed method and system to explore the relationship between cellular dry mass distributions and drug effects for cancer cells. The results show that cells with higher nuclear dry mass and nuclear density standard deviations were more likely to survive the chemotherapy. The presented work shows potential values for cell growth dynamics research, cell health characterization, medication guidance and adjuvant drug development.

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