[1] Wei A N, Qin C B, Dong S et al. Research progress of super-resolution fluorescence microscopy[J]. Laser & Optoelectronics Progress, 60, 1106012(2023).

[2] Cao Y T, Wang X, Lu X C et al. Label-free optical microscopy technique and its biomedical applications[J]. Laser & Optoelectronics Progress, 59, 0617012(2022).

[3] Fang N, Wu Z Y, Wang X F et al. Multiphoton technique for visualization of angiomatous meningiomas[J]. Laser & Optoelectronics Progress, 59, 0617025(2022).

[4] Ávila F J, Gambín A, Artal P et al. In vivo two-photon microscopy of the human eye[J]. Scientific Reports, 9, 10121(2019).

[5] Vicidomini G, Bianchini P, Diaspro A. STED super-resolved microscopy[J]. Nature Methods, 15, 173-182(2018).

[6] Sachdeva S, Davis R W, Saha A K. Microfluidic point-of-care testing: commercial landscape and future directions[J]. Frontiers in Bioengineering and Biotechnology, 8, 602659(2021).

[7] Berlanda S F, Breitfeld M, Dietsche C L et al. Recent advances in microfluidic technology for bioanalysis and diagnostics[J]. Analytical Chemistry, 93, 311-331(2021).

[8] Yeo L Y, Chang H C, Chan P P Y et al. Microfluidic devices for bioapplications[J]. Small, 7, 12-48(2011).

[9] Zhao Y H, Stratton Z S, Guo F et al. Optofluidic imaging: now and beyond[J]. Lab on a Chip, 13, 17-24(2013).

[10] Minzioni P, Osellame R, Sada C et al. Roadmap for optofluidics[J]. Journal of Optics, 19, 093003(2017).

[11] Zhu J M, Zhu X Q, Zuo Y F et al. Optofluidics: the interaction between light and flowing liquids in integrated devices[J]. Opto-Electronic Advances, 2, 190007(2019).

[12] Chen S H, Hao R, Zhang Y et al. Optofluidics in bio-imaging applications[J]. Photonics Research, 7, 532-542(2019).

[13] Tang J K, Qiu G Y, Wang J. Recent development of optofluidics for imaging and sensing applications[J]. Chemosensors, 10, 15(2022).

[14] Nishikawa M, Kanno H, Zhou Y Q et al. Massive image-based single-cell profiling reveals high levels of circulating platelet aggregates in patients with COVID-19[J]. Nature Communications, 12, 7135(2021).

[15] Siu D M D, Lee K C M, Chung B M F et al. Optofluidic imaging meets deep learning: from merging to emerging[J]. Lab on a Chip, 23, 1011-1033(2023).

[16] Zare Harofte S, Soltani M, Siavashy S et al. Recent advances of utilizing artificial intelligence in lab on a chip for diagnosis and treatment[J]. Small, 18, 2203169(2022).

[17] Huang K R, Matsumura H, Zhao Y Q et al. Deep imaging flow cytometry[J]. Lab on a Chip, 22, 876-889(2022).

[18] Isozaki A, Harmon J, Zhou Y Q et al. AI on a chip[J]. Lab on a Chip, 20, 3074-3090(2020).

[19] Pratt S L, Zath G K, Akiyama T et al. DropSOAC: stabilizing microfluidic drops for time-lapse quantification of single-cell bacterial physiology[J]. Frontiers in Microbiology, 10, 2112(2019).

[20] Mandracchia B, Son J, Jia S. Super-resolution optofluidic scanning microscopy[J]. Lab on a Chip, 21, 489-493(2021).

[21] Mikami H, Harmon J, Kobayashi H et al. Ultrafast confocal fluorescence microscopy beyond the fluorescence lifetime limit[J]. Optica, 5, 117-126(2018).

[22] Mathur P, Fomitcheva Khartchenko A, Stavrakis S et al. Quantifying antibody binding kinetics on fixed cells and tissues via fluorescence lifetime imaging[J]. Analytical Chemistry, 94, 10967-10975(2022).

[23] Lee D H, Li X, Ma N et al. Rapid and label-free identification of single leukemia cells from blood in a high-density microfluidic trapping array by fluorescence lifetime imaging microscopy[J]. Lab on a Chip, 18, 1349-1358(2018).

[24] Son J, Mandracchia B, Silva Trenkle A D et al. Portable light-sheet optofluidic microscopy for 3D fluorescence imaging flow cytometry[J]. Lab on a Chip, 23, 624-630(2023).

[25] Ugawa M, Ota S. High-throughput parallel optofluidic 3D-imaging flow cytometry[J]. Small Science, 2, 2100126(2022).

[26] Mikami H, Kawaguchi M, Huang C J et al. Virtual-freezing fluorescence imaging flow cytometry[J]. Nature Communications, 11, 1162(2020).

[27] Goda K, Tsia K K, Jalali B. Serial time-encoded amplified imaging for real-time observation of fast dynamic phenomena[J]. Nature, 458, 1145-1149(2009).

[28] Goda K, Ayazi A, Gossett D R et al. High-throughput single-microparticle imaging flow analyzer[J]. Proceedings of the National Academy of Sciences of the United States of America, 109, 11630-11635(2012).

[29] Jiang Y Y, Lei C, Yasumoto A et al. Label-free detection of aggregated platelets in blood by machine-learning-aided optofluidic time-stretch microscopy[J]. Lab on a Chip, 17, 2426-2434(2017).

[30] Deng Y J, Duque J A, Su C X et al. Understanding stenosis-induced platelet aggregation on a chip by high-speed optical imaging[J]. Sensors and Actuators B: Chemical, 356, 131318(2022).

[31] Zhang C Q, Herbig M, Zhou Y Q et al. Real-time intelligent classification of COVID-19 and thrombosis via massive image-based analysis of platelet aggregates[J]. Cytometry Part A, 103, 492-499(2023).

[32] Park Y, Depeursinge C, Popescu G. Quantitative phase imaging in biomedicine[J]. Nature Photonics, 12, 578-589(2018).

[33] Aknoun S, Yonnet M, Djabari Z et al. Quantitative phase microscopy for non-invasive live cell population monitoring[J]. Scientific Reports, 11, 4409(2021).

[34] Lee K C M, Lau A K S, Tang A H L et al. Multi-ATOM: ultrahigh-throughput single-cell quantitative phase imaging with subcellular resolution[J]. Journal of Biophotonics, 12, e201800479(2019).

[35] Deng Y J, Tay H M, Zhou Y Q et al. Studying the efficacy of antiplatelet drugs on atherosclerosis by optofluidic imaging on a chip[J]. Lab on a Chip, 23, 410-420(2023).

[36] Suzuki Y, Kobayashi K, Wakisaka Y et al. Label-free chemical imaging flow cytometry by high-speed multicolor stimulated Raman scattering[J]. Proceedings of the National Academy of Sciences of the United States of America, 116, 15842-15848(2019).

[37] Lyu Y K, Yuan X F, Glidle A et al. Automated Raman based cell sorting with 3D microfluidics[J]. Lab on a Chip, 20, 4235-4245(2020).

[38] Nitta N, Iino T, Isozaki A et al. Raman image-activated cell sorting[J]. Nature Communications, 11, 3452(2020).

[39] Kinegawa R, Gala de Pablo J, Wang Y et al. Label-free multiphoton imaging flow cytometry[J]. Cytometry. Part A, 103, 584-592(2023).

[40] Zhang T L, Hong Z Y, Tang S Y et al. Focusing of sub-micrometer particles in microfluidic devices[J]. Lab on a Chip, 20, 35-53(2020).

[41] Yan S, Yuan D. Continuous microfluidic 3D focusing enabling microflow cytometry for single-cell analysis[J]. Talanta, 221, 121401(2021).

[42] Fu L M, Hong T F, Wen C Y et al. Electrokinetic instability effects in microchannels with and without nanofilm coatings[J]. Electrophoresis, 29, 4871-4879(2008).

[43] Chou W P, Wang H M, Chang J H et al. The utilization of optically-induced-dielectrophoresis (ODEP)-based virtual cell filters in a microfluidic system for continuous isolation and purification of circulating tumour cells (CTCs) based on their size characteristics[J]. Sensors and Actuators B: Chemical, 241, 245-254(2017).

[44] Sadeghian H, Hojjat Y, Soleimani M. Interdigitated electrode design and optimization for dielectrophoresis cell separation actuators[J]. Journal of Electrostatics, 86, 41-49(2017).

[45] Salari A, Thompson M. Recent advances in AC electrokinetic sample enrichment techniques for biosensor development[J]. Sensors and Actuators B: Chemical, 255, 3601-3615(2018).

[46] Lin C H, Lee G B, Fu L M et al. Vertical focusing device utilizing dielectrophoretic force and its application on microflow cytometer[J]. Journal of Microelectromechanical Systems, 13, 923-932(2004).

[47] Leibacher I, Reichert P, Dual J. Microfluidic droplet handling by bulk acoustic wave (BAW) acoustophoresis[J]. Lab on a Chip, 15, 2896-2905(2015).

[48] Fakhfouri A, Devendran C, Albrecht T et al. Surface acoustic wave diffraction driven mechanisms in microfluidic systems[J]. Lab on a Chip, 18, 2214-2224(2018).

[49] Chen Y C, Nawaz A A, Zhao Y H et al. Standing surface acoustic wave (SSAW)-based microfluidic cytometer[J]. Lab on a Chip, 14, 916-923(2014).

[50] Wu M X, Chen K J, Yang S J et al. High-throughput cell focusing and separation via acoustofluidic tweezers[J]. Lab on a Chip, 18, 3003-3010(2018).

[51] Nawaz A A, Zhang X J, Mao X L et al. Sub-micrometer-precision, three-dimensional (3D) hydrodynamic focusing via “microfluidic drifting”[J]. Lab on a Chip, 14, 415-423(2014).

[52] Mao X L, Nawaz A A, Lin S C S et al. An integrated, multiparametric flow cytometry chip using “microfluidic drifting” based three-dimensional hydrodynamic focusing[J]. Biomicrofluidics, 6, 24113-241139(2012).

[53] Shah P, Kendall F, Khozin S et al. Artificial intelligence and machine learning in clinical development: a translational perspective[J]. NPJ Digital Medicine, 2, 69(2019).

[54] Rivenson Y, Wang H D, Wei Z S et al. Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning[J]. Nature Biomedical Engineering, 3, 466-477(2019).

[55] Bai B J, Yang X L, Li Y Z et al. Deep learning-enabled virtual histological staining of biological samples[J]. Light, Science & Applications, 12, 57(2023).

[56] LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 521, 436-444(2015).

[57] Feng S J, Xiao Y L, Yin W et al. Fringe-pattern analysis with ensemble deep learning[J]. Advanced Photonics Nexus, 2, 036010(2023).

[58] Yang J G, Choi S, Kim J et al. Recent advances in deep-learning-enhanced photoacoustic imaging[J]. Advanced Photonics Nexus, 2, 054001(2023).

[59] Wang J Y, Fan J C, Zhou B et al. Hybrid reconstruction of the physical model with the deep learning that improves structured illumination microscopy[J]. Advanced Photonics Nexus, 2, 016012(2023).

[60] He Y Z, Yao J et al. Self-supervised deep-learning two-photon microscopy[J]. Photonics Research, 11, 1-11(2022).

[61] Wang W H, Zhao X, Jiang Z X et al. Deep learning-based scattering removal of light field imaging[J]. Chinese Optics Letters, 20, 041101(2022).

[62] Riordon J, Sovilj D, Sanner S et al. Deep learning with microfluidics for biotechnology[J]. Trends in Biotechnology, 37, 310-324(2019).

[63] Zhang F Z, Lei C, Huang C J et al. Intelligent image de-blurring for imaging flow cytometry[J]. Cytometry Part A, 95, 549-554(2019).

[64] Heo Y J, Lee D, Kang J S et al. Real-time image processing for microscopy-based label-free imaging flow cytometry in a microfluidic chip[J]. Scientific Reports, 7, 11651(2017).

[65] Siu D M D, Lee K C M, Lo M C K et al. Deep-learning-assisted biophysical imaging cytometry at massive throughput delineates cell population heterogeneity[J]. Lab on a Chip, 20, 3696-3708(2020).