57 research outputs found

    Exploring parental behavior and child interactive engagement : a study on children with a significant cognitive and motor developmental delay

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    Background and aims: Parenting factors are one of the most striking gaps in the current scientific literature on the development of young children with significant cognitive and motor disabilities. We aim to explore the characteristics of, and the association between, parental behavior and children's interactive engagement within this target group. Methods and procedures: Twenty-five parent-child dyads (with children aged 6-59 months) were video-taped during a 15-min unstructured play situation. Parents were also asked to complete the Parental Behavior Scale for toddlers. The video-taped observations were scored using the Child and Maternal Behavior Rating Scales. Outcomes and results: Low levels of parental discipline and child initiation were found. Parental responsivity was positively related to child attention and initiation. Conclusions and implications: Compared to children with no or other levels of disabilities, this target group exhibits large differences in frequency levels and, to a lesser extent, the concrete operationalization of parenting domains Further, this study confirms the importance of sensitive responsivity as the primary variable in parenting research

    The relevance of arterial blood pressure in the management of glaucoma progression: a systematic review

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    Background Glaucoma is one of the leading causes of global blindness and is expected to co-occur more frequently with vascular morbidities in the upcoming years, as both are aging-related diseases. Yet, the pathogenesis of glaucoma is not entirely elucidated and the interplay between intraocular pressure, arterial blood pressure and ocular perfusion pressure is poorly understood. Objective This systematic review aims to provide clinicians with the latest literature regarding the management of arterial blood pressure in glaucoma patients. Methods A systematic search was performed in Medline, Embase, Web of Science and Cochrane Library. Articles written in English assessing the influence of arterial blood pressure and systemic antihypertensive treatment of glaucoma and its management were eligible for inclusion. Additional studies were identified by revising references included in selected articles. Results 80 articles were included in this systemic review. A bimodal relation between blood pressure and glaucoma progression was found. Both high and low blood pressure increase the risk of glaucoma. Glaucoma progression was, possibly via ocular perfusion pressure variation, strongly associated with nocturnal dipping and high variability in the blood pressure over 24-hours. Conclusions We concluded that systemic blood pressure level associates with glaucomatous damage and provided recommendations for the management and study of arterial blood pressure in glaucoma. Prospective clinical trials are needed to further support these recommendations

    New Normative Database of Inner Macular Layer Thickness Measured by Spectralis OCT Used as Reference Standard for Glaucoma Detection

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    Purpose: This study examines the capacity to detect glaucoma of inner macular layer thickness measured by spectral-domain optical coherence tomography (SD-OCT) using a new normative database as the reference standard. Methods: Participants (N = 148) were recruited from Leuven (Belgium) and Zaragoza (Spain): 74 patients with early/moderate glaucoma and 74 age-matched healthy controls. One eye was randomly selected for a macular scan using the Spectralis SD-OCT. The variables measured with the instrument's segmentation software were: macular nerve fiber layer (mRNFL), ganglion cell layer (GCL), and inner plexiform layer (IPL) volume and thickness along with circumpapillary RNFL thickness (cpRNFL). The new normative database of macular variables was used to define the cutoff of normality as the fifth percentile by age group. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) of each macular measurement and of cpRNFL were used to distinguish between patients and controls. Results: Overall sensitivity and specificity to detect early-moderate glaucoma were 42.2% and 88.9% for mRNFL, 42.4% and 95.6% for GCL, 42.2% and 94.5% for IPL, and 53% and 94.6% for RNFL, respectively. The best macular variable to discriminate between the two groups of subjects was outer temporal GCL thickness as indicated by an AUROC of 0.903. This variable performed similarly to mean cpRNFL thickness (AUROC = 0.845; P = 0.29). Conclusions: Using our normative database as reference, the diagnostic power of inner macular layer thickness proved comparable to that of peripapillary RNFL thickness. Translational Relevance: Spectralis SD-OCT, cpRNFL thickness, and individual macular inner layer thicknesses show comparable diagnostic capacity for glaucoma and RNFL, GCL, and IPL thickness may be useful as an alternative diagnostic test when the measure of cpRNFL shows artifacts

    REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs

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    [EN] Glaucoma is one of the leading causes of irreversible but preventable blindness in working age populations. Color fundus photography (CFP) is the most cost-effective imaging modality to screen for retinal disorders. However, its application to glaucoma has been limited to the computation of a few related biomarkers such as the vertical cup-to-disc ratio. Deep learning approaches, although widely applied for medical image analysis, have not been extensively used for glaucoma assessment due to the limited size of the available data sets. Furthermore, the lack of a standardize benchmark strategy makes difficult to compare existing methods in a uniform way. In order to overcome these issues we set up the Retinal Fundus Glaucoma Challenge, REFUGE (https://refuge.grand-challenge.org), held in conjunction with MIC-CAI 2018. The challenge consisted of two primary tasks, namely optic disc/cup segmentation and glaucoma classification. As part of REFUGE, we have publicly released a data set of 1200 fundus images with ground truth segmentations and clinical glaucoma labels, currently the largest existing one. We have also built an evaluation framework to ease and ensure fairness in the comparison of different models, encouraging the development of novel techniques in the field. 12 teams qualified and participated in the online challenge. This paper summarizes their methods and analyzes their corresponding results. In particular, we observed that two of the top-ranked teams outperformed two human experts in the glaucoma classification task. Furthermore, the segmentation results were in general consistent with the ground truth annotations, with complementary outcomes that can be further exploited by ensembling the results.This work was supported by the Christian Doppler Research Association, the Austrian Federal Ministry for Digital and Economic Affairs and the National Foundation for Research, Technology and Development, J.I.O is supported by WWTF (Medical University of Vienna: AugUniWien/FA7464A0249, University of Vienna: VRG12- 009). Team Masker is supported by Natural Science Foundation of Guangdong Province of China (Grant 2017A030310647). Team BUCT is partially supported by the National Natural Science Foundation of China (Grant 11571031). The authors would also like to thank REFUGE study group for collaborating with this challenge.Orlando, JI.; Fu, H.; Breda, JB.; Van Keer, K.; Bathula, DR.; Diaz-Pinto, A.; Fang, R.... (2020). REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs. Medical Image Analysis. 59:1-21. https://doi.org/10.1016/j.media.2019.101570S12159Abramoff, M. D., Garvin, M. K., & Sonka, M. (2010). Retinal Imaging and Image Analysis. IEEE Reviews in Biomedical Engineering, 3, 169-208. doi:10.1109/rbme.2010.2084567Abràmoff, M. D., Lavin, P. T., Birch, M., Shah, N., & Folk, J. C. (2018). Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. npj Digital Medicine, 1(1). doi:10.1038/s41746-018-0040-6Al-Bander, B., Williams, B., Al-Nuaimy, W., Al-Taee, M., Pratt, H., & Zheng, Y. (2018). Dense Fully Convolutional Segmentation of the Optic Disc and Cup in Colour Fundus for Glaucoma Diagnosis. Symmetry, 10(4), 87. doi:10.3390/sym10040087Almazroa, A., Burman, R., Raahemifar, K., & Lakshminarayanan, V. (2015). Optic Disc and Optic Cup Segmentation Methodologies for Glaucoma Image Detection: A Survey. Journal of Ophthalmology, 2015, 1-28. doi:10.1155/2015/180972Burlina, P. M., Joshi, N., Pekala, M., Pacheco, K. D., Freund, D. E., & Bressler, N. M. (2017). Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks. JAMA Ophthalmology, 135(11), 1170. doi:10.1001/jamaophthalmol.2017.3782Carmona, E. J., Rincón, M., García-Feijoó, J., & Martínez-de-la-Casa, J. M. (2008). Identification of the optic nerve head with genetic algorithms. Artificial Intelligence in Medicine, 43(3), 243-259. doi:10.1016/j.artmed.2008.04.005Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 16, 321-357. doi:10.1613/jair.953Christopher, M., Belghith, A., Bowd, C., Proudfoot, J. A., Goldbaum, M. H., Weinreb, R. N., … Zangwill, L. M. (2018). Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs. Scientific Reports, 8(1). doi:10.1038/s41598-018-35044-9De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., … Ronneberger, O. (2018). Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature Medicine, 24(9), 1342-1350. doi:10.1038/s41591-018-0107-6Decencière, E., Zhang, X., Cazuguel, G., Lay, B., Cochener, B., Trone, C., … Klein, J.-C. (2014). FEEDBACK ON A PUBLICLY DISTRIBUTED IMAGE DATABASE: THE MESSIDOR DATABASE. Image Analysis & Stereology, 33(3), 231. doi:10.5566/ias.1155DeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L. (1988). Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach. Biometrics, 44(3), 837. doi:10.2307/2531595European Glaucoma Society Terminology and Guidelines for Glaucoma, 4th Edition - Part 1Supported by the EGS Foundation. (2017). British Journal of Ophthalmology, 101(4), 1-72. doi:10.1136/bjophthalmol-2016-egsguideline.001Farbman, Z., Fattal, R., Lischinski, D., & Szeliski, R. (2008). Edge-preserving decompositions for multi-scale tone and detail manipulation. ACM Transactions on Graphics, 27(3), 1-10. doi:10.1145/1360612.1360666Fu, H., Cheng, J., Xu, Y., Wong, D. W. K., Liu, J., & Cao, X. (2018). Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation. IEEE Transactions on Medical Imaging, 37(7), 1597-1605. doi:10.1109/tmi.2018.2791488Gómez-Valverde, J. J., Antón, A., Fatti, G., Liefers, B., Herranz, A., Santos, A., … Ledesma-Carbayo, M. J. (2019). Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning. Biomedical Optics Express, 10(2), 892. doi:10.1364/boe.10.000892Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., … Webster, D. R. (2016). Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA, 316(22), 2402. doi:10.1001/jama.2016.17216Hagiwara, Y., Koh, J. E. W., Tan, J. H., Bhandary, S. V., Laude, A., Ciaccio, E. J., … Acharya, U. R. (2018). Computer-aided diagnosis of glaucoma using fundus images: A review. Computer Methods and Programs in Biomedicine, 165, 1-12. doi:10.1016/j.cmpb.2018.07.012Haleem, M. S., Han, L., van Hemert, J., & Li, B. (2013). Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: A review. Computerized Medical Imaging and Graphics, 37(7-8), 581-596. doi:10.1016/j.compmedimag.2013.09.005Holm, S., Russell, G., Nourrit, V., & McLoughlin, N. (2017). DR HAGIS—a fundus image database for the automatic extraction of retinal surface vessels from diabetic patients. Journal of Medical Imaging, 4(1), 014503. doi:10.1117/1.jmi.4.1.014503Joshi, G. D., Sivaswamy, J., & Krishnadas, S. R. (2011). Optic Disk and Cup Segmentation From Monocular Color Retinal Images for Glaucoma Assessment. IEEE Transactions on Medical Imaging, 30(6), 1192-1205. doi:10.1109/tmi.2011.2106509Kaggle, 2015. Diabetic Retinopathy Detection. https://www.kaggle.com/c/diabetic-retinopathy-detection. [Online; accessed 10-January-2019].Kumar, J. R. H., Seelamantula, C. S., Kamath, Y. S., & Jampala, R. (2019). Rim-to-Disc Ratio Outperforms Cup-to-Disc Ratio for Glaucoma Prescreening. Scientific Reports, 9(1). doi:10.1038/s41598-019-43385-2Lavinsky, F., Wollstein, G., Tauber, J., & Schuman, J. S. (2017). The Future of Imaging in Detecting Glaucoma Progression. Ophthalmology, 124(12), S76-S82. doi:10.1016/j.ophtha.2017.10.011Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324. doi:10.1109/5.726791Li, Z., He, Y., Keel, S., Meng, W., Chang, R. T., & He, M. (2018). Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs. Ophthalmology, 125(8), 1199-1206. doi:10.1016/j.ophtha.2018.01.023Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., … Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60-88. doi:10.1016/j.media.2017.07.005Liu, S., Graham, S. L., Schulz, A., Kalloniatis, M., Zangerl, B., Cai, W., … You, Y. (2018). A Deep Learning-Based Algorithm Identifies Glaucomatous Discs Using Monoscopic Fundus Photographs. Ophthalmology Glaucoma, 1(1), 15-22. doi:10.1016/j.ogla.2018.04.002Lowell, J., Hunter, A., Steel, D., Basu, A., Ryder, R., Fletcher, E., & Kennedy, L. (2004). Optic Nerve Head Segmentation. IEEE Transactions on Medical Imaging, 23(2), 256-264. doi:10.1109/tmi.2003.823261Maier-Hein, L., Eisenmann, M., Reinke, A., Onogur, S., Stankovic, M., Scholz, P., … Kopp-Schneider, A. (2018). Why rankings of biomedical image analysis competitions should be interpreted with care. Nature Communications, 9(1). doi:10.1038/s41467-018-07619-7Miri, M. S., Abramoff, M. D., Lee, K., Niemeijer, M., Wang, J.-K., Kwon, Y. H., & Garvin, M. K. (2015). Multimodal Segmentation of Optic Disc and Cup From SD-OCT and Color Fundus Photographs Using a Machine-Learning Graph-Based Approach. IEEE Transactions on Medical Imaging, 34(9), 1854-1866. doi:10.1109/tmi.2015.2412881Niemeijer, M., van Ginneken, B., Cree, M. J., Mizutani, A., Quellec, G., Sanchez, C. I., … Abramoff, M. D. (2010). Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs. IEEE Transactions on Medical Imaging, 29(1), 185-195. doi:10.1109/tmi.2009.2033909Odstrcilik, J., Kolar, R., Budai, A., Hornegger, J., Jan, J., Gazarek, J., … Angelopoulou, E. (2013). Retinal vessel segmentation by improved matched filtering: evaluation on a new high‐resolution fundus image database. IET Image Processing, 7(4), 373-383. doi:10.1049/iet-ipr.2012.0455Orlando, J. I., Prokofyeva, E., & Blaschko, M. B. (2017). A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images. IEEE Transactions on Biomedical Engineering, 64(1), 16-27. doi:10.1109/tbme.2016.2535311Park, S. J., Shin, J. Y., Kim, S., Son, J., Jung, K.-H., & Park, K. H. (2018). A Novel Fundus Image Reading Tool for Efficient Generation of a Multi-dimensional Categorical Image Database for Machine Learning Algorithm Training. Journal of Korean Medical Science, 33(43). doi:10.3346/jkms.2018.33.e239Poplin, R., Varadarajan, A. V., Blumer, K., Liu, Y., McConnell, M. V., Corrado, G. S., … Webster, D. R. (2018). Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nature Biomedical Engineering, 2(3), 158-164. doi:10.1038/s41551-018-0195-0Porwal, P., Pachade, S., Kamble, R., Kokare, M., Deshmukh, G., Sahasrabuddhe, V., & Meriaudeau, F. (2018). Indian Diabetic Retinopathy Image Dataset (IDRiD): A Database for Diabetic Retinopathy Screening Research. Data, 3(3), 25. doi:10.3390/data3030025Prokofyeva, E., & Zrenner, E. (2012). Epidemiology of Major Eye Diseases Leading to Blindness in Europe: A Literature Review. Ophthalmic Research, 47(4), 171-188. doi:10.1159/000329603Raghavendra, U., Fujita, H., Bhandary, S. V., Gudigar, A., Tan, J. H., & Acharya, U. R. (2018). Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images. Information Sciences, 441, 41-49. doi:10.1016/j.ins.2018.01.051Reis, A. S. C., Sharpe, G. P., Yang, H., Nicolela, M. T., Burgoyne, C. F., & Chauhan, B. C. (2012). Optic Disc Margin Anatomy in Patients with Glaucoma and Normal Controls with Spectral Domain Optical Coherence Tomography. Ophthalmology, 119(4), 738-747. doi:10.1016/j.ophtha.2011.09.054Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., … Fei-Fei, L. (2015). ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision, 115(3), 211-252. doi:10.1007/s11263-015-0816-ySchmidt-Erfurth, U., Sadeghipour, A., Gerendas, B. S., Waldstein, S. M., & Bogunović, H. (2018). Artificial intelligence in retina. Progress in Retinal and Eye Research, 67, 1-29. doi:10.1016/j.preteyeres.2018.07.004Sevastopolsky, A. (2017). Optic disc and cup segmentation methods for glaucoma detection with modification of U-Net convolutional neural network. Pattern Recognition and Image Analysis, 27(3), 618-624. doi:10.1134/s1054661817030269Taha, A. A., & Hanbury, A. (2015). Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Medical Imaging, 15(1). doi:10.1186/s12880-015-0068-xThakur, N., & Juneja, M. (2018). Survey on segmentation and classification approaches of optic cup and optic disc for diagnosis of glaucoma. Biomedical Signal Processing and Control, 42, 162-189. doi:10.1016/j.bspc.2018.01.014Tham, Y.-C., Li, X., Wong, T. Y., Quigley, H. A., Aung, T., & Cheng, C.-Y. (2014). Global Prevalence of Glaucoma and Projections of Glaucoma Burden through 2040. Ophthalmology, 121(11), 2081-2090. doi:10.1016/j.ophtha.2014.05.013Johnson, S. S., Wang, J.-K., Islam, M. S., Thurtell, M. J., Kardon, R. H., & Garvin, M. K. (2018). Local Estimation of the Degree of Optic Disc Swelling from Color Fundus Photography. Lecture Notes in Computer Science, 277-284. doi:10.1007/978-3-030-00949-6_33Trucco, E., Ruggeri, A., Karnowski, T., Giancardo, L., Chaum, E., Hubschman, J. P., … Dhillon, B. (2013). Validating Retinal Fundus Image Analysis Algorithms: Issues and a Proposal. Investigative Opthalmology & Visual Science, 54(5), 3546. doi:10.1167/iovs.12-10347Vergara, I. A., Norambuena, T., Ferrada, E., Slater, A. W., & Melo, F. (2008). StAR: a simple tool for the statistical comparison of ROC curves. BMC Bioinformatics, 9(1). doi:10.1186/1471-2105-9-265Wu, Z., Shen, C., & van den Hengel, A. (2019). Wider or Deeper: Revisiting the ResNet Model for Visual Recognition. Pattern Recognition, 90, 119-133. doi:10.1016/j.patcog.2019.01.006Zheng, Y., Hijazi, M. H. A., & Coenen, F. (2012). Automated «Disease/No Disease» Grading of Age-Related Macular Degeneration by an Image Mining Approach. Investigative Opthalmology & Visual Science, 53(13), 8310. doi:10.1167/iovs.12-957

    Conventional and ambulatory blood pressure as predictors of diastolic left ventricular function in a Flemish population

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    Background--No longitudinal study compared associations of echocardiographic indexes of diastolic left ventricular function studies with conventional (CBP) and daytime ambulatory (ABP) blood pressure in the general population. Methods and Results--In 780 Flemish (mean age, 50.2 years; 51.7% women), we measured left atrial volume index (LAVI), peak velocities of the transmitral blood flow (E) and mitral annular movement (e0) in early diastole and E/e0 9.6 years (median) after CBP and ABP. In adjusted models including CBP and ABP, we expressed associations per 10/5-mm Hg systolic/diastolic blood pressure increments. LAVI and E/e0 were 0.65/0.40 mL/m2 and 0.17/0.09 greater with higher systolic/diastolic ABP (P≤0.028), but not with higher baseline CBP (P≤0.086). e0 was lower (P≤0.032) with higher diastolic CBP (-0.09 cm/s) and ABP (-0.19 cm/s). When we substituted baseline CBP by CBP recorded concurrently with echocardiography, LAVI and E/e0 remained 0.45/0.38 mL/m2 and 0.15/0.08 greater with baseline ABP (P≤0.036), while LAVI (+0.53 mL/m2) and E/e0 (+0.19) were also greater (P < 0.001) in relation to concurrent systolic CBP. In categorized analyses of baseline data, sustained hypertension or masked hypertension compared with normotension or white-

    Retinal oxygen saturation as a non-invasive estimate for mixed venous oxygen saturation and cardiac output

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    PURPOSE: To investigate the correlation between retinal vessel oxygen saturation and mixed venous oxygen saturation (SvO2-mixed ) and cardiac output (CO). METHODS: Retinal arterial (SaO2-retinal ) and venous (SvO2-retinal ) oxygen saturation were measured non-invasively with dual-wavelength retinal oximetry in subjects receiving invasive measurements of SvO2-mixed and CO through right heart catheterization. Correlations were analysed using Spearman's rank correlation coefficients and linear regression models. RESULTS: Fourteen patients (median age 62.7 years, range: 21-77) were included in the analysis. When adjusted for age, SvO2-retinal showed a positive correlation with SvO2-mixed (β = 0.80, p = 0.003). Retinal arteriovenous oxygen saturation difference was significantly correlated with the inverse of CO (Spearman's ρ = 0.59, p = 0.026). CONCLUSION: This pilot study provides proof of concept for the use of retinal oximetry as a non-invasive tool to assess systemic cardiovascular function.status: publishe

    Correlation Between Peripapillary Choroidal Thickness and Retinal Vessel Oxygen Saturation in Young Healthy Individuals and Glaucoma Patients

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    To investigate the correlation between peripapillary choroidal thickness (CT) and retinal vessel oxygen saturation (SO2) in young healthy individuals and open-angle glaucoma (OAG) patients.status: publishe

    Non-invasive assessment of cerebral oxygenation: A comparison of retinal and transcranial oximetry

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    To investigate the correlation between cerebral (SO2-transcranial), retinal arterial (SaO2-retinal) and venous (SvO2-retinal) oxygen saturation as measured by near-infrared spectroscopy (NIRS) and retinal oximetry respectively.status: publishe
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