Deep learning a boon for biophotonics

This review covers original articles using deep learning in the biophotonic field published in the last years. In these years deep learning, which is a subset of machine learning mostly based on artificial neural network geometries, was applied to a number of biophotonic tasks and has achieved state-of-the-art performances. Therefore, deep learning in the biophotonic field is rapidly growing and it will be utilized in the next years to obtain real-time biophotonic decision-making systems and to analyze biophotonic data in general. In this contribution, we discuss the possibilities of deep learning in the biophotonic field including image classification, segmentation, registration, pseudostaining and resolution enhancement. Additionally, we discuss the potential use of deep learning for spectroscopic data including spectral data preprocessing and spectral classification. We conclude this review by addressing the potential applications and challenges of using deep learning for biophotonic data. © 2020 The Authors. Journal of Biophotonics published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinhei

Computational tissue staining of non-linear multimodal imaging using supervised and unsupervised deep learning

Hematoxylin and Eosin (H&E) staining is the 'gold-standard' method in histopathology. However, standard H&E staining of high-quality tissue sections requires long sample preparation times including sample embedding, which restricts its application for 'real-time' disease diagnosis. Due to this reason, a label-free alternative technique like non-linear multimodal (NLM) imaging, which is the combination of three non-linear optical modalities including coherent anti-Stokes Raman scattering, two-photon excitation fluorescence and second-harmonic generation, is proposed in this work. To correlate the information of the NLM images with H&E images, this work proposes computational staining of NLM images using deep learning models in a supervised and an unsupervised approach. In the supervised and the unsupervised approach, conditional generative adversarial networks (CGANs) and cycle conditional generative adversarial networks (cycle CGANs) are used, respectively. Both CGAN and cycle CGAN models generate pseudo H&E images, which are quantitatively analyzed based on mean squared error, structure similarity index and color shading similarity index. The mean of the three metrics calculated for the computationally generated H&E images indicate significant performance. Thus, utilizing CGAN and cycle CGAN models for computational staining is beneficial for diagnostic applications without performing a laboratory-based staining procedure. To the author's best knowledge, it is the first time that NLM images are computationally stained to H&E images using GANs in an unsupervised manner

Semantic segmentation of non-linear multimodal images for disease grading of inflammatory bowel disease: A segnet-based application

Non-linear multimodal imaging, the combination of coherent anti-stokes Raman scattering (CARS), two-photon excited fluorescence (TPEF) and second harmonic generation (SHG), has shown its potential to assist the diagnosis of different inflammatory bowel diseases (IBDs). This label-free imaging technique can support the ‘gold-standard’ techniques such as colonoscopy and histopathology to ensure an IBD diagnosis in clinical environment. Moreover, non-linear multimodal imaging can measure biomolecular changes in different tissue regions such as crypt and mucosa region, which serve as a predictive marker for IBD severity. To achieve a real-time assessment of IBD severity, an automatic segmentation of the crypt and mucosa regions is needed. In this paper, we semantically segment the crypt and mucosa region using a deep neural network. We utilized the SegNet architecture (Badrinarayanan et al., 2015) and compared its results with a classical machine learning approach. Our trained SegNet mod el achieved an overall F1 score of 0.75. This model outperformed the classical machine learning approach for the segmentation of the crypt and mucosa region in our study

Identifying research priorities for road safety in Nepal: A Delphi study

OBJECTIVE: To identify and prioritise the research needed to help Nepali agencies develop an improved road safety system. DESIGN: Delphi study.Nepal. PARTICIPANTS: Stakeholders from government institutions, academia, engineering, healthcare and civil society were interviewed to identify knowledge gaps and research questions. Participants then completed two rounds of ranking and a workshop. RESULTS: A total of 93 participants took part in interviews and two rounds of ranking. Participants were grouped with others sharing expertise relating to each of the five WHO 'pillars' of road safety: (1) road safety management; (2) safer roads; (3) safer vehicles; (4) safer road users and (5) effective postcrash response. Interviews yielded 1019 research suggestions across the five pillars. Two rounds of ranking within expert groups yielded consensus on the important questions for each pillar. A workshop involving all participants then led to the selection of 6 questions considered the most urgent: (1) How can implementing agencies be made more accountable? (2) How should different types of roads, and roads in different geographical locations, be designed to make them safer for all road users? (3) What vehicle fitness factors lead to road traffic crashes? (4) How can the driver licensing system be improved to ensure safer drivers? (5) What factors lead to public vehicle crashes and how can they be addressed? and (6) What factors affect emergency response services getting to the patient and then getting them to the right hospital in the best possible time? CONCLUSIONS: The application of the Delphi approach is useful to enable participants representing a range of institutions and expertise to contribute to the identification of road safety research priorities. Outcomes from this study provide Nepali researchers with a greater understanding of the necessary focus for future road safety research

Auf künstlicher Intelligenz basierende Technologien für biophotonische Daten

For decades, biophotonic technologies have been booming in various fields of sciences. These technologies reveal not only structural but also molecular and functional changes in the sample under investigation. Additionally, they have prominent advantages such as high molecular sensitivity, high usability, high compactness, and high spatial and temporal resolution. Due to these advantages, biophotonic technologies have great potential in clinical applications. Nowadays, researchers emphasize the use of biophotonic technologies for point-of-care testing in clinics and the in vivo imaging of live cells for automating the disease diagnosis workflow. Furthermore, researchers are also focusing on integrating multiple biophotonic technologies in a single unit for understanding diseases at the cellular, molecular, and tissue level. Such ever-increasing developments in biophotonic technologies result in a massive amount of biophotonic data, and analysis of large biophotonic data by a human being is challenging. Therefore, algorithms that can automatically analyze biophotonic data to extract useful "patterns" like an experienced person are crucial. Extracting patterns from data using algorithms which can imitate human intelligence by learning from the data itself is categorized into a field of "artificial intelligence" (AI). Utilizing AI to analyze data from biophotonic technologies like Raman spectroscopy, coherent anti-Stokes Raman scattering (CARS) microscopy, two-photon excitation fluorescence (TPEF) microscopy, and second-harmonic generation (SHG) microscopy is the main highlight of this thesis. Concisely, this thesis will use AI and biophotonic data for biomedical applications like the prediction of disease, segmentation of various regions in tissue, and transformation of one modality into another modality. The results in this thesis will show that utilizing AI, along with biophotonic technologies, can benefit the field of biomedicine and the life sciences

Time of pediatric intensive care unit admission and mortality: a systematic review and meta-analysis

The aim of this study was to determine the association between the time of admission (day, night, and/or weekends) and mortality among critically ill children admitted to a pediatric intensive care unit (PICU). Electronic databases that were searched include PubMed, Embase, Web of Science, CINAHL (Cumulative Index of Nursing and Allied Health Literature), Ovid, and Cochrane Library since inception till June 15, 2018. The article included observational studies reporting inhospital mortality and the time of admission to PICU limited to patients aged younger than 18 years. Meta-analysis was performed by a frequentist approach with both fixed and random effect models. The GRADE (Grading of Recommendations Assessment, Development, and Evaluation) approach was used to evaluate the quality of evidence. Ten studies met our inclusion criteria. Five studies comparing weekday with weekend admissions showed better odds of survival on weekdays (odds ratio [OR]: 0.77; 95% confidence interval [CI]: 0.60–0.99). Pooled data of four studies showed that odds of mortality were similar between day and night admissions (OR: 0.93; 95% CI: 0.77–1.13). Similarly, three studies comparing admission during off-hours versus regular hours did not show better odds of survival during regular hours (OR: 0.77; 95% CI: 0.57–1.05). Heterogeneity was significant due to variable sample sizes and time period. Inconsistency in adjusting for confounders across the included studies precluded us from analyzing the adjusted risk of mortality. Weekday admissions to PICU were associated with lesser odds of mortality. No significant differences in the odds of mortality were found between admissions during day versus night or between admission during regular hours and that during off-hours. However, the evidence is of low quality and requires larger prospective studies

Paracetamol exposure and asthma: What does the evidence say? An overview of systematic reviews

Objective: To conduct an umbrella review collating the existing evidence to determine whether there is an association between exposure of Paracetamol in-utero or in infancy and the development of childhood Asthma. Methods: In this review, systematic reviews with or without meta-analysis that reported the association between paracetamol and asthma in children were included. To identify relevant reviews, a search was performed in the electronic databases PubMed, the Cochrane Library, and Ovid MEDLINE. The protocol was registered in PROSPERO CRD42020156023. A separate search was conducted for primary studies from the last 5 years not yet included in systematic reviews reporting the association from January 2016 to March 2021. Results: The electronic searches identified 1966 review titles. After the removal of 493 duplicates, 1475 titles and abstracts were screened against the eligibility criteria. Full-text screening yielded six systematic reviews to be included in this review. The search for primary studies in the last 5 years yielded 1214 hits, out of which 5 studies were found suitable for inclusion. Three of them, that were not included in the systematic reviews, and have been summarised in this paper. The odds ratios (ORs) for the outcome of asthma in offspring of mothers with prenatal paracetamol consumption in any trimester were 1.28 (1.13–1.39) and 1.21 (1.02–1.44). For first trimester exposures, they were 1.12 (0.99–1.27), 1.39 (1.01–1.91), and 1.21 (1.14–1.28), for the second or third trimester, they were 1.49 (1.37–1.63) and 1.13 (1.04–1.23). For the third trimester only, the figure was 1.17 (1.04–1.31). Of the six reviews included, 1 had a low risk of bias, 2 had an unclear risk while 3 had a high risk of bias assessed using the ROBIS tool. There was no significant increased risk of asthma with early infancy exposure. The inter-study heterogeneity varied from I2 = 41% to I2 = 76% across reviews. In the primary studies, the OR for prenatal exposure ranged from 1.12 (0.25–4.98) to 4.66 (1.92–11.3) and for infancy exposure was 1.56 (1.06–2.30). All three included primary studies were adjudged to be of high quality using the Newcastle Ottawa scale. Conclusions: There is a modest association between paracetamol exposure in-utero and the future development of asthma. Exposure in infancy has a less consistant association. All the studies done thus far are observational in nature, with their inherent biases. Further research, preferably randomized controlled trials are recommended to answer this pertinent question

Comparative accuracy of 1,3 beta-D glucan and galactomannan for diagnosis of invasive fungal infections in pediatric patients: a systematic review with meta-analysis

Invasive fungal infections (IFI) cause considerable morbidity and mortality in pediatric patients. Serum biomarkers such as 1,3-beta-D glucan (BDG) and galactomannan (GM) have been evaluated for the IFI diagnosis. However, most evidence regarding their utility is derived from studies in adult oncology patients. This systematic review aimed to compare the diagnostic accuracy of BDG and GM individually or in combination for diagnosing IFI in pediatric patients. PubMed, CINAHL, Embase, and Cochrane Library were searched until March 2019 for diagnostic studies evaluating both serum GM and BDG for diagnosing pediatric IFI. The pooled diagnostic odds ratio (DOR), specificity and sensitivity were computed. Receiver operating characteristics (ROC) curve and area under the curve (AUC) were used for summarizing overall assay performance. Six studies were included in the meta-analysis. The summary estimates of sensitivity, specificity, pooled DOR, AUC of the GM assay for proven or probable IFI were 0.74, 0.76, 13.25, and 0.845. The summary estimates of sensitivity, specificity, pooled DOR, AUC of the BDG assay were 0.70, 0.69, 4.3, and 0.722. The combined predictive ability of both tests was reported in two studies (sensitivity: 0.67, specificity: 0.877). Four studies were performed in hematology-oncology patients, while two were retrospective studies from pediatric intensive care units (ICUs). In the subgroup of hematology-oncology patients, DOR of BDG remained similar at 4.25 but increased to 40.28 for GM. We conclude that GM and BDG have a modest performance for identifying IFI in pediatric patients. GM has a better accuracy over BDG. Combining both improves the specificity at the cost of sensitivity