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Image classification with convolutional neural networks (CNN) offers an unprecedented opportunity to medical imaging. Regulatory agencies in the USA and Europe have already cleared numerous deep learning/machine learning based medical devices and algorithms. While the field of radiology is on the forefront of artificial intelligence (AI) revolution, conventional pathology, which commonly relies on examination of tissue samples on a glass slide, is falling behind in leveraging this technology. On the other hand, ex vivo confocal laser scanning microscopy (ex vivo CLSM), owing to its digital workflow features, has a high potential to benefit from integrating AI tools into the assessment and decision-making process. Aim of this work was to explore a preliminary application of CNN in digitally stained ex vivo CLSM images of cutaneous squamous cell carcinoma (cSCC) for automated detection of tumor tissue. Thirty-four freshly excised tissue samples were prospectively collected and examined immediately after resection. After the histologically confirmed ex vivo CLSM diagnosis, the tumor tissue was annotated for segmentation by experts, in order to train the MobileNet CNN. The model was then trained and evaluated using cross validation. The overall sensitivity and specificity of the deep neural network for detecting cSCC and tumor free areas on ex vivo CLSM slides compared to expert evaluation were 0.76 and 0.91, respectively. The area under the ROC curve was equal to 0.90 and the area under the precision-recall curve was 0.85. The results demonstrate a high potential of deep learning models to detect cSCC regions on digitally stained ex vivo CLSM slides and to distinguish them from tumor-free skin.

Until now, the clinical differentiation between a nevus and a melanoma is still challenging in some cases. Line-field confocal optical coherence tomography (LC-OCT) is a new tool with the aim to change that. The aim of the study was to evaluate LC-OCT for the discrimination between nevi and melanomas. A total of 84 melanocytic lesions were examined with LC-OCT and 36 were also imaged with RCM. The observers recorded the diagnoses, and the presence or absence of the 18 most common imaging parameters for melanocytic lesions, nevi, and melanomas in the LC-OCT images. Their confidence in diagnosis and the image quality of LC-OCT and RCM were evaluated. The most useful criteria, the sensitivity and specificity of LC-OCT vs. RCM vs. histology, to differentiate a (dysplastic) nevus from a melanoma were analyzed. Good image quality correlated with better diagnostic performance (Spearman correlation: 0.4). LC-OCT had a 93% sensitivity and 100% specificity compared to RCM (93% sensitivity, 95% specificity) for diagnosing a melanoma (vs. all types of nevi). No difference in performance between RCM and LC-OCT was observed (McNemar’s p value = 1). Both devices falsely diagnosed dysplastic nevi as non-dysplastic (43% sensitivity for dysplastic nevus diagnosis). The most significant criteria for diagnosing a melanoma with LC-OCT were irregular honeycombed patterns (92% occurrence rate; 31.7 odds ratio (OR)), the presence of pagetoid spread (89% occurrence rate; 23.6 OR) and the absence of dermal nests (23% occurrence rate, 0.02 OR). In conclusion LC-OCT is useful for the discrimination between melanomas and nevi.

To assess the effect of applying a protocol for image selection and the number of images required for adequate quantification of corneal nerve pathology using in vivo corneal confocal microscopy (IVCCM). IVCCM was performed in 35 participants by a single examiner. For each participant, 4 observers used a standardized protocol to select 6 central corneal nerve images to assess the inter-observer variability. Furthermore, images were selected by a single observer on two occasions to assess intra-observer variability and the effect of sample size was assessed by comparing 6 with 12 images. Corneal nerve fiber density (CNFD), branch density (CNBD) and length (CNFL) were quantified using fully automated software. The data were compared using the intra class correlation coefficient (ICC) and Bland-Altman agreement plots for all experiments. The ICC values for CNFD, CNBD and CNFL were 0.93 (P<0.0001), 0.96 (P<0.0001) and 0.95 (P<0.0001) for inter-observer variability and 0.95 (P<0.0001), 0.97 (P<0.001) and 0.97 (P<0.0001) for intra-observer variability. For sample size variability, ICC values were 0.94 (P<0.0001), 0.95 (P<0.0001), and 0.96 (P<0.0001) for CNFD, CNBD and CNFL. Bland-Altman plots showed excellent agreement for all parameters. This study shows that implementing a standardized protocol to select IVCCM images results in high intra and inter-observer reproducibility for all corneal nerve parameters and 6 images are adequate for analysis. IVCCM could therefore be deployed in large multicenter clinical trials with confidence.

Cutaneous malignancies are common malignancies worldwide, with rising incidence. Most skin cancers, including melanoma, can be cured if diagnosed correctly at an early stage. Thus, millions of biopsies are performed annually, posing a major economic burden. Non-invasive skin imaging techniques can aid in early diagnosis and save unnecessary benign biopsies. In this review article, we will discuss in vivo and ex vivo confocal microscopy (CM) techniques that are currently being utilized in dermatology clinics for skin cancer diagnosis. We will discuss their current applications and clinical impact. Additionally, we will provide a comprehensive review of the advances in the field of CM, including multi-modal approaches, the integration of fluorescent targeted dyes, and the role of artificial intelligence for improved diagnosis and management.

Background Vivascope 2500 ex vivo confocal microscopy (EVCM) is an emerging optical imaging device that allows nuclear level resolution of freshly excised tissues. EVCM provides, rapid real‐time pathological examination in many subspecialties of pathology including skin, prostate, breast, liver, etc. In contrast to traditional time‐consuming frozen sectioning and histological analysis. Aims To evaluate the current state of EVCM utilization. Materials and Methods This study highlights the advantages, limitations, and prospects of EVCM in skin pathology. Results Our findings demonstrate that EVCM is a promising adjunctive tool to assess margins in Mohs surgery and to provide rapid, accurate diagnosis of cutaneous tumors, infectious and inflammatory diseases. Conclusion EVCM is a revolutionary device that can be used as an adjunct to paraffin‐fixed, hematoxylin and eosin‐stained slides and frozen sectioning. Additional refinements are required before EVCM can be used as an alternative to frozen sectioning or traditional tissue processing.

Confocal microscopy 1 remains a major workhorse in biomedical optical microscopy owing to its reliability and flexibility in imaging various samples, but suffers from substantial point spread function anisotropy, diffraction-limited resolution, depth-dependent degradation in scattering samples and volumetric bleaching 2 . Here we address these problems, enhancing confocal microscopy performance from the sub-micrometre to millimetre spatial scale and the millisecond to hour temporal scale, improving both lateral and axial resolution more than twofold while simultaneously reducing phototoxicity. We achieve these gains using an integrated, four-pronged approach: (1) developing compact line scanners that enable sensitive, rapid, diffraction-limited imaging over large areas; (2) combining line-scanning with multiview imaging, developing reconstruction algorithms that improve resolution isotropy and recover signal otherwise lost to scattering; (3) adapting techniques from structured illumination microscopy, achieving super-resolution imaging in densely labelled, thick samples; (4) synergizing deep learning with these advances, further improving imaging speed, resolution and duration. We demonstrate these capabilities on more than 20 distinct fixed and live samples, including protein distributions in single cells; nuclei and developing neurons in Caenorhabditis elegans embryos, larvae and adults; myoblasts in imaginal disks of Drosophila wings; and mouse renal, oesophageal, cardiac and brain tissues. A combination of multiview imaging, structured illumination, reconstruction algorithms and deep-learning predictions realizes spatial- and temporal-resolution improvements in fluorescence microscopy to produce super-resolution images from diffraction-limited input images.

Aims/hypothesisCorneal confocal microscopy is a rapid non-invasive ophthalmic imaging technique that identifies peripheral and central neurodegenerative disease. Quantification of corneal sub-basal nerve plexus morphology, however, requires either time-consuming manual annotation or a less-sensitive automated image analysis approach. We aimed to develop and validate an artificial intelligence-based, deep learning algorithm for the quantification of nerve fibre properties relevant to the diagnosis of diabetic neuropathy and to compare it with a validated automated analysis program, ACCMetrics.MethodsOur deep learning algorithm, which employs a convolutional neural network with data augmentation, was developed for the automated quantification of the corneal sub-basal nerve plexus for the diagnosis of diabetic neuropathy. The algorithm was trained using a high-end graphics processor unit on 1698 corneal confocal microscopy images; for external validation, it was further tested on 2137 images. The algorithm was developed to identify total nerve fibre length, branch points, tail points, number and length of nerve segments, and fractal numbers. Sensitivity analyses were undertaken to determine the AUC for ACCMetrics and our algorithm for the diagnosis of diabetic neuropathy.ResultsThe intraclass correlation coefficients for our algorithm were superior to those for ACCMetrics for total corneal nerve fibre length (0.933 vs 0.825), mean length per segment (0.656 vs 0.325), number of branch points (0.891 vs 0.570), number of tail points (0.623 vs 0.257), number of nerve segments (0.878 vs 0.504) and fractals (0.927 vs 0.758). In addition, our proposed algorithm achieved an AUC of 0.83, specificity of 0.87 and sensitivity of 0.68 for the classification of participants without (n = 90) and with (n = 132) neuropathy (defined by the Toronto criteria).Conclusions/interpretationThese results demonstrated that our deep learning algorithm provides rapid and excellent localisation performance for the quantification of corneal nerve biomarkers. This model has potential for adoption into clinical screening programmes for diabetic neuropathy.Data availabilityThe publicly shared cornea nerve dataset (dataset 1) is available at http://bioimlab.dei.unipd.it/Corneal%20Nerve%20Tortuosity%20Data%20Set.htm and http://bioimlab.dei.unipd.it/Corneal%20Nerve%20Data%20Set.htm.

Lipid nanoparticles (LNPs) or liposomes are the most widely used drug carriers for nanomedicines. The size of LNPs is one of the essential factors affecting drug delivery efficiency and therapeutic efficiency. Here, we demonstrated the effect of lipid concentration and mixing performance on the LNP size using microfluidic devices with the aim of understanding the LNP formation mechanism and controlling the LNP size precisely. We fabricated microfluidic devices with different depths, 11 μm and 31 μm, of their chaotic micromixer structures. According to the LNP formation behavior results, by using a low concentration of the lipid solution and the microfluidic device equipped with the 31 μm chaotic mixer structures, we were able to produce the smallest-sized LNPs yet with a narrow particle size distribution. We also evaluated the mixing rate of the microfluidic devices using a laser scanning confocal microscopy and we estimated the critical ethanol concentration for controlling the LNP size. The critical ethanol concentration range was estimated to be 60-80% ethanol. Ten nanometer-sized tuning of LNPs was achieved for the optimum residence time at the critical concentration using the microfluidic devices with chaotic mixer structures. The residence times at the critical concentration necessary to control the LNP size were 10, 15-25, and 50 ms time-scales for 30, 40, and 50 nm-sized LNPs, respectively. Finally, we proposed the LNP formation mechanism based on the determined LNP formation behavior and the critical ethanol concentration. The precise size-controlled LNPs produced by the microfluidic devices are expected to become carriers for next generation nanomedicines and they will lead to new and effective approaches for cancer treatment.

Darkfield and confocal laser scanning microscopy both allow for a simultaneous observation of live cells and single nanoparticles. Accordingly, a characterization of nanoparticle uptake and intracellular mobility appears possible within living cells. Single particle tracking allows to measure the size of a diffusing particle close to a cell. However, within the more complex system of a cell's cytoplasm normal, confined or anomalous diffusion together with directed motion may occur. In this work we present a method to automatically classify and segment single trajectories into their respective motion types. Single trajectories were found to contain more than one motion type. We have trained a random forest with 9 different features. The average error over all motion types for synthetic trajectories was 7.2%. The software was successfully applied to trajectories of positive controls for normal- and constrained diffusion. Trajectories captured by nanoparticle tracking analysis served as positive control for normal diffusion. Nanoparticles inserted into a diblock copolymer membrane was used to generate constrained diffusion. Finally we segmented trajectories of diffusing (nano-)particles in V79 cells captured with both darkfield- and confocal laser scanning microscopy. The software called \"TraJClassifier\" is freely available as ImageJ/Fiji plugin via https://git.io/v6uz2.

Aims/hypothesisSmall cohort studies raise the hypothesis that corneal nerve abnormalities (including corneal nerve fibre length [CNFL]) are valid non-invasive imaging endpoints for diabetic sensorimotor polyneuropathy (DSP). We aimed to establish concurrent validity and diagnostic thresholds in a large cohort of participants with and without DSP.MethodsNine hundred and ninety-eight participants from five centres (516 with type 1 diabetes and 482 with type 2 diabetes) underwent CNFL quantification and clinical and electrophysiological examination. AUC and diagnostic thresholds were derived and validated in randomly selected samples using receiver operating characteristic analysis. Sensitivity analyses included latent class models to address the issue of imperfect reference standard.ResultsType 1 and type 2 diabetes subcohorts had mean age of 42 ± 19 and 62 ± 10 years, diabetes duration 21 ± 15 and 12 ± 9 years and DSP prevalence of 31% and 53%, respectively. Derivation AUC for CNFL was 0.77 in type 1 diabetes (p < 0.001) and 0.68 in type 2 diabetes (p < 0.001) and was approximately reproduced in validation sets. The optimal threshold for automated CNFL was 12.5 mm/mm2 in type 1 diabetes and 12.3 mm/mm2 in type 2 diabetes. In the total cohort, a lower threshold value below 8.6 mm/mm2 to rule in DSP and an upper value of 15.3 mm/mm2 to rule out DSP were associated with 88% specificity and 88% sensitivity.Conclusions/interpretationWe established the diagnostic validity and common diagnostic thresholds for CNFL in type 1 and type 2 diabetes. Further research must determine to what extent CNFL can be deployed in clinical practice and in clinical trials assessing the efficacy of disease-modifying therapies for DSP.