Classification of established atopic dermatitis in children with the in vivo imaging methods

Automated Prediction of Osteoarthritis Level in Human Osteochondral Tissue Using Histopathological Images

Osteoarthritis (OA) is the most common arthritis and the leading cause of lower extremity disability in older adults. Understanding OA progression is important in the development of patient-specific therapeutic techniques at the early stage of OA rather than at the end stage. Histopathology scoring systems are usually used to evaluate OA progress and the mechanisms involved in the development of OA. This study aims to classify the histopathological images of cartilage specimens automatically, using artificial intelligence algorithms. Hematoxylin and eosin (HE)- and safranin O and fast green (SafO)-stained images of human cartilage specimens were divided into early, mild, moderate, and severe OA. Five pre-trained convolutional networks (DarkNet-19, MobileNet, ResNet-101, NasNet) were utilized to extract the twenty features from the last fully connected layers for both scenarios of SafO and HE. Principal component analysis (PCA) and ant lion optimization (ALO) were utilized to obtain the best-weighted features. The support vector machine classifier was trained and tested based on the selected descriptors to achieve the highest accuracies of 98.04% and 97.03% in HE and SafO, respectively. Using the ALO algorithm, the F1 scores were 0.97, 0.991, 1, and 1 for the HE images and 1, 0.991, 0.97, and 1 for the SafO images for the early, mild, moderate, and severe classes, respectively. This algorithm may be a useful tool for researchers to evaluate the histopathological images of OA without the need for experts in histopathology scoring systems or the need to train new experts. Incorporating automated deep features could help to improve the characterization and understanding of OA progression and development.

Investigating the optical clearing effects of 50% glycerol in ex vivo human skin by harmonic generation microscopy

Imaging depth and quality of optical microscopy can be enhanced by optical clearing. Here we investigate the optical clearing of the ex vivo human skin by 50% glycerol topical application, which is allowed for cosmetic usage. Harmonic generation microscopy, by combining second and third harmonic generation (THG) modalities, was utilized to examine the clearing effect. The THG image intensity is sensitive to the improved optical homogeneity after optical clearing, and the second harmonic generation (SHG) image intensity in the dermis could serve as a beacon to confirm the reduction of the scattering in the epidermis layer. As a result, our study supports the OC effect through 50% glycerol topical application. Our study further indicates the critical role of stratum corneum shrinkage for the observed SHG and THG signal recovery.

Slide-free clinical imaging of melanin with absolute quantities using label-free third-harmonic-generation enhancement-ratio microscopy

The capability to image the 3D distribution of melanin in human skin in vivo with absolute quantities and microscopic details will not only enable noninvasive histopathological diagnosis of melanin-related cutaneous disorders, but also make long term treatment assessment possible. In this paper, we demonstrate clinical in vivo imaging of the melanin distribution in human skin with absolute quantities on mass density and with microscopic details by using label-free third-harmonic-generation (THG) enhancement-ratio microscopy. As the dominant absorber in skin, melanin provides the strongest THG nonlinearity in human skin due to resonance enhancement. We show that the THG-enhancement-ratio (erTHG) parameter can be calibrated in vivo and can indicate the melanin mass density. With an unprecedented clinical imaging resolution, our study revealed erTHG-microscopy's unique capability for long-term treatment assessment and direct clinical observation of melanin's micro-distribution to shed light into the unknown pathway and regulation mechanism of melanosome transfer and translocation.