Deep Learning-Based Evaluation of Ultrasound Images for Benign Skin Tumors.

In this study, a combined convolutional neural network for the diagnosis of three benign skin tumors was designed, and its effectiveness was verified through quantitative and statistical analysis. To this end, 698 sonographic images were taken and diagnosed at the Department of Dermatology at Severance Hospital in Seoul, Korea, between 10 November 2017 and 17 January 2020. Through an empirical process, a convolutional neural network combining two structures, which consist of a residual structure and an attention-gated structure, was designed. Five-fold cross-validation was applied, and the train set for each fold was augmented by the Fast AutoAugment technique. As a result of training, for three benign skin tumors, an average accuracy of 95.87%, an average sensitivity of 90.10%, and an average specificity of 96.23% were derived. Also, through statistical analysis using a class activation map and physicians' findings, it was found that the judgment criteria of physicians and the trained combined convolutional neural network were similar. This study suggests that the model designed and trained in this study can be a diagnostic aid to assist physicians and enable more efficient and accurate diagnoses.

DEEP LEARNING-BASED PREDICTION OF OUTCOMES FOLLOWING NONCOMPLICATED EPIRETINAL MEMBRANE SURGERY.

PURPOSE: We used deep learning to predict the final central foveal thickness (CFT), changes in CFT, final best corrected visual acuity, and best corrected visual acuity changes following noncomplicated idiopathic epiretinal membrane surgery. METHODS: Data of patients who underwent noncomplicated epiretinal membrane surgery at Severance Hospital from January 1, 2010, to December 31, 2018, were reviewed. Patient age, sex, hypertension and diabetes statuses, and preoperative optical coherence tomography scans were noted. For image analysis and model development, a pre-trained VGG16 was adopted. The mean absolute error and coefficient of determination (R 2 ) were used to evaluate the model performances. The study involved 688 eyes of 657 patients. RESULTS: For final CFT, the mean absolute error was the lowest in the model that considered only clinical and demographic characteristics; the highest accuracy was achieved by the model that considered all clinical and surgical information. For CFT changes, models utilizing clinical and surgical information showed the best performance. However, our best model failed to predict the final best corrected visual acuity and best corrected visual acuity changes. CONCLUSION: A deep learning model predicted the final CFT and CFT changes in patients 1 year after epiretinal membrane surgery. Central foveal thickness prediction showed the best results when demographic factors, comorbid diseases, and surgical techniques were considered.

Deep-Learning-Based Automatic Detection and Segmentation of Brain Metastases with Small Volume for Stereotactic Ablative Radiotherapy.

Recently, several efforts have been made to develop the deep learning (DL) algorithms for automatic detection and segmentation of brain metastases (BM). In this study, we developed an advanced DL model to BM detection and segmentation, especially for small-volume BM. From the institutional cancer registry, contrast-enhanced magnetic resonance images of 65 patients and 603 BM were collected to train and evaluate our DL model. Of the 65 patients, 12 patients with 58 BM were assigned to test-set for performance evaluation. Ground-truth for BM was assigned to one radiation oncologist to manually delineate BM and another one to cross-check. Unlike other previous studies, our study dealt with relatively small BM, so the area occupied by the BM in the high-resolution images were small. Our study applied training techniques such as the overlapping patch technique and 2.5-dimensional (2.5D) training to the well-known U-Net architecture to learn better in smaller BM. As a DL architecture, 2D U-Net was utilized by 2.5D training. For better efficacy and accuracy of a two-dimensional U-Net, we applied effective preprocessing include 2.5D overlapping patch technique. The sensitivity and average false positive rate were measured as detection performance, and their values were 97% and 1.25 per patient, respectively. The dice coefficient with dilation and 95% Hausdorff distance were measured as segmentation performance, and their values were 75% and 2.057 mm, respectively. Our DL model can detect and segment BM with small volume with good performance. Our model provides considerable benefit for clinicians with automatic detection and segmentation of BM for stereotactic ablative radiotherapy.

Evaluation of Atopic Dermatitis Improvement Caused by Low-Level, Low-Frequency Pulsed Electromagnetic Fields.

This study aimed to evaluate the effectiveness of using low-level, low-frequency pulsed electromagnetic field (LLLF_PEMF) stimulation to improve atopic dermatitis induced by 2,4-dinitrochlorobenzene (DNCB). Twenty 6-week-old hairless mice were randomly divided into Normal (n = 5), PEMF 15 Hz (n = 5), PEMF 75 Hz (n = 5), and Sham (n = 5) groups. Following the onset of atopic dermatitis symptoms, PEMF groups (15 and 75 Hz) were stimulated with LLLF_PEMF (15 mT) for 8 h per day for 1 week. Sensory evaluation analysis revealed a significant difference between the PEMF 15 Hz group and Sham group (P < 0.05), but these differences were not visually obvious. While both the PEMF and Sham groups had atopic dermatitis lesions, lesion size was significantly smaller in the two PEMF groups than in the Sham group (P < 0.001). Additionally, changes in epithelial thickness because of skin inflammation significantly decreased for both PEMF groups, compared with the Sham group (P < 0.001). In conclusion, these results suggest that PEMF stimulation in vivo triggers electro-chemical reactions that affect immune response. © 2022 Bioelectromagnetics Society.

A Novel Method for Effective Cell Segmentation and Tracking in Phase Contrast Microscopic Images.

Cell migration plays an important role in the identification of various diseases and physiological phenomena in living organisms, such as cancer metastasis, nerve development, immune function, wound healing, and embryo formulation and development. The study of cell migration with a real-time microscope generally takes several hours and involves analysis of the movement characteristics by tracking the positions of cells at each time interval in the images of the observed cells. Morphological analysis considers the shapes of the cells, and a phase contrast microscope is used to observe the shape clearly. Therefore, we developed a segmentation and tracking method to perform a kinetic analysis by considering the morphological transformation of cells. The main features of the algorithm are noise reduction using a block-matching 3D filtering method, k-means clustering to mitigate the halo signal that interferes with cell segmentation, and the detection of cell boundaries via active contours, which is an excellent way to detect boundaries. The reliability of the algorithm developed in this study was verified using a comparison with the manual tracking results. In addition, the segmentation results were compared to our method with unsupervised state-of-the-art methods to verify the proposed segmentation process. As a result of the study, the proposed method had a lower error of less than 40% compared to the conventional active contour method.

Deep learning models for the prediction of intraoperative hypotension.

BACKGROUND: Intraoperative hypotension is associated with a risk of postoperative organ dysfunction. In this study, we aimed to present deep learning algorithms for real-time predictions 5, 10, and 15 min before a hypotensive event. METHODS: In this retrospective observational study, deep learning algorithms were developed and validated using biosignal waveforms acquired from patient monitoring of noncardiac surgery. The classification model was a binary classifier of a hypotensive event (MAP <65 mm Hg) or a non-hypotensive event by analysing biosignal waveforms. The regression model was developed to directly estimate the MAP. The primary outcome was area under the receiver operating characteristic (AUROC) curve and the mean absolute error (MAE). RESULTS: In total, 3301 patients were included. For invasive models, the multichannel model with an arterial pressure waveform, electrocardiography, photoplethysmography, and capnography showed greater AUROC than the arterial-pressure-only models (AUROC15-min, 0.897 [95% confidence interval {CI}: 0.894-0.900] vs 0.891 [95% CI: 0.888-0.894]) and lesser MAE (MAE15-min, 7.76 mm Hg [95% CI: 7.64-7.87 mm Hg] vs 8.12 mm Hg [95% CI: 8.02-8.21 mm Hg]). For the noninvasive models, the multichannel model showed greater AUROCs than that of the photoplethysmography-only models (AUROC15-min, 0.762 [95% CI: 0.756-0.767] vs 0.694 [95% CI: 0.686-0.702]) and lesser MAEs (MAE15-min, 11.68 mm Hg [95% CI: 11.57-11.80 mm Hg] vs 12.67 [95% CI: 12.56-12.79 mm Hg]). CONCLUSIONS: Deep learning models can predict hypotensive events based on biosignals acquired using invasive and noninvasive patient monitoring. In addition, the model shows better performance when using combined rather than single signals.

Artificial Intelligence in Cutaneous Oncology.

Skin cancer, previously known to be a common disease in Western countries, is becoming more common in Asian countries. Skin cancer differs from other carcinomas in that it is visible to our eyes. Although skin biopsy is essential for the diagnosis of skin cancer, decisions regarding whether or not to conduct a biopsy are made by an experienced dermatologist. From this perspective, it is easy to obtain and store photos using a smartphone, and artificial intelligence technologies developed to analyze these photos can represent a useful tool to complement the dermatologist's knowledge. In addition, the universal use of dermoscopy, which allows for non-invasive inspection of the upper dermal level of skin lesions with a usual 10-fold magnification, adds to the image storage and analysis techniques, foreshadowing breakthroughs in skin cancer diagnosis. Current problems include the inaccuracy of the available technology and resulting legal liabilities. This paper presents a comprehensive review of the clinical applications of artificial intelligence and a discussion on how it can be implemented in the field of cutaneous oncology.

Variable Membrane Dielectric Polarization Characteristic in Individual Live Cells.

Investigation of the dielectric properties of cell membranes plays an important role in understanding the biological activities that sustain cellular life and realize cellular functionalities. Herein, the variable dielectric polarization characteristics of cell membranes are reported. In controlling the dielectric polarization of a cell using dielectrophoresis force spectroscopy, different cellular crossover frequencies were observed by modulating both the direction and sweep rate of the frequency. The crossover frequencies were used for the extraction of the variable capacitance, which is involved in the dielectric polarization across the cell membranes. In addition, this variable phenomenon was investigated by examining cells whose membranes were cholesterol-depleted with methyl-ß-cyclodextrin, which verified a strong correlation between the variable dielectric polarization characteristics and membrane composition changes. This study presented the dielectric polarization properties in live cells' membranes that can be modified by the regulation of external stimuli and provided a powerful platform to explore cellular membrane dielectric polarization.

A novel automatic segmentation and tracking method to measure cellular dielectrophoretic mobility from individual cell trajectories for high throughput assay.

BACKGROUND AND OBJECTIVE: The dielectrophoresis (DEP) technique is increasingly being recognised as a potentially valuable tool for non-contact manipulation of numerous cells as well as for biological single cell analysis with non-invasive characterisation of a cell's electrical properties. Several studies have attempted to track multiple cells to characterise their cellular DEP mobility. However, they encountered difficulties in simultaneously tracking the movement of a large number of individual cells in a bright-field image sequence because of interference from the background electrode pattern. Consequently, this present study aims to develop an automatic system for imaging-based characterisation of cellular DEP mobility, which enables the simultaneous tracking of several hundred of cells inside a microfluidic device. METHODS: The proposed method for segmentation and tracking of cells consists of two main stages: pre-processing and particle centre localisation. In the pre-processing stage, background subtraction and contrast enhancement were performed to distinguish the cell region from the background image. In the particle centre localisation stage, the unmarked cell was automatically detected via graph-cut algorithm-based K-means clustering. RESULTS: Our algorithm enabled segmentation and tracking of numerous Michigan Cancer Foundation-7 (MCF-7) cell trajectories while the DEP force was oscillated between positive and negative. The cell tracking accuracy and cell count capability was at least 90% of the total number of cells with the newly developed algorithm. In addition, the cross-over frequency was measured by analysing the segmented and tracked trajectory data of the cellular movements caused by the positive and negative DEP force. The measured cross-over frequency was compared with previous results. The multi-cellular movements investigation based on the measured cross-over frequency was repeated until the viability of cells was unchanged in the same environment as in a microfluidic device. The results were statistically consistent, indicating that the developed algorithm was reliable for the investigation of DEP cellular mobility. CONCLUSION: This study developed a powerful platform to simultaneously measure the DEP-induced trajectories of numerous cells, and to investigate in a robust, efficient, and accurate manner the DEP properties at both the single cell and cell ensemble level.

Automated stitching of microscope images of fluorescence in cells with minimal overlap.

The morphology of tumor cells is highly related to their phenotype and activity. To verify the drug response of a brain tumor patient, fluorescence microscope images of drug-treated patient-derived cells in each well are analyzed. Due to the limitation of the field of view (FOV), a large number of small FOVs are acquired to compose one complete microscope well. Here, we propose an automated method for accurately stitching tile-scanned fluorescence microscope images, even with noise and a narrow overlapping region between adjacent fields. The proposed method is based on intensity-based normalized cross-correlation (NCC) and a triangular method-based threshold. The proposed method's quantitative accuracy and the sensitivity of the input was compared to other existing stitching tools, MIST and FijiIS, setting manually stitched images as the ground truth. The test images were 20 samples of 3 × 3 grid images in three versions of the fluorescence channel. The distance between the location of each field and number of cells was determined for different input field overlap ranges (1%, 3%, 5%, and 10%), while the actual value was about 1.15%. The proposed method had a distance error of 1.5 pixels at an input overlap of 1%, showing the lowest minimum error at all channels. Regarding the difference in cell numbers, although the number of overlapping cells was always small because of the narrow overlapping range, the proposed method was able to generate the resultant image with the smallest difference. In addition, to confirm the size limitation of the proposed algorithm, the accuracy of stitching images of grid structures 3 × 3, 5 × 5, 10 × 10-20 × 20 was tested, showing consistent results. In conclusion, quantitative evaluation of the performance of the method proved its improved accuracy compared to other current state-of-art techniques, and it showed robust performance even with noise and a narrow overlapping region between adjacent fields.

Comparison between reflectance confocal microscopy and 2-photon microscopy in early detection of cutaneous radiation injury in a mouse model in vivo.

Cutaneous radiation injury (CRI) is a skin injury caused by high-dose exposure of ionizing radiation (IR). For proper treatment, early detection of CRI before clinical symptoms is important. Optical microscopic techniques such as reflectance confocal microscopy (RCM) and 2-photon microscopy (TPM) have been tested as the early diagnosis method by detecting cellular changes. In this study, RCM and TPM were compared in the detection of cellular changes caused by CRI in an in vivo mouse model. CRI was induced on the mouse hindlimb skin with various IR doses and the injured skin regions were imaged longitudinally by both modalities until the onset of clinical symptoms. Both RCM and TPM detected the changes of epidermal cells and sebaceous glands before clinical symptoms in different optical contrasts. RCM detected changes of cell morphology and scattering property based on light reflection. TPM detected detail changes of cellular structures based on autofluorescence of cells. Since both RCM and TPM were sensitive to the early stage CRI by using different contrasts, the optimal method for clinical CRI diagnosis could be either individual methods or their combination.

Correction: Acral melanoma detection using a convolutional neural network for dermoscopy images.

[This corrects the article DOI: 10.1371/journal.pone.0193321.].

Acral melanoma detection using a convolutional neural network for dermoscopy images.

BACKGROUND/PURPOSE: Acral melanoma is the most common type of melanoma in Asians, and usually results in a poor prognosis due to late diagnosis. We applied a convolutional neural network to dermoscopy images of acral melanoma and benign nevi on the hands and feet and evaluated its usefulness for the early diagnosis of these conditions. METHODS: A total of 724 dermoscopy images comprising acral melanoma (350 images from 81 patients) and benign nevi (374 images from 194 patients), and confirmed by histopathological examination, were analyzed in this study. To perform the 2-fold cross validation, we split them into two mutually exclusive subsets: half of the total image dataset was selected for training and the rest for testing, and we calculated the accuracy of diagnosis comparing it with the dermatologist's and non-expert's evaluation. RESULTS: The accuracy (percentage of true positive and true negative from all images) of the convolutional neural network was 83.51% and 80.23%, which was higher than the non-expert's evaluation (67.84%, 62.71%) and close to that of the expert (81.08%, 81.64%). Moreover, the convolutional neural network showed area-under-the-curve values like 0.8, 0.84 and Youden's index like 0.6795, 0.6073, which were similar score with the expert. CONCLUSION: Although further data analysis is necessary to improve their accuracy, convolutional neural networks would be helpful to detect acral melanoma from dermoscopy images of the hands and feet.

IgG Sensitization to Extracellular Vesicles in Indoor Dust Is Closely Associated With the Prevalence of Non-Eosinophilic Asthma, COPD, and Lung Cancer.

PURPOSE: Recent experimental evidence shows that extracellular vesicles (EVs) in indoor dust induce neurtrophilic pulmonary inflammation, which is a characteristic pathology in patients with severe asthma and chronic obstructive pulmonary disease (COPD). In addition, COPD is known to be an important risk factor for lung cancer, irrespective of cigarette smoking. Here, we evaluated whether sensitization to indoor dust EVs is a risk for the development of asthma, COPD, or lung cancer. METHODS: Serum IgG antibodies against dust EVs were measured in 90 healthy control subjects, 294 asthmatics, 242 COPD patients, and 325 lung cancer patients. Serum anti-dust EV IgG titers were considered high if they exceeded a 95 percentile value of the control subjects. Age-, gender-, and cigarette smoke-adjusted multiple logistic regression analyses were performed to determine odds ratios (ORs) for asthma, COPD, and lung cancer patients vs the control subjects. RESULTS: In total, 4.4%, 13.6%, 29.3%, and 54.9% of the control, asthma, COPD, and lung cancer groups, respectively, had high serum anti-dust EV IgG titers. Adjusted multiple logistic regression revealed that sensitization to dust EVs (high serum anti-dust EV IgG titer) was an independent risk factor for asthma (adjusted OR, 3.3; 95% confidence interval [CI], 1.1-10.0), COPD (adjusted OR, 8.0; 95% CI, 2.0-32.5) and lung cancer (adjusted OR, 38.7; 95% CI, 10.4-144.3). CONCLUSIONS: IgG sensitization to indoor dust EVs appears to be a major risk for the development of asthma, COPD, and lung cancer.