An ensemble-based deep learning model for detection of mutation causing cutaneous melanoma.

When the mutation affects the melanocytes of the body, a condition called melanoma results which is one of the deadliest skin cancers. Early detection of cutaneous melanoma is vital for raising the chances of survival. Melanoma can be due to inherited defective genes or due to environmental factors such as excessive sun exposure. The accuracy of the state-of-the-art computer-aided diagnosis systems is unsatisfactory. Moreover, the major drawback of medical imaging is the shortage of labeled data. Generalized classifiers are required to diagnose melanoma to avoid overfitting the dataset. To address these issues, blending ensemble-based deep learning (BEDLM-CMS) model is proposed to detect mutation of cutaneous melanoma by integrating long short-term memory (LSTM), Bi-directional LSTM (BLSTM) and gated recurrent unit (GRU) architectures. The dataset used in the proposed study contains 2608 human samples and 6778 mutations in total along with 75 types of genes. The most prominent genes that function as biomarkers for early diagnosis and prognosis are utilized. Multiple extraction techniques are used in this study to extract the most-prominent features. Afterwards, we applied different DL models optimized through grid search technique to diagnose melanoma. The validity of the results is confirmed using several techniques, including tenfold cross validation (10-FCVT), independent set (IST), and self-consistency (SCT). For validation of the results multiple metrics are used which include accuracy, specificity, sensitivity, and Matthews's correlation coefficient. BEDLM gives the highest accuracy of 97% in the independent set test whereas in self-consistency test and tenfold cross validation test it gives 94% and 93% accuracy, respectively. Accuracy of in self-consistency test, independent set test, and tenfold cross validation test is LSTM (96%, 94%, 92%), GRU (93%, 94%, 91%), and BLSTM (99%, 98%, 93%), respectively. The findings demonstrate that the proposed BEDLM-CMS can be used effectively applied for early diagnosis and treatment efficacy evaluation of cutaneous melanoma.

A survey on deep learning for skin lesion segmentation.

Skin cancer is a major public health problem that could benefit from computer-aided diagnosis to reduce the burden of this common disease. Skin lesion segmentation from images is an important step toward achieving this goal. However, the presence of natural and artificial artifacts (e.g., hair and air bubbles), intrinsic factors (e.g., lesion shape and contrast), and variations in image acquisition conditions make skin lesion segmentation a challenging task. Recently, various researchers have explored the applicability of deep learning models to skin lesion segmentation. In this survey, we cross-examine 177 research papers that deal with deep learning-based segmentation of skin lesions. We analyze these works along several dimensions, including input data (datasets, preprocessing, and synthetic data generation), model design (architecture, modules, and losses), and evaluation aspects (data annotation requirements and segmentation performance). We discuss these dimensions both from the viewpoint of select seminal works, and from a systematic viewpoint, examining how those choices have influenced current trends, and how their limitations should be addressed. To facilitate comparisons, we summarize all examined works in a comprehensive table as well as an interactive table available online3.

Evaluation of antithrombotic prophylaxis and thrombotic events in children with COVID-19 or MIS-C: A tertiary pediatric center experience.

OBJECTIVES: COVID-19 and multisystem inflammatory syndrome in children (MIS-C) are associated with a risk of hypercoagulability and thrombotic events. We aimed (a) to evaluate the demographic, clinical, and laboratory findings as well as the incidence of thrombotic events of COVID-19 and MIS-C in children and (b) to determine the role of antithrombotic prophylaxis. METHODS: A single-center retrospective study evaluated hospitalized children with COVID-19 or MIS-C. RESULTS: The study group consisted of 690 patients, 596 (86.4%) diagnosed with COVID-19 and 94 (13.6%) diagnosed with MIS-C. Antithrombotic prophylaxis was used for 154 (22.3%) patients: 63 patients (10.6%) in the COVID-19 group and 91 (96.8%) patients in the MIS-C group. Use of antithrombotic prophylaxis was statistically higher in the MIS-C group (p<0.001). Patients who received antithrombotic prophylaxis were of older median age, were more commonly male, and had more frequent underlying diseases than those without prophylaxis (p<0.001, p<0.012, p<0.019, respectively). The most common underlying condition was obesity in patients who received antithrombotic prophylaxis. Thrombosis was observed in one (0.2%) patient in the COVID-19 group with a thrombus in the cephalic vein, two (2.1%) patients in the MIS-C group, with a dural thrombus in one patient and a cardiac thrombus in the other patient. The patients with thrombotic events were previously healthy and had mild disease. CONCLUSION: In our study, thrombotic events were rare compared with previous reports. We used antithrombotic prophylaxis for most children with underlying risk factors; perhaps for this reason, we did not observe thrombotic events in children with underlying risk factors. We suggest that patients diagnosed with COVID-19 or MIS-C be closely monitored for thrombotic events.

Private Facial Prediagnosis as an Edge Service for Parkinson's DBS Treatment Valuation.

Facial phenotyping for medical prediagnosis has recently been successfully exploited as a novel way for the preclinical assessment of a range of rare genetic diseases, where facial biometrics is revealed to have rich links to underlying genetic or medical causes. In this paper, we aim to extend this facial prediagnosis technology for a more general disease, Parkinson's Diseases (PD), and proposed an Artificial-Intelligence-of-Things (AIoT) edge-oriented privacy-preserving facial prediagnosis framework to analyze the treatment of Deep Brain Stimulation (DBS) on PD patients. In the proposed framework, a novel edge-based privacy-preserving framework is proposed to implement private deep facial diagnosis as a service over an AIoT-oriented information theoretically secure multi-party communication scheme, while data privacy has been a primary concern toward a wider exploitation of Electronic Health and Medical Records (EHR/EMR) over cloud-based medical services. In our experiments with a collected facial dataset from PD patients, for the first time, we proved that facial patterns could be used to evaluate the facial difference of PD patients undergoing DBS treatment. We further implemented a privacy-preserving information theoretical secure deep facial prediagnosis framework that can achieve the same accuracy as the non-encrypted one, showing the potential of our facial prediagnosis as a trustworthy edge service for grading the severity of PD in patients.

Checklist for Evaluation of Image-Based Artificial Intelligence Reports in Dermatology: CLEAR Derm Consensus Guidelines From the International Skin Imaging Collaboration Artificial Intelligence Working Group.

IMPORTANCE: The use of artificial intelligence (AI) is accelerating in all aspects of medicine and has the potential to transform clinical care and dermatology workflows. However, to develop image-based algorithms for dermatology applications, comprehensive criteria establishing development and performance evaluation standards are required to ensure product fairness, reliability, and safety. OBJECTIVE: To consolidate limited existing literature with expert opinion to guide developers and reviewers of dermatology AI. EVIDENCE REVIEW: In this consensus statement, the 19 members of the International Skin Imaging Collaboration AI working group volunteered to provide a consensus statement. A systematic PubMed search was performed of English-language articles published between December 1, 2008, and August 24, 2021, for "artificial intelligence" and "reporting guidelines," as well as other pertinent studies identified by the expert panel. Factors that were viewed as critical to AI development and performance evaluation were included and underwent 2 rounds of electronic discussion to achieve consensus. FINDINGS: A checklist of items was developed that outlines best practices of image-based AI development and assessment in dermatology. CONCLUSIONS AND RELEVANCE: Clinically effective AI needs to be fair, reliable, and safe; this checklist of best practices will help both developers and reviewers achieve this goal.

Reconstruction of ear canal wall with superior pedicled composite multi-fractured osteoperiosteal flap: a new technique and preliminary outcomes.

OBJECTIVE: This study evaluated the functional results of the superior pedicled composite multi-fractured osteoperiosteal flap technique. This method is a novel technique for the reconstruction of the external auditory canal. The study also examined the effect of the superior pedicled composite multi-fractured osteoperiosteal flap technique on patients' disease-related quality of life. METHOD: A total of 37 patients who underwent the superior pedicled composite multi-fractured osteoperiosteal flap technique were enrolled in the study. Their functional hearing results and disease-related quality of life scores were evaluated. RESULTS: A significant improvement was observed in the patients' hearing scores at the post-operative sixth month relative to the pre-operative period, and the patients' disease-related quality of life increased significantly. CONCLUSION: The superior pedicled composite multi-fractured osteoperiosteal flap method can be safely used, especially in patients undergoing retrograde mastoidectomy because of limited cholesteatoma. This method contributes to improving patients' hearing levels and disease-related quality of life.

Osteoclastic activity in chronic otitis media with cholesteatoma-related bone destruction.

BACKGROUND: Cholesteatoma-related bone destruction is the cause of many complications due to chronic otitis media. This study aimed to evaluate osteoclastic activity in cholesteatoma-related bone destruction using tartrate-resistant acid phosphatase 5b, an enzyme specific to osteoclastic activity. METHOD: Seventy-two patients diagnosed with chronic otitis media were included in this study and were divided into two groups: with and without bone destruction. The blood serum and tissue tartrate-resistant acid phosphatase 5b levels from both groups were compared. RESULTS: There were no significant differences in the level of serum enzymes between both groups. However, in tissue samples, tartrate-resistant acid phosphatase 5b levels were significantly lower in the bone destruction group than the group without bone destruction. CONCLUSION: This study determined that the level of tartrate-resistant acid phosphatase 5b, a specific enzyme for osteoclastic activity in cholesteatoma-related bone destruction, is locally decreased. This data suggests that osteoclastic activity may decrease in cholesteatoma-related bone destruction. However, further experimental and clinical studies are required to clarify this highly complex mechanism.

Skin Melanoma Detection in Microscopic Images Using HMM-Based Asymmetric Analysis and Expectation Maximization.

Melanoma is one of the deadliest types of skin cancer with increasing incidence. The most definitive diagnosis method is the histopathological examination of the tissue sample. In this paper, a melanoma detection algorithm is proposed based on decision-level fusion and a Hidden Markov Model (HMM), whose parameters are optimized using Expectation Maximization (EM) and asymmetric analysis. The texture heterogeneity of the samples is determined using asymmetric analysis. A fusion-based HMM classifier trained using EM is introduced. For this purpose, a novel texture feature is extracted based on two local binary patterns, namely local difference pattern (LDP) and statistical histogram features of the microscopic image. Extensive experiments demonstrate that the proposed melanoma detection algorithm yields a total error of less than 0.04%.

Dermoscopy Image Analysis: Overview and Future Directions.

Dermoscopy is a non-invasive skin imaging technique that permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. While studies on the automated analysis of dermoscopy images date back to the late 1990s, because of various factors (lack of publicly available datasets, open-source software, computational power, etc.), the field progressed rather slowly in its first two decades. With the release of a large public dataset by the International Skin Imaging Collaboration in 2016, development of open-source software for convolutional neural networks, and the availability of inexpensive graphics processing units, dermoscopy image analysis has recently become a very active research field. In this paper, we present a brief overview of this exciting subfield of medical image analysis, primarily focusing on three aspects of it, namely, segmentation, feature extraction, and classification. We then provide future directions for researchers.

A Survey of Feature Extraction in Dermoscopy Image Analysis of Skin Cancer.

Dermoscopy image analysis (DIA) is a growing field, with works being published every week. This makes it difficult not only to keep track of all the contributions, but also for new researchers to identify relevant information and new directions to be explored. Several surveys have been written in the past decade, but these tend to cover all of the steps of a CAD system, which can be overwhelming. Moreover, in these works, each of the steps is briefly discussed due to lack of space. Among the different blocks of the CAD system, the most relevant is the one devoted to feature extraction. This is also the block where existing works exhibit the most variability. Therefore, we believe that it is important to review the state-of-the-art on this matter. This work thoroughly explores the several types of features that have been used in DIA. A discussion on their relevance and limitations, as well as suggestions for future research are provided.

Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images.

BACKGROUND: Computer vision may aid in melanoma detection. OBJECTIVE: We sought to compare melanoma diagnostic accuracy of computer algorithms to dermatologists using dermoscopic images. METHODS: We conducted a cross-sectional study using 100 randomly selected dermoscopic images (50 melanomas, 44 nevi, and 6 lentigines) from an international computer vision melanoma challenge dataset (n = 379), along with individual algorithm results from 25 teams. We used 5 methods (nonlearned and machine learning) to combine individual automated predictions into "fusion" algorithms. In a companion study, 8 dermatologists classified the lesions in the 100 images as either benign or malignant. RESULTS: The average sensitivity and specificity of dermatologists in classification was 82% and 59%. At 82% sensitivity, dermatologist specificity was similar to the top challenge algorithm (59% vs. 62%, P = .68) but lower than the best-performing fusion algorithm (59% vs. 76%, P = .02). Receiver operating characteristic area of the top fusion algorithm was greater than the mean receiver operating characteristic area of dermatologists (0.86 vs. 0.71, P = .001). LIMITATIONS: The dataset lacked the full spectrum of skin lesions encountered in clinical practice, particularly banal lesions. Readers and algorithms were not provided clinical data (eg, age or lesion history/symptoms). Results obtained using our study design cannot be extrapolated to clinical practice. CONCLUSION: Deep learning computer vision systems classified melanoma dermoscopy images with accuracy that exceeded some but not all dermatologists.

A novel computational remodelling algorithm for the probabilistic evolution of collagen fibre dispersion in biaxially strained vascular tissue.

In this work, we constructed a novel collagen fibre remodelling algorithm that incorporates the complex nature of random evolution acting on single fibres causing macroscopic fibre dispersion. The proposed framework is different from the existing remodelling algorithms, in that the microscopic random force on cellular scales causing a rotational-type Brownian motion alone is considered as an aspect of vascular tissue remodelling. A continuum mechanical framework for the evolution of local dispersion and how it could be used for modeling the evolution of internal radius of biaxially strained artery structures under constant internal blood pressure are presented. A linear evolution form for the statistical fibre dispersion is employed in the model. The random force component of the evolution, which depends on the mechanical stress stimuli, is described by a single parameter. Although the mathematical form of the proposed model is simple, there is a strong link between the microscopic evolution of collagen dispersion on the cellular level and its effects on the macroscopic visible world through mechanical variables. We believe that the proposed algorithm utilizes a better understanding of the relationship between the evolution rates of mean fibre direction and fibre dispersion. The predictive capability of the algorithm is presented using experimental data. The model has been simulated by solving a single-layered axisymmetric artery (adventitia) deformation problem. The algorithm performed well for estimating the quantitative features of experimental anisotropy, the mean fibre direction vector and the dispersion (κ) measurements under strain-dependent evolution assumptions.

Thresholding methods for lesion segmentation of basal cell carcinoma in dermoscopy images.

PURPOSE: Algorithms employed for pigmented lesion segmentation perform poorly on dermoscopy images of basal cell carcinoma (BCC), the most common skin cancer. The main objective was to develop better methods for BCC segmentation. METHODS: Fifteen thresholding methods were implemented for BCC lesion segmentation. We propose two error metrics that better measure the type II error: Relative XOR Error and Lesion Capture Ratio. RESULTS: On training/test sets of 305 and 34 BCC images, respectively, five new techniques outperform two state-of-the-art methods used in segmentation of melanomas, based on the new error metrics. CONCLUSION: The proposed algorithms, which include solutions for image vignetting correction and border expansion to achieve dermatologist-like borders, provide more inclusive and feature-preserving border detection, favoring better BCC classification accuracy, in future work.

Assessment of ischaemia-reperfusion injury in the mice testis by using contrast ultrasound molecular imaging.

Timely diagnosis of ischaemia-reperfusion (IR)-induced injury after testicular torsion may be critical for saving reproductive function. The purpose of this study was to detect IR-induced injury, indicated by E-selectin overexpression, in murine testis using ultrasound molecular contrast imaging. Mice underwent 720° unilateral testicular torsion (ischaemia) followed by detorsion (reperfusion), and the control group (Sham-IR) was operated identically without extended ischaemia. In a separate positive control group, TNF-α was injected intratesticularly to induce inflammation and compared to intratesticular saline injection. Selectin-targeted or nontargeted ultrasound contrast microbubbles were injected intravenously, and two-dimensional (2D) real-time high-resolution ultrasound testicular imaging was performed after reperfusion or after TNF-α injection. Contrast intensity levels were significantly higher in the testis of the IR group as compared to the Sham-IR group after injection of targeted contrast microbubbles. Contrast intensities were similar between the IR and Sham-IR groups after injection of nontargeted microbubbles. In addition, targeted contrast intensity levels were significantly higher in the TNF-α-treated group as compared to the control group. This study indicates that ultrasound contrast molecular imaging with microbubbles targeted to E-selectin can be used to assess IR-induced testicular injury.

Quasi-non-linear deformation modeling of a human liver based on artificial and experimental data.

BACKGROUND: Researchers working on error-prevention theories have shown that the use of replica models within simulation systems has improved operating skills, resulting in better patient outcomes. METHODS: This study aims to provide material test data specifically for a human liver to validate the accuracy of viscoelastic soft tissue models. This allows the validation of virtual surgery simulators by comparison with physical test data obtained from material tests on a viscoelastic silicone gel pad. RESULTS: The results proved that stress behavior and relaxation curves of Aquaflex® experiment and FEM simulation are close if average liver response and respective material parameters and model are used. CONCLUSIONS: The precise representation of manipulated tissues used in virtual surgery trainers involves the accurate characterization of mechanical properties of the tissue. Consequently, successful implementations of these mechanical properties in a mathematical model of the deforming organ are of major importance. Copyright © 2015 John Wiley & Sons, Ltd.

Melanoma detection algorithm based on feature fusion.

A Computer Aided-Diagnosis (CAD) System for melanoma diagnosis usually makes use of different types of features to characterize the lesions. The features are often combined into a single vector that can belong to a high dimensional space (early fusion). However, it is not clear if this is the optimal strategy and works on other fields have shown that early fusion has some limitations. In this work, we address this issue and investigate which is the best approach to combine different features comparing early and late fusion. Experiments carried on the datasets PH2 (single source) and EDRA (multi source) show that late fusion performs better, leading to classification scores of Sensitivity = 98% and Specificity = 90% (PH(2)) and Sensitivity = 83% and Specificity = 76% (EDRA).

A clinically oriented system for melanoma diagnosis using a color representation.

Computer Aided-Diagnosis (CAD) systems have been proposed to help dermatologists diagnose melanomas. However, these systems fail to provide a medical explanation for the diagnosis. This makes the dermatologists unsure about their use, since they are not easy to understand. In this paper we propose a CAD system that extracts a clinically inspired color description of the lesion and then, uses this information to discriminate melanomas from benign lesions. The proposed system is also capable of showing the extracted color features, making the system and its decisions more comprehensible for practitioners. The development of this system is hampered by the lack of a database of detailed annotate dermoscopy images. Nonetheless, we are able to tackle this issue using an image annotation framework based on the Correspondence-LDA algorithm. This method is applied with success to the identification of relevant colors in dermoscopy images, obtaining an average Precision of 84.9% and a Recall of 85.5%. The proposed color representation is then used to classify skin lesions, resulting in a Sensitivity of 78.9% and Specificity of 76.7%, these values are promising and comparable with the state-of-the art.

Improving dermoscopy image classification using color constancy.

Robustness is one of the most important characteristics of computer-aided diagnosis systems designed for dermoscopy images. However, it is difficult to ensure this characteristic if the systems operate with multisource images acquired under different setups. Changes in the illumination and acquisition devices alter the color of images and often reduce the performance of the systems. Thus, it is important to normalize the colors of dermoscopy images before training and testing any system. In this paper, we investigate four color constancy algorithms: Gray World, max-RGB, Shades of Gray, and General Gray World. Our results show that color constancy improves the classification of multisource images, increasing the sensitivity of a bag-of-features system from 71.0% to 79.7% and the specificity from 55.2% to 76% using only 1-D RGB histograms as features.