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Dermatology Dermatology

Artificial intelligence for the classification of pigmented skin lesions in populations with skin of colour: A systematic review.

In Dermatology (Basel, Switzerland)

Background While skin cancers are less prevalent in people with skin of color, they are more often diagnosed at later stages and have a poorer prognosis. The use of artificial intelligence (AI) models can potentially improve early detection of skin cancers, however the lack of skin color diversity in training datasets may only widen the pre-existing racial discrepancies in dermatology. Objective To systematically review the technique, quality, accuracy, and implications of studies using AI models trained or tested in populations with skin of color, for classification of pigmented skin lesions. Methods PubMed was used to identify any studies describing AI models for classification of pigmented skin lesions. Only studies that used training datasets with at least 10% of images from people with skin of color were eligible. Outcomes on study population, design of AI model, accuracy, and quality of the studies were reviewed. Results Twenty-two eligible articles were identified. Majority of studies were trained on datasets obtained from Chinese (7/22), Korean (5/22), and Japanese populations (3/22). Seven studies used diverse datasets containing Fitzpatrick skin type I-III in combination with at least 10% from Black American, Native American, Pacific Islander or Fitzpatrick IV-VI. AI models producing binary outcomes (e.g., benign vs malignant) reported an accuracy ranging from 70% to 99.7%. Accuracy of AI models reporting multiclass outcomes (e.g., specific lesion diagnosis) was lower, ranging from 43% to 93%. Reader studies, where dermatologists' classification is compared with AI model outcomes, reported similar accuracy in one study, higher AI accuracy in three studies, and higher clinician accuracy in two studies. A quality review revealed that dataset description and variety, benchmarking, public evaluation, and healthcare application were frequently not addressed. Conclusions While this review provides promising evidence of accurate AI models in skin of color populations, there are still large discrepancies remain in the number of AI models developed in populations with skin of color (particularly Fitzpatrick type IV-VI) and those with largely European ancestry. A lack of publicly available datasets from diverse populations is likely a contributing factor, as is the inadequate reporting of patient-level metadata relating to skin color in training datasets.

Liu Yuyang, Primiero Clare A, Kulkarni Vishnutheertha, Soyer H Peter, Betz-Stablein Brigid

2023-Mar-21

General General

Lower body kinematics estimation during walking using an accelerometer.

In Journal of biomechanics

Measuring and predicting accurate joint angles are important to developing analytical tools to gauge users' progress. Such measurement is usually performed in laboratory settings, which is difficult and expensive. So, the aim of this study was continuous estimation of lower limb joint angles during walking using an accelerometer and random forest (RF). Thus, 73 subjects (26 women and 47 men) voluntarily participated in this study. The subjects walked at the slow, moderate, and fast speeds on a walkway, which was covered with 10 Vicon camera. Acceleration was used as input for a RF to estimate ankle, knee, and hip angles (in transverse, frontal, and sagittal planes). Pearson correlation coefficient (r) and Mean Square Error (MSE) were computed between the experimental and estimated data. Paired statistical parametric mapping (SPM) t-test was used to compare the experimental and estimated data throughout gait cycle. The results of this study showed that the MSE of joint angles between the experimental and estimated data ranged from 0.04 to 24.29 and r > 0.91. Moreover, the findings of SPM indicated that there was no significant difference between the experimental and estimated data of ankle, knee, and hip angles in all three planes throughout gait cycle. The results of our research developed a more accessible, portable procedure to quantifying lower limb joint angles by an accelerometer and RF. So, such wearable-based joint angles have the potential to be used in outside-laboratory settings to measure walking kinematics.

Mantashloo Zahed, Abbasi Ali, Tazji Mehdi Khaleghi, Pedram Mir Mohsen

2023-Mar-17

Accelerometer, Gait analysis, Joint angle, Machine learning, Random forest, Statistical parametric mapping

General General

Metabolic and Inflammatory profiles define phenotypes with clinical relevance in female knee osteoarthritis patients with joint effusion.

In Rheumatology (Oxford, England)

OBJECTIVES : Osteoarthritis has been the subject of abundant research in the last years with limited translation to the clinical practice, probably due to the disease's high heterogeneity. In this study, we aimed to identify different phenotypes in Knee osteoarthritis (KOA) patients with joint effusion based on their metabolic and inflammatory profiles.

METHODS : A non-supervised strategy based on Statistical and Machine Learning methods was applied to 45 parameters measured on 168 female KOA patients with persistent joint effusion, consecutively recruited at our hospital after a monographic OA outpatient visit. Data comprised anthropometric and metabolic factors and a panel of systemic and local inflammatory markers. The resulting clusters were compared regarding their clinical, radiographic and ultrasound severity at baseline and their radiographic progression at two years.

RESULTS : Our analyses identified four KOA Inflammatory Phenotypes (KOIP): a group characterized by metabolic syndrome, probably driven by body fat and obesity, and by high local and systemic inflammation (KOIP-1); a metabolically healthy phenotype with mild overall inflammation (KOIP-2); a non-metabolic phenotype with high inflammation levels (KOIP-3) and; a metabolic phenotype with low inflammation and cardiovascular risk factors not associated with obesity (KOIP-4). Of interest, these groups exhibited differences regarding pain, functional disability and radiographic progression, pointing to a clinical relevance of the uncovered phenotypes.

CONCLUSION : Our results support the existence of different KOA phenotypes with clinical relevance and differing pathways regarding their pathophysiology and disease evolution, which entails implications in patients' stratification, treatment tailoring and the search of novel and personalized therapies.

Calvet Joan, García-Manrique María, Berenguer-Llergo Antoni, Orellana Cristóbal, Cirera Silvia Garcia, Llop Maria, Lencastre Carlos Galisteo, Arévalo Marta, Aymerich Cristina, Gómez Rafael, Giménez Néstor Albiñana, Gratacó Jordi

2023-Mar-21

Knee osteoarthritis, clinical severity, inflammatory, machine learning, metabolism, phenotype

General General

Phenonaut; multiomics data integration for phenotypic space exploration.

In Bioinformatics (Oxford, England)

SUMMARY : Data integration workflows for multiomics data take many forms across academia and industry. Efforts with limited resources often encountered in academia can easily fall short of data integration best practices for processing and combining high content imaging, proteomics, metabolomics and other omics data. We present Phenonaut, a Python software package designed to address the data workflow needs of migration, control, integration, and auditability in the application of literature and proprietary techniques for data source and structure agnostic workflow creation.

AVAILABILITY AND IMPLEMENTATION : Source code: https://github.com/CarragherLab/phenonaut, Documentation: https://carragherlab.github.io/phenonaut, PyPI package: https://pypi.org/project/phenonaut/.

SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics online.

Shave Steven, Dawson John C, Athar Abdullah M, Nguyen Cuong Q, Kasprowicz Richard, Carragher Neil O

2023-Mar-21

General General

Racial difference in the association between non-alcoholic fatty liver disease and incident type 2 diabetes: findings from the CARDIA study.

In Diabetologia ; h5-index 79.0

AIMS/HYPOTHESIS : Type 2 diabetes and non-alcoholic fatty liver disease (NAFLD) are prevalent diseases of metabolic origin. We examined the association between NAFLD and the development of type 2 diabetes among non-Asian adults, and whether the association differs by race.

METHODS : We analysed data from the Coronary Artery Risk Development in Young Adults (CARDIA) study, a population-based prospective cohort study. Participants underwent non-contrast abdominal computed tomography (CT) at baseline (2010-2011) and assessment of glucose measures at the follow-up exam (2015-2016). NAFLD was defined as liver attenuation ≤51 Hounsfield units on CT images after exclusion for other liver fat causes. Race was self-reported. We used targeted maximum likelihood estimation (TMLE) with machine-learning algorithms to estimate difference in type 2 diabetes risk between the NAFLD and non-NAFLD groups.

RESULTS : Of the 1995 participants without type 2 diabetes at baseline (mean age±SD, 50.0±3.6 years; 59% women; 55.0% White and 45.0% Black), 21.7% of White and 16.8% of Black participants had NAFLD at baseline, and 3.7% of White and 8.0% of Black participants developed type 2 diabetes at follow up. After multivariable adjustment, risk difference for type 2 diabetes associated with NAFLD vs no NAFLD was 4.1% (95% CI 0.3%, 7.9%) among White participants and -1.9% (95% CI -5.7%, 2.0%) in Black participants.

CONCLUSIONS/INTERPRETATION : NAFLD was associated with a higher risk of type 2 diabetes among White participants but not among Black participants. This finding suggests that the effect of liver fat on impaired glucose metabolism may be smaller in Black than in White individuals.

Hatano Yu, VanWagner Lisa B, Carnethon Mercedes R, Bancks Michael P, Carson April P, Lloyd-Jones Donald M, Østbye Truls, Viera Anthony J, Yano Yuichiro

2023-Mar-21

Machine learning, Non-alcoholic fatty liver disease, Racial difference, Type 2 diabetes

General General

Gender Differences in the Nonspecific and Health-Specific Use of Social Media Before and During the COVID-19 Pandemic: Trend Analysis Using HINTS 2017-2020 Data.

In Journal of health communication ; h5-index 36.0

The use of social media has changed since the outbreak of coronavirus disease 2019 (COVID-19). However, little is known about the gender disparity in social media use for nonspecific and health-specific issues before and during the COVID-19 pandemic. Based on a gender difference perspective, this study aimed to examine how the nonspecific and health-specific uses of social media changed in 2017-2020. The data came from the Health Information National Trends Survey Wave 5 Cycle 1-4. This study included 10,426 participants with complete data. Compared to 2017, there were higher levels of general use in 2019 and 2020, and an increased likelihood of health-related use in 2020 was reported among the general population. Female participants were more likely to be nonspecific and health-specific users than males. Moreover, the relationship of gender with general use increased in 2019 and 2020; however, concerning health-related use, it expanded in 2019 but narrowed in 2020. The COVID-19 global pandemic led to increased use of social media, especially for health-related issues among males. These findings further our understanding of the gender gap in health communication through social media, and contribute to targeted messaging to promote health and reduce disparities between different groups during the pandemic.

Ye Linglong, Chen Yang, Cai Yongming, Kao Yi-Wei, Wang Yuanxin, Chen Mingchih, Shia Ben-Chang, Qin Lei

2023-Mar-21