Detection of Nocturnal Scratching Movements in Patients with Atopic Dermatitis Using Accelerometers and Recurrent Neural Networks

A Hierarchical Framework for Selecting Reference Measures for the Analytical Validation of Sensor-Based Digital Health Technologies

Sensor-based digital health technologies (sDHTs) are increasingly used to support scientific and clinical decision-making. The digital clinical measures they generate offer enormous benefits, including providing more patient-relevant data, improving patient access, reducing costs, and driving inclusion across health care ecosystems. Scientific best practices and regulatory guidance now provide clear direction to investigators seeking to evaluate sDHTs for use in different contexts. However, the quality of the evidence reported for analytical validation of sDHTs-evaluation of algorithms converting sample-level sensor data into a measure that is clinically interpretable-is inconsistent and too often insufficient to support a particular digital measure as fit-for-purpose. We propose a hierarchical framework to address challenges related to selecting the most appropriate reference measure for conducting analytical validation and codify best practices and an approach that will help capture the greatest value of sDHTs for public health, patient care, and medical product development.

Quantifying Nocturnal Scratch in Atopic Dermatitis: A Machine Learning Approach Using Digital Wrist Actigraphy

Nocturnal scratching substantially impairs the quality of life in individuals with skin conditions such as atopic dermatitis (AD). Current clinical measurements of scratch rely on patient-reported outcomes (PROs) on itch over the last 24 h. Such measurements lack objectivity and sensitivity. Digital health technologies (DHTs), such as wearable sensors, have been widely used to capture behaviors in clinical and real-world settings. In this work, we develop and validate a machine learning algorithm using wrist-wearing actigraphy that could objectively quantify nocturnal scratching events, therefore facilitating accurate assessment of disease progression, treatment effectiveness, and overall quality of life in AD patients. A total of seven subjects were enrolled in a study to generate data overnight in an inpatient setting. Several machine learning models were developed, and their performance was compared. Results demonstrated that the best-performing model achieved the F1 score of 0.45 on the test set, accompanied by a precision of 0.44 and a recall of 0.46. Upon satisfactory performance with an expanded subject pool, our automatic scratch detection algorithm holds the potential for objectively assessing sleep quality and disease state in AD patients. This advancement promises to inform and refine therapeutic strategies for individuals with AD.

Measuring Symptoms Across the Spectrum of Chronic Kidney Disease: Strategies for Incorporation Into Kidney Care

Many people across the spectrum of chronic kidney disease (CKD) experience a large symptom burden. Measuring symptoms can be a way of responding to the concerns of patients and their priorities of care and may help to improve overall outcomes, including health-related quality of life. The objective of this article is to discuss approaches to measuring symptoms across the spectrum of CKD and to highlight strategies to facilitate the incorporation of routine symptom assessment into kidney care. Specifically, we discuss the use of validated patient-reported outcome measures in CKD as they relate to measuring symptoms, including their benefits and limitations, and describe commonly used patient-reported outcome measures. We discuss potential barriers that should be considered when contemplating the development of a program to routinely measure and address symptoms. Finally, we outline a systematic, stepwise approach to measuring symptoms with implementation strategies to address the common barriers. Although the principles outlined in this article can be applied to research and audit, the principal focus is on symptom measurement aimed at informing clinical practice and directly improving patient outcomes.

Artificial Intelligence: A Snapshot of Its Application in Chronic Inflammatory and Autoimmune Skin Diseases

Immuno-correlated dermatological pathologies refer to skin disorders that are closely associated with immune system dysfunction or abnormal immune responses. Advancements in the field of artificial intelligence (AI) have shown promise in enhancing the diagnosis, management, and assessment of immuno-correlated dermatological pathologies. This intersection of dermatology and immunology plays a pivotal role in comprehending and addressing complex skin disorders with immune system involvement. The paper explores the knowledge known so far and the evolution and achievements of AI in diagnosis; discusses segmentation and the classification of medical images; and reviews existing challenges, in immunological-related skin diseases. From our review, the role of AI has emerged, especially in the analysis of images for both diagnostic and severity assessment purposes. Furthermore, the possibility of predicting patients' response to therapies is emerging, in order to create tailored therapies.

A Sensorised Glove to Detect Scratching for Patients with Atopic Dermatitis

In this work, a lightweight compliant glove that detects scratching using data from microtubular stretchable sensors on each finger and an inertial measurement unit (IMU) on the palm through a machine learning model is presented: the SensorIsed Glove for Monitoring Atopic Dermatitis (SIGMA). SIGMA provides the user and clinicians with a quantifiable way of assaying scratch as a proxy to itch. With the quantitative information detailing scratching frequency and duration, the clinicians would be able to better classify the severity of itch and scratching caused by atopic dermatitis (AD) more objectively to optimise treatment for the patients, as opposed to the current subjective methods of assessments that are currently in use in hospitals and research settings. The validation data demonstrated an accuracy of 83% of the scratch prediction algorithm, while a separate 30 min validation trial had an accuracy of 99% in a controlled environment. In a pilot study with children (n = 6), SIGMA accurately detected 94.4% of scratching when the glove was donned. We believe that this simple device will empower dermatologists to more effectively measure and quantify itching and scratching in AD, and guide personalised treatment decisions.

A multimodal sensing ring for quantification of scratch intensity

BACKGROUND

An objective measurement of chronic itch is necessary for improvements in patient care for numerous medical conditions. While wearables have shown promise for scratch detection, they are currently unable to estimate scratch intensity, preventing a comprehensive understanding of the effect of itch on an individual.

METHODS

In this work, we present a framework for the estimation of scratch intensity in addition to the detection of scratch. This is accomplished with a multimodal ring device, consisting of an accelerometer and a contact microphone, a pressure-sensitive tablet for capturing ground truth intensity values, and machine learning algorithms for regression of scratch intensity on a 0-600 milliwatts (mW) power scale that can be mapped to a 0-10 continuous scale.

RESULTS

We evaluate the performance of our algorithms on 20 individuals using leave one subject out cross-validation and using data from 14 additional participants, we show that our algorithms achieve clinically-relevant discrimination of scratching intensity levels. By doing so, our device enables the quantification of the substantial variations in the interpretation of the 0-10 scale frequently utilized in patient self-reported clinical assessments.

CONCLUSIONS

This work demonstrates that a finger-worn device can provide multidimensional, objective, real-time measures for the action of scratching.

Managing the symptom burden associated with maintenance dialysis: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference

Individuals with kidney failure undergoing maintenance dialysis frequently report a high symptom burden that can interfere with functioning and diminish life satisfaction. Until recently, the focus of nephrology care for dialysis patients has been related primarily to numerical targets for laboratory measures, and outcomes such as cardiovascular disease and mortality. Routine symptom assessment is not universal or standardized in dialysis care. Even when symptoms are identified, treatment options are limited and are initiated infrequently, in part because of a paucity of evidence in the dialysis population and the complexities of medication interactions in kidney failure. In May of 2022, Kidney Disease: Improving Global Outcomes (KDIGO) held a Controversies Conference-Symptom-Based Complications in Dialysis-to identify the optimal means for diagnosing and managing symptom-based complications in patients undergoing maintenance dialysis. Participants included patients, physicians, behavioral therapists, nurses, pharmacists, and clinical researchers. They outlined foundational principles and consensus points related to identifying and addressing symptoms experienced by patients undergoing dialysis and described gaps in the knowledge base and priorities for research. Healthcare delivery and education systems have a responsibility to provide individualized symptom assessment and management. Nephrology teams should take the lead in symptom management, although this does not necessarily mean taking ownership of all aspects of care. Even when options for clinical response are limited, clinicians should focus on acknowledging, prioritizing, and managing symptoms that are most important to individual patients. A recognized factor in the initiation and implementation of improvements in symptom assessment and management is that they will be based on locally existing needs and resources.

Margined Horn-Shaped Air Chamber for Body-Conduction Microphone

The sound amplification ratios of sealed air chambers with different shapes were quantitatively compared to design a body-conduction microphone to measure animal scratching sounds. Recently, quantitative monitoring of scratching intensity in dogs has been required. We have already developed a collar with a body-conduction microphone to measure body-conducted scratching sounds. However, the air chamber, one of the components of the body-conduction microphone, has not been appropriately designed. This study compared the amplification ratios of air chambers with different shapes through numerical analysis and experiments. According to the results, the horn-shaped air chamber achieved the highest amplification performance, at least for sound frequencies below 3 kHz. The simulated amplification ratio of the horn-shaped air chamber with a 1 mm height and a 15 mm diameter was 52.5 dB. The deformation of the bottom of the air chamber affected the amplification ratio. Adjusting the margin of the margined horn shape could maintain its amplification ratio at any pressing force. The simulated and experimental amplification ratios of the margined horn-shaped air chamber were 53.4 dB and 19.4 dB, respectively.

Assessing nocturnal scratch with actigraphy in atopic dermatitis patients

Nocturnal scratch is one major factor leading to impaired quality of life in atopic dermatitis (AD) patients. Therefore, objectively quantifying nocturnal scratch events aids in assessing the disease state, treatment effect, and AD patients' quality of life. In this paper, we describe the use of actigraphy, highly predictive topological features, and a model-ensembling approach to develop an assessment of nocturnal scratch events by measuring scratch duration and intensity. Our assessment is tested in a clinical setting against the ground truth obtained from video recordings. The new approach addresses unmet challenges in existing studies, such as the lack of generalizability to real-world applications, the failure to capture finger scratches, and the limitations in the evaluation due to imbalanced data in the current literature. Furthermore, the performance evaluation shows agreement between derived digital endpoints and the video annotation ground truth, as well as patient-reported outcomes, which demonstrated the validity of the new assessment of nocturnal scratch.

Scoring sleep with artificial intelligence enables quantification of sleep stage ambiguity: hypnodensity based on multiple expert scorers and auto-scoring

STUDY OBJECTIVES

To quantify the amount of sleep stage ambiguity across expert scorers and to validate a new auto-scoring platform against sleep staging performed by multiple scorers.

METHODS

We applied a new auto-scoring system to three datasets containing 95 PSGs scored by 6-12 scorers, to compare sleep stage probabilities (hypnodensity; i.e. the probability of each sleep stage being assigned to a given epoch) as the primary output, as well as a single sleep stage per epoch assigned by hierarchical majority rule.

RESULTS

The percentage of epochs with 100% agreement across scorers was 46 ± 9%, 38 ± 10% and 32 ± 9% for the datasets with 6, 9, and 12 scorers, respectively. The mean intra-class correlation coefficient between sleep stage probabilities from auto- and manual-scoring was 0.91, representing excellent reliability. Within each dataset, agreement between auto-scoring and consensus manual-scoring was significantly higher than agreement between manual-scoring and consensus manual-scoring (0.78 vs. 0.69; 0.74 vs. 0.67; and 0.75 vs. 0.67; all p < 0.01).

CONCLUSIONS

Analysis of scoring performed by multiple scorers reveals that sleep stage ambiguity is the rule rather than the exception. Probabilities of the sleep stages determined by artificial intelligence auto-scoring provide an excellent estimate of this ambiguity. Compared to consensus manual-scoring, sleep staging derived from auto-scoring is for each individual PSG noninferior to manual-scoring meaning that auto-scoring output is ready for interpretation without the need for manual adjustment.

Defining digital measurement of scratching during sleep, or “Nocturnal Scratch”: A review of the literature (Preprint)

BACKGROUND

Digital sensing solutions represent a convenient, objective, relatively inexpensive method that could be leveraged for assessing symptoms of various health conditions. Recent progress in the capabilities of digital sensing products has targeted the measurement of scratching during sleep, traditionally referred to as nocturnal scratching, in patients with atopic dermatitis or other skin conditions. Many solutions measuring nocturnal scratch have been developed; however, a lack of efforts toward standardization of the measure's definition and contextualization of scratching during sleep hampers the ability to compare different technologies for this purpose.

OBJECTIVE

We aimed to address this gap and bring forth unified measurement definitions for nocturnal scratch.

METHODS

We performed a narrative literature review of definitions of scratching in patients with skin inflammation and a targeted literature review of sleep in the context of the period during which such scratching occurred. Both searches were limited to English language studies in humans. The extracted data were synthesized into themes based on study characteristics: scratch as a behavior, other characterization of the scratching movement, and measurement parameters for both scratch and sleep. We then developed ontologies for the digital measurement of sleep scratching.

RESULTS

In all, 29 studies defined inflammation-related scratching between 1996 and 2021. When cross-referenced with the results of search terms describing the sleep period, only 2 of these scratch-related papers also described sleep-related variables. From these search results, we developed an evidence-based and patient-centric definition of nocturnal scratch: an action of rhythmic and repetitive skin contact movement performed during a delimited time period of intended and actual sleep that is not restricted to any specific time of the day or night. Based on the measurement properties identified in the searches, we developed ontologies of relevant concepts that can be used as a starting point to develop standardized outcome measures of scratching during sleep in patients with inflammatory skin conditions.

CONCLUSIONS

This work is intended to serve as a foundation for the future development of unified and well-described digital health technologies measuring nocturnal scratching and should enable better communication and sharing of results between various stakeholders taking part in research in atopic dermatitis and other inflammatory skin conditions.

Updated Perspectives on the Diagnosis and Management of Onychomycosis

Onychomycosis is the most common nail disease encountered in clinical practice and can cause pain, difficulty with ambulation, and psycho-social problems. A thorough history and physical examination, including dermoscopy, should be performed for each patient presenting with nail findings suggestive of onychomycosis. Several approaches are available for definitive diagnostic testing, including potassium hydroxide and microscopy, fungal culture, histopathology, polymerase chain reaction, or a combination of techniques. Confirmatory testing should be performed for each patient prior to initiating any antifungal therapies. There are several different therapeutic options available, including oral and topical medications as well as device-based treatments. Oral antifungals are generally recommended for moderate to severe onychomycosis and have higher cure rates, while topical antifungals are recommended for mild to moderate disease and have more favorable safety profiles. Oral terbinafine, itraconazole, and griseofulvin and topical ciclopirox 8% nail lacquer, efinaconazole 10% solution, and tavaborole 5% solution are approved by the Food and Drug Administration for treatment of onychomycosis in the United States and amorolfine 5% nail lacquer is approved in Europe. Laser treatment is approved in the United States for temporary increases in clear nail, but clinical results are suboptimal. Oral fluconazole is not approved in the United States for onychomycosis treatment, but is frequently used off-label with good efficacy. Several novel oral, topical, and over-the-counter therapies are currently under investigation. Physicians should consider the disease severity, infecting pathogen, medication safety, efficacy and cost, and patient age, comorbidities, medication history, and likelihood of compliance when determining management plans. Onychomycosis is a chronic disease with high recurrence rates and patients should be counseled on an appropriate plan to minimize recurrence risk following effective antifungal therapy.

Deep learning-based diagnosis models for onychomycosis in dermoscopy

BACKGROUND

Onychomycosis is a common disease. Emerging noninvasive, real-time techniques such as dermoscopy and deep convolutional neural networks have been proposed for the diagnosis of onychomycosis. However, deep learning application in dermoscopic image hasn't been reported.

OBJECTIVES

To explore the establishment of deep learning-based diagnostic models for onychomycosis in dermoscopy to improve the diagnostic efficiency and accuracy.

METHODS

We evaluated the dermoscopic patterns of onychomycosis diagnosed at Sun Yat-sen Memorial Hospital, Guangzhou, China from May 2019 to February 2021 and included nail psoriasis and traumatic onychodystrophy as control groups. Based on the dermoscopic images and the characteristic dermoscopic patterns of onychomycosis, we gain the faster region-based convolutional neural networks to distinguish between nail disorder and normal nail, onychomycosis and non-mycological nail disorder (nail psoriasis and traumatic onychodystrophy). The diagnostic performance is compared between deep learning-based diagnosis models and dermatologists.

RESULTS

All of 1155 dermoscopic images were collected, including onychomycosis (603 images), nail psoriasis (221 images), traumatic onychodystrophy (104 images) and normal cases (227 images). Statistical analyses revealed subungual keratosis, distal irregular termination, longitudinal striae, jagged edge, marble-like turbid area, and cone-shaped keratosis were of high specificity (>82%) for onychomycosis diagnosis. The deep learning-based diagnosis models (ensemble model) showed test accuracy /specificity/ sensitivity /Youden index of (95.7%/98.8%/82.1%/0.809), (87.5%/93.0%/78.5%/0.715) for nail disorder and onychomycosis. The diagnostic performance for onychomycosis using ensemble model was superior to 54 dermatologists.

CONCLUSIONS

Our study demonstrated onychomycosis had distinctive dermoscopic patterns, compared with nail psoriasis and traumatic onychodystrophy. The deep learning-based diagnosis models showed a diagnostic accuracy of onychomycosis, superior to dermatologists.

Use of technology for the objective evaluation of scratching behavior: A systematic review

Introduction

Pruritus is a common symptom across various dermatologic conditions, with a negative impact on quality of life. Devices to quantify itch objectively primarily use scratch as a proxy. This review compares and evaluates the performance of technologies aimed at objectively measuring scratch behavior.

Methods

Articles identified from literature searches performed in October 2020 were reviewed and those that did not report a primary statistical performance measure (eg, sensitivity, specificity) were excluded. The articles were independently reviewed by 2 authors.

Results

The literature search resulted in 6231 articles, of which 24 met eligibility criteria. Studies were categorized by technology, with actigraphy being the most studied (n = 21). Wrist actigraphy's performance is poorer in pruritic patients and inherently limited in finger-dominant scratch detection. It has moderate correlations with objective measures (Eczema and Area Severity Index/Investigator's Global Assessment: r_s(ρ) = 0.70-0.76), but correlations with subjective measures are poor (r² = 0.06, r_s(ρ) = 0.18-0.40 for itch measured using a visual analog scale). This may be due to varied subjective perception of itch or actigraphy's underestimation of scratch.

Conclusion

Actigraphy's large variability in performance and limited understanding of its specificity for scratch merits larger studies looking at validation of data analysis algorithms and device performance, particularly within target patient populations.

Dissecting the precise nature of itch-evoked scratching

Itch is a discrete and irritating sensation tightly coupled to a drive to scratch. Acute scratching developed evolutionarily as an adaptive defense against skin irritants, pathogens, or parasites. In contrast, the itch-scratch cycle in chronic itch is harmful, inducing escalating itch and skin damage. Clinically and preclinically, scratching incidence is currently evaluated as a unidimensional motor parameter and believed to reflect itch severity. We propose that scratching, when appreciated as a complex, multidimensional motor behavior, will yield greater insight into the nature of itch and the organization of neural circuits driving repetitive motor patterns. We outline the limitations of standard measurements of scratching in rodent models and present new approaches to observe and quantify itch-evoked scratching. We argue that accurate quantitative measurements of scratching are critical for dissecting the molecular, cellular, and circuit mechanisms underlying itch and for preclinical development of therapeutic interventions for acute and chronic itch disorders.

The role of machine learning in clinical research: transforming the future of evidence generation

Background

Interest in the application of machine learning (ML) to the design, conduct, and analysis of clinical trials has grown, but the evidence base for such applications has not been surveyed. This manuscript reviews the proceedings of a multi-stakeholder conference to discuss the current and future state of ML for clinical research. Key areas of clinical trial methodology in which ML holds particular promise and priority areas for further investigation are presented alongside a narrative review of evidence supporting the use of ML across the clinical trial spectrum.

Results

Conference attendees included stakeholders, such as biomedical and ML researchers, representatives from the US Food and Drug Administration (FDA), artificial intelligence technology and data analytics companies, non-profit organizations, patient advocacy groups, and pharmaceutical companies. ML contributions to clinical research were highlighted in the pre-trial phase, cohort selection and participant management, and data collection and analysis. A particular focus was paid to the operational and philosophical barriers to ML in clinical research. Peer-reviewed evidence was noted to be lacking in several areas.

Conclusions

ML holds great promise for improving the efficiency and quality of clinical research, but substantial barriers remain, the surmounting of which will require addressing significant gaps in evidence.

EVIDENCE Publication Checklist for Studies Evaluating Connected Sensor Technologies: Explanation and Elaboration

The EVIDENCE (EValuatIng connecteD sENsor teChnologiEs) checklist was developed by a multidisciplinary group of content experts convened by the Digital Medicine Society, representing the clinical sciences, data management, technology development, and biostatistics. The aim of EVIDENCE is to promote high quality reporting in studies where the primary objective is an evaluation of a digital measurement product or its constituent parts. Here we use the terms digital measurement product and connected sensor technology interchangeably to refer to tools that process data captured by mobile sensors using algorithms to generate measures of behavioral and/or physiological function. EVIDENCE is applicable to 5 types of evaluations: (1) proof of concept; (2) verification, (3) analytical validation, and (4) clinical validation as defined by the V3 framework; and (5) utility and usability assessments. Using EVIDENCE, those preparing, reading, or reviewing studies evaluating digital measurement products will be better equipped to distinguish necessary reporting requirements to drive high-quality research. With broad adoption, the EVIDENCE checklist will serve as a much-needed guide to raise the bar for quality reporting in published literature evaluating digital measurements products.

Teledermatology in 2020: past, present and future perspectives

Born in 1995, teledermatology (TD) turns 25 years old today. Since then, TD evolved according to patients and physicians needs. The present review aimed to summarize all the efforts and experiences carried out in the field of TD and its subspecialties, the evolution and the future perspectives. A literature search was conducted in PubMed and Google Scholar. The state of the art of the "tele-dermo research" included TD and clinical trials, TD/TDS web platforms, TDS and artificial intelligence studies. Finally, the future perspective of TD/TDS in the era of social distancing was discussed. Using TD in specific situations adds several benefits including time-effectiveness of intervention and reduction in the waiting time for the first visit, reduced travel-costs, reduced sanitary costs, equalization of access from patient to specialistic consult. The communication technologies devices currently available can adequately support the growing needs of tele-assistance. A main limit is the current lack of a common clear European regulation for practicing TD, encompassing privacy issues and data management. The pandemic lockdown of 2020 has highlighted the importance of performing TD for all those patient, elderly and/or fragile, where the alternative would be no care at all. Many efforts are needed to develop efficient workflows and TD programs to facilitate the interplay among the different TD actors, along with practice guidelines or position statements.

A skin-conformable wireless sensor to objectively quantify symptoms of pruritus

Itch is a common clinical symptom and major driver of disease-related morbidity across a wide range of medical conditions. A substantial unmet need is for objective, accurate measurements of itch. In this article, we present a noninvasive technology to objectively quantify scratching behavior via a soft, flexible, and wireless sensor that captures the acousto-mechanic signatures of scratching from the dorsum of the hand. A machine learning algorithm validated on data collected from healthy subjects (n = 10) indicates excellent performance relative to smartwatch-based approaches. Clinical validation in a cohort of predominately pediatric patients (n = 11) with moderate to severe atopic dermatitis included 46 sleep-nights totaling 378.4 hours. The data indicate an accuracy of 99.0% (84.3% sensitivity, 99.3% specificity) against visual observation. This work suggests broad capabilities relevant to applications ranging from assessing the efficacy of drugs for conditions that cause itch to monitoring disease severity and treatment response.

Development of digital measures for nighttime scratch and sleep using wrist-worn wearable devices

Patients with atopic dermatitis experience increased nocturnal pruritus which leads to scratching and sleep disturbances that significantly contribute to poor quality of life. Objective measurements of nighttime scratching and sleep quantity can help assess the efficacy of an intervention. Wearable sensors can provide novel, objective measures of nighttime scratching and sleep; however, many current approaches were not designed for passive, unsupervised monitoring during daily life. In this work, we present the development and analytical validation of a method that sequentially processes epochs of sample-level accelerometer data from a wrist-worn device to provide continuous digital measures of nighttime scratching and sleep quantity. This approach uses heuristic and machine learning algorithms in a hierarchical paradigm by first determining when the patient intends to sleep, then detecting sleep–wake states along with scratching episodes, and lastly deriving objective measures of both sleep and scratch. Leveraging reference data collected in a sleep laboratory (NCT ID: NCT03490877), results show that sensor-derived measures of total sleep opportunity (TSO; time when patient intends to sleep) and total sleep time (TST) correlate well with reference polysomnography data (TSO: r = 0.72, p < 0.001; TST: r = 0.76, p < 0.001; N = 32). Log transformed sensor derived measures of total scratching duration achieve strong agreement with reference annotated video recordings (r = 0.82, p < 0.001; N = 25). These results support the use of wearable sensors for objective, continuous measurement of nighttime scratching and sleep during daily life.

Research Techniques Made Simple:Teledermatology in Clinical Trials

Telemedicine is well established as a means of providing high-quality healthcare at a distance, particularly to patients in underserved populations. Technologies in teledermatology can be used to complement traditional methodologies of clinical trials, expanding accessibility of trials to people typically unable to participate in research. Tools of communication technology may enhance many aspects of clinical trials in dermatology, from recruitment and retention of participants to collection of real-time data. Clinical trials can be made completely virtual or incorporate aspects of virtual technologies at any stage of research. Virtual clinical trials are considered highly patient-centered, as the ability of participants to engage with research staff from their own home often supplants the need for many or all on-site clinic visits. As technological advances influence every aspect of modern life, clinical trials will also evolve to incorporate these tools, meeting participant expectations and overcoming traditional challenges of conducting research. Virtual clinical trials come with specific issues pertaining to analysis of data, technology, and oversight. As more virtual trials are conducted, advantages and limitations of using such technology in research will become clearer and regulatory guidelines will be more firmly established.

Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations

Machine learning (ML) has the potential to improve the dermatologist's practice from diagnosis to personalized treatment. Recent advancements in access to large datasets (e.g., electronic medical records, image databases, omics), faster computing, and cheaper data storage have encouraged the development of ML algorithms with human-like intelligence in dermatology. This article is an overview of the basics of ML, current applications of ML, and potential limitations and considerations for further development of ML. We have identified five current areas of applications for ML in dermatology: (1) disease classification using clinical images; (2) disease classification using dermatopathology images; (3) assessment of skin diseases using mobile applications and personal monitoring devices; (4) facilitating large-scale epidemiology research; and (5) precision medicine. The purpose of this review is to provide a guide for dermatologists to help demystify the fundamentals of ML and its wide range of applications in order to better evaluate its potential opportunities and challenges.

Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare

Precision Medicine implies a deep understanding of inter-individual differences in health and disease that are due to genetic and environmental factors. To acquire such understanding there is a need for the implementation of different types of technologies based on artificial intelligence (AI) that enable the identification of biomedically relevant patterns, facilitating progress towards individually tailored preventative and therapeutic interventions. Despite the significant scientific advances achieved so far, most of the currently used biomedical AI technologies do not account for bias detection. Furthermore, the design of the majority of algorithms ignore the sex and gender dimension and its contribution to health and disease differences among individuals. Failure in accounting for these differences will generate sub-optimal results and produce mistakes as well as discriminatory outcomes. In this review we examine the current sex and gender gaps in a subset of biomedical technologies used in relation to Precision Medicine. In addition, we provide recommendations to optimize their utilization to improve the global health and disease landscape and decrease inequalities.

Detecting and Classifying Self-injurious Behavior in Autism Spectrum Disorder Using Machine Learning Techniques

Traditional self-injurious behavior (SIB) management can place compliance demands on the caregiver and have low ecological validity and accuracy. To support an SIB monitoring system for autism spectrum disorder (ASD), we evaluated machine learning methods for detecting and distinguishing diverse SIB types. SIB episodes were captured with body-worn accelerometers from children with ASD and SIB. The highest detection accuracy was found with k-nearest neighbors and support vector machines (up to 99.1% for individuals and 94.6% for grouped participants), and classification efficiency was quite high (offline processing at ~ 0.1 ms/observation). Our results provide an initial step toward creating a continuous and objective smart SIB monitoring system, which could in turn facilitate the future care of a pervasive concern in ASD.

Emerging Methods to Objectively Assess Pruritus in Atopic Dermatitis

INTRODUCTION

Atopic dermatitis (AD) is an inflammatory skin disease with a chronic, relapsing course. Clinical features of AD vary by age, duration, and severity but can include papules, vesicles, erythema, exudate, xerosis, scaling, and lichenification. However, the most defining and universal symptom of AD is pruritus. Pruritus or itch, defined as an unpleasant urge to scratch, is problematic for many reasons, particularly its negative impact on quality of life. Despite the profoundly negative impact of pruritus on patients with AD, clinicians and researchers lack standardized and validated methods to objectively measure pruritus. The purpose of this review is to discuss emerging methods to assess pruritus in AD by describing objective patient-centered tools developed or enhanced over the last decade that can be utilized by clinicians and researchers alike.

METHODS

This review is based on a literature search in Medline, Embase, and Web of Science databases. The search was performed in February 2019. The keywords were used "pruritus," "itch," "atopic dermatitis," "eczema," "measurements," "tools," "instruments," "accelerometer," "wrist actigraphy," "smartwatch," "transducer," "vibration," "brain mapping," "magnetic resonance imaging," and "positron emission tomography." Only articles written in English were included, and no restrictions were set on study type. To focus on emerging methods, prioritization was given to results from the last decade (2009-2019).

RESULTS

The search yielded 49 results in PubMed, 134 results in Embase, and 85 results in Web of Science. Each result was independently reviewed in a standardized manner by two of the authors (M.S., K.L.), and disagreements between reviewers were resolved by consensus. Relevant findings were categorized into the following sections: video surveillance, acoustic surveillance, wrist actigraphy, smart devices, vibration transducers, and neurological imaging. Examples are provided along with descriptions of how each technology works, instances of use in research or clinical practice, and as applicable, reports of validation studies and correlation with other methods.

CONCLUSION

The variety of new and improved methods to evaluate pruritus in AD is welcomed by clinicians, researchers, and patients alike. Future directions include next-generation smart devices as well as exploring new territories, such as identifying biomarkers that correlate to itch and machine-learning programs to identify itch processing in the brain. As these efforts continue, it will be essential to remain patient-centered by developing techniques that minimize discomfort, respect privacy, and provide accurate data that can be used to better manage itch in AD.

Developing and adopting safe and effective digital biomarkers to improve patient outcomes

Biomarkers are physiologic, pathologic, or anatomic characteristics that are objectively measured and evaluated as an indicator of normal biologic processes, pathologic processes, or biological responses to therapeutic interventions. Recent advances in the development of mobile digitally connected technologies have led to the emergence of a new class of biomarkers measured across multiple layers of hardware and software. Quantified in ones and zeros, these "digital" biomarkers can support continuous measurements outside the physical confines of the clinical environment. The modular software-hardware combination of these products has created new opportunities for patient care and biomedical research, enabling remote monitoring and decentralized clinical trial designs. However, a systematic approach to assessing the quality and utility of digital biomarkers to ensure an appropriate balance between their safety and effectiveness is needed. This paper outlines key considerations for the development and evaluation of digital biomarkers, examining their role in clinical research and routine patient care.

Incorporating patient-reported symptom assessments into routine care for people with chronic kidney disease

In this issue of Clinical Kidney Journal, Brown and colleagues show that symptom burden is high across all stages of chronic kidney disease (CKD). Still, management of symptoms in kidney patients leaves room for improvement, which may partly stem from symptoms being underreported. The use of patient-reported questionnaires may facilitate a more systematic approach to symptom assessment, but to date, the majority of these instruments have been used only in the context of research studies. In this editorial, we review how systematic patient-reported symptom assessments can be incorporated in CKD care. We show examples from an initiative in the UK where 14 renal units explored how to collect and use symptom burden assessments as part of their routine ways of working. We also discuss how to move from paper-based questionnaires towards digital collection of patient-reported symptom data. Lastly, we introduce wearable and smartphone sensors as novel methods for collecting information to support and enrich symptom assessments while minimizing data collection burden.