IMPROVE 1.0: Individual Monitoring of Psoriasis Activity by Regular Online App Questionnaires and Outpatient Visits

Garzorz‐Stark, Natalie; Beicht, Sarah; Baghin, V.; Stark, Sebastian P; Biedermann, Tilo; Lauffer, Felix

doi:10.3389/fmed.2021.648233

Cited by 5 publications

(4 citation statements)

References 23 publications

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“…A study (Garzorz-stark et al, 2021) investigated the potential of a new smart phone app to monitor psoriasis disease activity. 15 Patients entered self-PASI, life quality, stress questionnaires and lifestyle factors over 12 months and this was compared with data collected by dermatologists. Patients feedback on use of app was obtained.…”

Section: Resultsmentioning

confidence: 99%

Telemedicine and e-Health in the Management of Psoriasis: Improving Patient Outcomes – A Narrative Review

Havelin¹,

Hampton²

2022

PTT

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The role of technology in dermatology is expanding. Telemedicine and eHealth are increasingly being used by doctors and patients in the management of psoriasis. This is a narrative review of the literature relating to the use of digital technology in the management of psoriasis. We divided psoriasis e-health into three areas: mobile phone applications, teledermatology and artificial Intelligence (AI). Literature searches were conducted using the following databases: Pubmed, Google Scholar, Scopus, both app stores using App Annie platform. The following words were used in searches; psoriasis, dermatology, mobile phone application, application, app, smartphone, teledermatology, telemedicine, artificial intelligence, AI, machine learning in various combinations. We defined three key questions, one relating to each of the 3 areas. We then reviewed the relevant papers found in the searches and selected the papers of highest research quality and greatest relevance in order to answer the questions. In addition, for apps, operating systems for IOS and android devices were searched for apps containing the key word “psoriasis” in the title using the app analytic website www.appannie.com on 08/11/21. Research publications linked to these apps were reviewed.

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Section: Resultsmentioning

confidence: 99%

Telemedicine and e-Health in the Management of Psoriasis: Improving Patient Outcomes – A Narrative Review

Havelin¹,

Hampton²

2022

PTT

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“…While applications have been developed for digitally tracking psoriatic disease, [51][52][53] they largely focus on questionnaire-based patient reported outcomes. To our knowledge, ours is the first application using ubiquitous smartphone sensor technology for patients to remotely measure their psoriatic disease phenotypes, including detection of nail psoriasis and a continuous variable outcome measure of joint tenderness and enthesitis based on range of motion (ROM).…”

Section: Discussionmentioning

confidence: 99%

Clinical validation of digital biomarkers and machine learning models for remote measurement of psoriasis and psoriatic arthritis

Webster

Haberman

Chada

et al. 2022

Preprint

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Background Psoriasis and psoriatic arthritis are common immune-mediated inflammatory conditions that primarily affect the skin, joints and entheses and can lead to significant disability and worsening quality of life. Although early recognition and treatment can prevent the development of permanent damage, psoriatic disease remains underdiagnosed and undertreated due in part to the disparity between disease prevalence and relative lack of access to clinical specialists in dermatology and rheumatology. Remote patient self-assessment aided by smartphone sensor technology may be able to address these gaps in care, however, these innovative disease measurements require robust clinical validation. Methods We developed smartphone-based assessments, collectively named the Psorcast suite, that can be self-administered to measure cutaneous and musculoskeletal signs and symptoms of psoriatic disease. The image and motion sensor data collected by these assessments was processed to generate digital biomarkers or machine learning models to detect psoriatic disease phenotypes. To evaluate these digital endpoints, a cross-sectional, in-clinic validation study was performed with 92 participants across two specialized academic sites consisting of healthy controls and participants diagnosed with psoriasis and/or psoriatic arthritis. Findings In the domain of skin disease, digital patient assessment of percent body surface area (BSA) affected with psoriasis demonstrated very strong concordance (CCC = 0.94, [95%CI = 0.91-0.96]) with physician-assessed BSA. Patient-captured psoriatic plaque photos were remotely assessed by physicians and compared to in-clinic Physician Global Assessment parameters for the same plaque with fair to moderate concordance (CCCerythema=0.72 [0.59-0.85]; CCCinduration=0.72 [0.62-0.82]; CCCscaling=0.60 [0.48-0.72]). Arm range of motion was measured by the Digital Jar Open assessment to classify physician-assessed upper extremity involvement with joint tenderness or enthesitis, demonstrating an AUROC = 0.68 (0.47-0.85). Patient-captured hand photos were processed with object detection and deep learning models to classify clinically-diagnosed nail psoriasis with an accuracy of 0.76, which is on par with remote physician rating of nail images (avg. accuracy = 0.63) with model performance maintaining accuracy when raters were too unsure or image quality was too poor for a remote assessment. Interpretation The Psorcast digital assessments, performed by patient self-measurement, achieve significant clinical validity when compared to in-person physical exams. These assessments should be considered appropriately validated for self-monitoring and exploratory research applications, particularly those that require frequent, remote disease measurements. However, further validation in larger cohorts will be necessary to demonstrate robustness and generalizability across populations for use in evidence-based medicine or clinical trial settings. The smartphone software and analysis pipelines from the Psorcast suite are open source and available to the scientific community. Funding This work is funded by the Psorcast Digital Biomarker Consortium consisting of Sage Bionetworks, Psoriasis and Psoriatic Arthritis Centers for Multicenter Advancement Network (PPACMAN), Novartis, UCB, Pfizer, and Janssen Pharmaceuticals. J.U.S work was supported by the Snyder Family Foundation and the Riley Family Foundation.

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“…Patients entered life quality, stress questionnaires, self‐PASI, and lifestyle factors over 12 months, and the data was compared with dermatologists' collected data. The application can detect lifestyle changes preceding a shift in PASI, potentially serving as an alert system by drawing patients' attention to increasing stress levels or weight gain and preventing a flare 83 . Moreover, patient‐reported outcome measures (PROMs) have gained popularity in clinical practice.…”

Section: Ai Application In Psoriasis: Where We Are Nowmentioning

confidence: 99%

“…The application can detect lifestyle changes preceding a shift in PASI, potentially serving as an alert system by drawing patients' attention to increasing stress levels or weight gain and preventing a flare. 83 Moreover, patient-reported outcome measures (PROMs) have gained popularity in clinical practice. PROMs assess subjective aspects of patient conditions and aid in evaluating disease and treatment burden from patient viewpoints.…”

Section: Prediction Candidate Drugsmentioning

confidence: 99%

Artificial intelligence in psoriasis: Where we are and where we are going

Liu,

Wang,

et al. 2023

Experimental Dermatology

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Artificial intelligence (AI) is a field of computer science that involves the development of programs designed to replicate human cognitive processes and the analysis of complex data. In dermatology, which is predominantly a visual‐based diagnostic field, AI has become increasingly important in improving professional processes, particularly in the diagnosis of psoriasis. In this review, we summarized current AI applications in psoriasis: (i) diagnosis, including identification, classification, lesion segmentation, lesion severity and area scoring; (ii) treatment, including prediction treatment efficiency and prediction candidate drugs; (iii) management, including e‐health and preventive medicine. Key challenges and future aspects of AI in psoriasis were also discussed, in hope of providing potential directions for future studies.

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IMPROVE 1.0: Individual Monitoring of Psoriasis Activity by Regular Online App Questionnaires and Outpatient Visits

Cited by 5 publications

References 23 publications

Telemedicine and e-Health in the Management of Psoriasis: Improving Patient Outcomes – A Narrative Review

Telemedicine and e-Health in the Management of Psoriasis: Improving Patient Outcomes – A Narrative Review

Clinical validation of digital biomarkers and machine learning models for remote measurement of psoriasis and psoriatic arthritis

Artificial intelligence in psoriasis: Where we are and where we are going

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