Trial Summary
What is the purpose of this trial?The study is conducted to determine if image-based computer grading can of acne, melasma, rosacea and seborrheic dermatitis correlate well to expert based clinical severity grading.
Do I need to stop my current medications for this trial?
The protocol does not specify if you need to stop your current medications.
What data supports the idea that Computer Image Analysis for Skin Conditions (also known as: No Intervention) is an effective treatment?
The available research shows that computer image analysis for skin conditions is effective because it can predict clinical management decisions accurately. One study found that using images to predict management decisions directly was more accurate than predicting the diagnosis first. This approach also reduced unnecessary procedures by 24.56%. Another study demonstrated that digital imaging for skin cancer diagnosis had almost complete agreement with traditional clinical consultations, showing its promise in teledermatology. Additionally, a facial skin analysis system on a handheld device showed good agreement with dermatologist assessments, supporting its potential use in clinical settings.
12345What safety data exists for computer image analysis in skin conditions?
The safety data for computer image analysis in skin conditions can be informed by various sources. The CAPER Registry collects adverse event data from dermatologic procedures, which can help identify safety issues. Safety of dermatology treatments is assessed through clinical trials, registries, and spontaneous reporting, providing a comprehensive understanding of safety profiles. The FDA evaluates safety using data from clinical trials, postmarketing reports, and registries, and continues to develop tools for risk assessment. These sources collectively contribute to understanding the safety of treatments in dermatology, including computer image analysis.
678910Is computer image analysis a promising treatment for skin conditions?
Yes, computer image analysis is a promising treatment for skin conditions. It helps doctors diagnose skin issues like melanoma by analyzing images of the skin. This technology can detect changes in skin lesions over time, which is important for early cancer detection. It can also differentiate between benign and malignant lesions, making it a useful tool for doctors, especially those who are not experts in dermatology.
1112131415Eligibility Criteria
This trial is for adults with acne, rosacea, melasma, or seborrheic dermatitis. Participants must be able to give consent and should not have facial markings like piercings or tattoos that could affect the imaging process on the day of facial photography.Inclusion Criteria
I have acne, rosacea, melasma, or seborrheic dermatitis.
Exclusion Criteria
I am unable to give consent for medical procedures.
Artificial facial markings on day of facial photography (such as piercings and tattoos) that may interfere with imaging in the opinion of the investigator
Prisoners
Participant Groups
The study aims to see if computer-based image grading can accurately assess the severity of skin conditions such as acne, melasma, rosacea, and seborrheic dermatitis compared to expert clinical evaluations.
4Treatment groups
Experimental Treatment
Group I: Seborrheic DermatitisExperimental Treatment1 Intervention
Grading of seborrheic dermatitis with Seborrheic Dermatitis Area Severity Index score
Group II: RosaceaExperimental Treatment1 Intervention
Inflammatory lesion count
Group III: MelasmaExperimental Treatment1 Intervention
Pigment intensity and distribution with use of Melasma Area Severity Index
Group IV: Acne vulgarisExperimental Treatment1 Intervention
Inflammatory and non-inflammatory lesion counts
Find a Clinic Near You
Research Locations NearbySelect from list below to view details:
Integrative Skin and ResearchSacramento, CA
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Who Is Running the Clinical Trial?
Integrative Skin Science and ResearchLead Sponsor
Codex LabsCollaborator
References
Feasibility and diagnostic accuracy of teledermatology in Swiss primary care: process analysis of a randomized controlled trial. [2015]To test feasibility and diagnostic accuracy of dermatologist's feedback based on digital images of skin lesions collected in Swiss primary care.
Predicting the clinical management of skin lesions using deep learning. [2021]Automated machine learning approaches to skin lesion diagnosis from images are approaching dermatologist-level performance. However, current machine learning approaches that suggest management decisions rely on predicting the underlying skin condition to infer a management decision without considering the variability of management decisions that may exist within a single condition. We present the first work to explore image-based prediction of clinical management decisions directly without explicitly predicting the diagnosis. In particular, we use clinical and dermoscopic images of skin lesions along with patient metadata from the Interactive Atlas of Dermoscopy dataset (1011 cases; 20 disease labels; 3 management decisions) and demonstrate that predicting management labels directly is more accurate than predicting the diagnosis and then inferring the management decision ([Formula: see text] and [Formula: see text] improvement in overall accuracy and AUROC respectively), statistically significant at [Formula: see text]. Directly predicting management decisions also considerably reduces the over-excision rate as compared to management decisions inferred from diagnosis predictions (24.56% fewer cases wrongly predicted to be excised). Furthermore, we show that training a model to also simultaneously predict the seven-point criteria and the diagnosis of skin lesions yields an even higher accuracy (improvements of [Formula: see text] and [Formula: see text] in overall accuracy and AUROC respectively) of management predictions. Finally, we demonstrate our model's generalizability by evaluating on the publicly available MClass-D dataset and show that our model agrees with the clinical management recommendations of 157 dermatologists as much as they agree amongst each other.
A pilot trial of digital imaging in skin cancer. [2017]We have used inexpensive off-the-shelf equipment for store-and-forward teledermatology and compared the precision and accuracy of digital image consultations with conventional, clinic-based consultations. Thirteen lesions were studied on 12 patients referred to a dermatology clinic for a suspected skin cancer. Patients were examined by two dermatologists. Subsequently, digital images were examined by two different dermatologists. There was almost complete agreement, both among and between the clinical and digital examiners, on different diagnosis and biopsy recommendations. Agreement on the single most likely diagnosis was also good. Digital imaging shows promise in teledermatology.
Automated detection of nonmelanoma skin cancer using digital images: a systematic review. [2020]Computer-aided diagnosis of skin lesions is a growing area of research, but its application to nonmelanoma skin cancer (NMSC) is relatively under-studied. The purpose of this review is to synthesize the research that has been conducted on automated detection of NMSC using digital images and to assess the quality of evidence for the diagnostic accuracy of these technologies.
Initial validation of a new device for facial skin analysis. [2022]The field of dermatology is met with many subjective analysis methods. Due to the relative nature of subjective analysis methods, objective analysis methods with greater accuracy and reliability were developed. Many of these devices are either inaccessible to patients without being a part of a clinical trial, bulky, or costly. However, with the advances in artificial intelligence and handheld devices, measurement methods have become simplified. The purpose of our study was to validate an objective skin analysis software available on a handheld device by comparing it to a board-certified dermatologist's assessment. Participants of various ages and skin types were analyzed with the facial analysis system on an iPad Pro. The same photographs were ranked by a physician based on 14 common skin characteristics. The facial analysis system and the physician's rankings had a good agreement rate of 69%. The greatest agreement rates were with the assessment of erythema (83.7%) and wrinkles (81.6%) and the lowest with oiliness (53.1%). The analysis system's high re-test reliability and good agreement rates with physician assessment support its potential use in the clinical setting.
How is safety of dermatology drugs assessed: trials, registries, and spontaneous reporting. [2021]Introduction: Skin conditions are common and highly varied in their etiology; therefore, a diverse array of therapeutics are utilized. Drug safety studies in dermatology can be challenging as there are over 3000 diagnoses to consider. As a result, dermatologists rely on data from multiple sources including clinical trials and real-world evidence.Areas covered: In this review, we cover the main sources of safety data available, their strengths and weaknesses and how dermatologists should utilize such data. We use real-world examples of the different types of adverse events reported and how they are best captured by either randomized controlled trials or post-marketing pharmacovigilance methods. With multiple new therapies in dermatology, such as dupilumab for atopic dermatitis and janus-kinase inhibitors for alopecia areata the specialty is awash with evolving high-level evidence for their use. It is important to understand the optimal way to assess safety from trials but also appreciate the need for ongoing capture of safety data in clinical practice.Expert opinion: In dermatology, there is a plethora of conditions to treat and clinical trials, post-marketing surveillance, such as drug registries and spontaneous reporting, all enable dermatologists to gain a more comprehensive understanding of the safety profiles of drugs being used.
Detecting adverse events in dermatologic surgery. [2019]Despite increasing awareness of and public attention to patient safety, little is documented about how adverse events (AEs) can or should be monitored in dermatologic surgery. Data to address this shortcoming are needed, although well-defined methodologies have yet to be implemented. OBJECTIVE To summarize current strategies in detecting adverse outcomes of dermatologic surgical procedures.
Finding, evaluating, and managing drug-related risks: approaches taken by the US Food and Drug Administration (FDA). [2009]Marketed pharmaceuticals are evaluated for safety by the US Food and Drug Administration (FDA) throughout the life cycle of the products. The FDA uses data from controlled clinical trials, from postmarketing case reports reported to the FDA's Adverse Event Reporting System, from epidemiological studies, and from registries to evaluate the safety of approved products. For some products, including some products used in dermatologic medicine, risks become apparent during the postmarketing period that require additional measures beyond product labeling and routine pharmacovigilance. The FDA continues to seek additional tools to assess risk, including pharmacogenomic biomarkers for adverse drug reactions and the use of large medical record and epidemiological databases for the systematic detection and characterization of drug-associated safety outcomes.
The Cutaneous Procedures Adverse Events Reporting (CAPER) Registry. [2022]The CAPER Registry is a voluntary, national safety reporting program that gathers patients' adverse events encountered during dermatologic procedures. This registry is intended as an aid for practitioners, patients, industry, and government regulators, and aims to facilitate safety monitoring for the specialty by identifying resource, process, education, and other systemic gaps associated with adverse events, as well as any potential risk factors for adverse events. CAPER will provide new or corroborating information to help dermatologists improve clinical practices, improve safety and effectiveness, and treat and prevent adverse events. The data generated will also help industry partners and regulatory bodies prevent adverse events from going unnoticed.
Recommended Diagnostic Approach to Documenting and Reporting Skin Findings of Nonhuman Primates from Regulatory Toxicity Studies. [2018]Cutaneous adverse drug reactions (CADRs) in patients are not uncommon, and they are difficult to predict from nonclinical safety studies. Nonhuman primates (NHPs) are predestinated for a high predictivity of adverse drug reactions, and we postulate that this may also be true for CADRs, if skin findings in NHPs are thoroughly worked up, following the diagnostic approach in clinical veterinary dermatology. This article proposes a systematic approach to describe, analyze, and report skin findings that occur in NHP toxicity studies. Implementing this approach may increase the likelihood to differentiate between test item-related cutaneous findings and those that are independent of the test item. This will eventually result in increased relevance of skin findings identified in the scope of an NHP regulatory toxicity study for the risk assessment process to safeguard patients in clinical trials and beyond.
Development and Narrow Validation of Computer Vision Approach to Facilitate Assessment of Change in Pigmented Cutaneous Lesions. [2023]The documentation of the change in the number and appearance of pigmented cutaneous lesions over time is critical to the early detection of skin cancers and may provide preliminary signals of efficacy in early-phase therapeutic prevention trials for melanoma. Despite substantial progress in computer-aided diagnosis of melanoma, automated methods to assess the evolution of lesions are relatively undeveloped. This report describes the development and narrow validation of mathematical algorithms to register nevi between sequential digital photographs of large areas of skin and to align images for improved detection and quantification of changes. Serial posterior truncal photographs from a pre-existing database were processed and analyzed by the software, and the results were evaluated by a panel of clinicians using a separate Extensible Markup Language‒based application. The software had a high sensitivity for the detection of cutaneous lesions as small as 2 mm. The software registered lesions accurately, with occasional errors at the edges of the images. In one pilot study with 17 patients, the use of the software enabled clinicians to identify new and/or enlarged lesions in 3‒11 additional patients versus the unregistered images. Automated quantification of size change performed similarly to that of human raters. These results support the further development and broader validation of this technique.
An open Internet platform to distributed image processing applied to dermoscopy. [2007]Proprietary systems for dermoscopy images analysis are available to improve the diagnosis and follow-up of the pigmented skin lesions. Their performance seems comparable with that of a human expert. Progress in computer-aided classification of melanocytic lesions depends notably on judicious choices of the algorithms dedicated to the extraction of signs from the dermoscopy images and of the method which combines these signs to classify the lesions. To allow the researcher's community to benefit from their large set of elementary algorithms already available for dermoscopy, we set up a system accessible through the Internet which: allows the engineers to register their algorithms while preserving their secrecy: their programs run on their own server; lets a user to define its own sequence of image analysis and to apply it to its images: the system automatically calls the appropriate remote programs; makes possible and stimulates the synergy of worldwide researchers in order to validate algorithms of images analysis best suited to achieve the correct diagnosis and to track the malignant melanoma; makes these techniques available to the greatest number of users through the Web and thus to support a mass screening; reduces the maintenance of the system to the minimum: it requires users only an Internet browser and engineers to follow a simple widespread standardised interface for distributed programs. Various problems should be addressed: the lack of standardisation of images acquisition: algorithms based on relative colours are best suited to this system; the copyrights on images and algorithms; charging the use of remote computer resources. This system allows for an international collaborative work in the fight against the malignant melanoma by offering a conceptual and technical platform of teledermoscopy. It is intended to support synergy between the engineers and the users implied in the diagnosis and teaching of dermoscopy.
Differentiation of benign pigmented skin lesions with the aid of computer image analysis: a novel approach. [2021]The differential diagnosis of common pigmented skin lesions is important in cosmetic dermatology. The computer aided image analysis would be a potent ancillary diagnostic tool when patients are hesitant to undergo a skin biopsy.
A possible new tool for clinical diagnosis of melanoma: the computer. [2019]The analysis of cutaneous melanoma images by two coupled computers (IBM 7350/4361) was carried out on twenty color slides. Each color slide was digitized with a spatial reduction of 25 X 25 microns. Classic technics of digital image analysis and new algorithms were used to improve the contrast on the full image or a portion of it, contrast a skin lesion with statistical information deduced from another lesion, evaluate the shape of the lesion, the roughness of the surface, and the transition region from the lesion to the normal skin, and analyze a lesion from the chromatic point of view. The theoretical reasons of interest are to have an objective method that is easy to standardize and reliably repeatable and to be able to analyze details not perceivable by the human eye. If the same technic are used in the evaluation of histologic characteristics of the lesions, a chance of making much more sophisticated clinicopathologic correlations will be available. The system needs to be improved at the technical level so that the response time of acquisition of the digitized images is shortened by the use of a digital television camera and the development of new computer programs to be run on a small computer. Evaluation of the system's sensitivity and specificity and an adequate clinical trial are needed.
Results obtained by using a computerized image analysis system designed as an aid to diagnosis of cutaneous melanoma. [2019]Results obtained using a computerized image analysis system as an aid to clinical diagnosis of melanoma are reported. The system comprises a colour television camera connected through a digitizing board to a 386 personal computer. By means of original algorithms able to measure the shape, the colours and texture of a pigmented lesion of the skin, the system provides eight on/off indicators that are matched with the histological diagnosis to identify benign and malignant pigmented lesions. The chances that a given lesion is malignant increase with the increasing number of positive indicators. The training field of the system was constituted of images and data of 169 cutaneous lesions in 165 patients. Taking two positive indicators as the threshold between pigmented benign and malignant lesions, the efficiency of the system is 0.98, the positive predictive value is 0.45 and the negative predictive value is 0.95. These values were confirmed in a series of 44 pigmented lesions, 10 of which were melanoma, that constitute the present test series. The authors conclude that this computerized image analysis system should be regarded as a useful aid to diagnosis for a non-expert clinician. The system limit is transformation within a naevus.