Computer-Aided Detection (CADe) System with Optical Coherent Tomography for Melanin Morphology Quantification in Melasma Patients

Chen, I‐Ling; Wang, Yen‐Jen; Chang, Chang‐Cheng; Wu, Yu‐Hung; Lu, Chih-Wei; Shen, Jia-Wei; Ling, Hoe I.; Lin, Bor-Shyh; Chiang, Hsiu-Mei

doi:10.3390/diagnostics11081498

Cited by 12 publications

(18 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One study used a voting-based probabilistic linear discriminant analysis to classify non-tumorous skin pigmentation diseases, including melasma, with an accuracy of 67.7% for melasma [ 22 ]. Another study presented a spatial compounding-based denoising convolutional neural network for quantifying and evaluating melanin in melasma optical coherence tomography images [ 23 ]. However, there is still a lack of research on large training datasets and high accuracy diagnostic systems for melasma facial images.…”

Section: Discussionmentioning

confidence: 99%

An Intelligent Diagnostic Model for Melasma Based on Deep Learning and Multimode Image Input

Liu

Chen

Xue

et al. 2022

Dermatol Ther (Heidelb)

View full text Add to dashboard Cite

Introduction:The diagnosis of melasma is often based on the naked-eye judgment of physicians. However, this is a challenge for inexperienced physicians and non-professionals, and incorrect treatment might have serious consequences. Therefore, it is important to develop an accurate method for melasma diagnosis. The objective of this study is to develop and validate an intelligent diagnostic system based on deep learning for melasma images. Methods: A total of 8010 images in the VISIA system, comprising 4005 images of patients with melasma and 4005 images of patients without melasma, were collected for training and testing. Inspired by four high-performance structures (i.e., DenseNet, ResNet, Swin Transformer, and MobileNet), the performances of deep learning models in melasma and nonmelasma binary classifiers were evaluated. Furthermore, considering that there were five modes of images for each shot in VISIA, we fused these modes via multichannel image input in different combinations to explore whether multimode images could improve network performance. Results: The proposed network based on Den-seNet121 achieved the best performance with an accuracy of 93.68% and an area under the curve (AUC) of 97.86% on the test set for the melasma classifier. The results of the Gradientweighted Class Activation Mapping showed that it was interpretable. In further experiments, for the five modes of the VISIA system,

show abstract

Section: Discussionmentioning

confidence: 99%

An Intelligent Diagnostic Model for Melasma Based on Deep Learning and Multimode Image Input

Liu

Chen

Xue

et al. 2022

Dermatol Ther (Heidelb)

View full text Add to dashboard Cite

show abstract

“…For example, "confetti melanin" appears dense and is concentrated in the stratum spinosum, while "grain melanin" has a higher density in the dermoepidermal junction, demonstrating OCT's potential to classify melasma in a noninvasive approach. 33 Similarly, Pomerantz et al 103 used dynamic optical coherence tomography and found that dermal blood vessels were increased in size and flow at baseline with subsequent reduction in both measures after oral tranexamic acid, further supporting OCT for measuring treatment response in melasma. Although these studies were limited to a small cohort, they highlight OCT as a useful tool with the potential to study the microscopy features of melasma in an objective and noninvasive approach.…”

Section: Optical Coherence Tomography (Oct)mentioning

confidence: 93%

“…They also identified patterns that could help identify the location and depth of melanin within the dermis. For example, “confetti melanin” appears dense and is concentrated in the stratum spinosum, while “grain melanin” has a higher density in the dermoepidermal junction, demonstrating OCT's potential to classify melasma in a noninvasive approach 33 . Similarly, Pomerantz et al 103 used dynamic optical coherence tomography and found that dermal blood vessels were increased in size and flow at baseline with subsequent reduction in both measures after oral tranexamic acid, further supporting OCT for measuring treatment response in melasma.…”

Section: Melasmamentioning

confidence: 99%

Diagnostic tools for hyperpigmentation disorders in skin of color: An updated review

Pagan

Mitchell

Yousif

et al. 2022

Dermatological Reviews

View full text Add to dashboard Cite

Hyperpigmentation disorders are highly prevalent and disproportionately affect patients with skin of color. Studies suggest that over 65% of Black or African American patients haveexperienced at least one episode of hyperpigmentation due to skin damage or irritation. Notably, melasma and postinflammatory hyperpigmentation are the two most common forms of dyschromia in patients with skin of color. Additionally, otherconditionssuch as lichen planus and erythema dyschromicum perstans can have a more severe presentation and significant morbidity in this patient population. Thus, hyperpigmentation disorders can benefit greatly from prompt and accurate diagnosis that results in more precise management. In this review, we present the most updated information regarding dermoscopy, reflectance confocal, and conventional light microscopy in the diagnosis of hyperpigmentation disorders in skin of color. In addition, we summarize the benefits and pitfalls of some of these techniques, with a special emphasis on their accuracy and potential to assess favorable treatment outcomes. We also describe recent studies that have compared these diagnostic techniques across many disorders of hyperpigmentation and the practical findings that can be applied to everyday practice. We recognize that the cost of equipment, clinician training, and device availability are all critical factors when choosing the ideal diagnostic tool and provide varied approaches to improve accuracy. Today, diagnosing and treating hyperpigmentation skin conditions has become easier through technological advances.More reliable, noninvasive techniques such as dermoscopy and reflectance confocal microscopy show promise as adjuncts to guide laser and light therapies and monitor treatment response in dermatology clinics and clinical trials.

show abstract

“…14 Based on our previous work, all targets with a diameter >0.5 µm and a brightness level >153 gray scale were retained and were named as "Detected" melanin (D). 14 We further performed morphological operations on the binarized image considering the differences in the aggregation forms of melanin to explore targets with a certain degree of aggregation, and all the objects with an area over 8.42 µm 2 (about a circle with a diameter of 3.3 µm) were defined as confetti melanin (C) from deep learning networks for the best-differentiated quality. 14 Melanin with the area between the detected (D) melanin and confetti (C) melanin was calculated and termed granular (G) melanin.…”

Section: En Face Images For Epidermal Melanin Quantificationmentioning

confidence: 99%

Adaptability of melanocytes post ultraviolet stimulation in patients with melasma

Wang

Chang

et al. 2023

Lasers Surg Med

Self Cite

View full text Add to dashboard Cite

BackgroundDynamic in vivo changes in melanin in melasma lesions after exposure to ultraviolet (UV) irradiation have not been described.ObjectivesTo determine whether melasma lesions and nearby perilesions demonstrated different adaptive responses to UV irradiation and whether the tanning responses were different among different locations on face.MethodsWe collected sequential images from real‐time cellular resolution full‐field optical coherence tomography (CRFF‐OCT) at melasma lesions and perilesions among 20 Asian patients. Quantitative and layer distribution analyses for melanin were performed using a computer‐aided detection (CADe) system that utilizes spatial compounding‐based denoising convolutional neural networks.ResultsThe detected melanin (D) is melanin with a diameter >0.5 µm, among which confetti melanin (C) has a diameter of >3.3 µm and corresponds to a melanosome‐rich package. The calculated C/D ratio is proportional to active melanin transportation. Before UV exposure, melasma lesions had more detected melanin (p = 0.0271), confetti melanin (p = 0.0163), and increased C/D ratio (p = 0.0152) in the basal layer compared to those of perilesions. After exposure to UV irradiation, perilesions have both increased confetti melanin (p = 0.0452) and the C/D ratio (p = 0.0369) in basal layer, and this effect was most prominent in right cheek (p = 0.030). There were however no significant differences in the detected, confetti, or granular melanin areas before and after exposure to UV irradiation in melasma lesions in all the skin layers.ConclusionsHyperactive melanocytes with a higher baseline C/D ratio were noted in the melasma lesions. They were “fixed” on the plateau and were not responsive to UV irradiation regardless of the location on face. Perilesions retained adaptability with a dynamic response to UV irradiation, in which more confetti melanin was shed, mainly in the basal layer. Therefore, aggravating effect of UV on melasma was mainly due to UV‐responsive perilesions rather than lesions.

show abstract

Computer-Aided Detection (CADe) System with Optical Coherent Tomography for Melanin Morphology Quantification in Melasma Patients

Cited by 12 publications

References 33 publications

An Intelligent Diagnostic Model for Melasma Based on Deep Learning and Multimode Image Input

An Intelligent Diagnostic Model for Melasma Based on Deep Learning and Multimode Image Input

Diagnostic tools for hyperpigmentation disorders in skin of color: An updated review

Adaptability of melanocytes post ultraviolet stimulation in patients with melasma

Contact Info

Product

Resources

About