Dermatological Images Research Articles

Where Artificial Intelligence Can Take Us in the Management and Understanding of Cancerization Fields.

Squamous cell carcinoma and its precursor lesion actinic keratosis are often found together in areas of skin chronically exposed to sun, otherwise called cancerisation fields. The clinical assessment of cancerisation fields and the correct diagnosis of lesions within these fields is usually challenging for dermatologists. The recent adoption of skin cancer diagnostic imaging techniques, particularly LC-OCT, helps clinicians in guiding treatment decisions of cancerization fields in a non-invasive way. The combination of artificial intelligence and non-invasive skin imaging opens up many possibilities as AI can perform tasks impossible for humans in a reasonable amount of time. In this text we review past examples of the application of AI to dermatological images for actinic keratosis/squamous cell carcinoma diagnosis, and we discuss about the prospects of the application of AI for the characterization and management of cancerization fields.

Comparative quantifications and morphological monitoring of the topical treatment approach for onychomycosis-affected in vivo toenail using optical coherence tomography: A case study

Onychomycosis is one of the most common toenail fungal infections that affect the quality of life of many patients. Long-term and noninvasive monitoring of morphological changes of onychomycosis-affected nail plate aids the medication process and provides comfort for patients. However, existing medical and dermatological imaging methods have various types of limitations in nail investigation due to low resolution, lack of volumetric data, the necessity of highly trained personnel for image analysis, and the variety of protocols. In this study, qualitative monitoring-based quantitative assessments were performed to assess the morphological changes of onychomycosis-affected toenail for 15 consecutive weeks using high-resolution optical coherence tomography (OCT). Layer intensity and surface roughness measuring algorithms were applied to two-dimensional OCT cross-sectional images to detect gradual changes in the morphological structure of the diseased toenail. A depth intensity profile and the angle formed between the nail plate and nail fold were also used to analyze the thickness and shape of the toenail plates, respectively. The quantitative and morphological monitoring results revealed significant changes in the toenail structure before and during the treatment process, confirming the healing of the diseased toenail. Therefore, the proposed noninvasive optical analysis approach can be applied to monitor nail abnormalities and evaluate the process of diseased toenail medication.

A dataset of skin lesion images collected in Argentina for the evaluation of AI tools in this population

In recent years, numerous dermatological image databases have been published to make possible the development and validation of artificial intelligence-based technologies to support healthcare professionals in the diagnosis of skin diseases. However, the generation of these datasets confined to certain countries as well as the lack of demographic information accompanying the images, prevents having a real knowledge of in which populations these models could be used. Consequently, this hinders the translation of the models to the clinical setting. This has led the scientific community to encourage the detailed and transparent reporting of the databases used for artificial intelligence developments, as well as to promote the formation of genuinely international databases that can be representative of the world population. Through this work, we seek to provide details of the processing stages of the first public database of dermoscopy and clinical images created in a hospital in Argentina. The dataset comprises 1,616 images corresponding to 1,246 unique lesions collected from 623 patients.

Application and Comparison of Dermoscopy and Reflectance Confocal Microscopy in the Target Treatment of Genital Lichen Sclerosus: A Single-Arm Prospective Study.

The treatment of genital lichen sclerosus (GLS) remains challenging. Baricitinib has been introduced in the treatment of GLS, but there's no imaging evaluation for GLS patients treated with it. No comparison of dermoscopy and reflectance confocal microscopy (RCM) assessments in GLS has been conducted. We performed this study to evaluate the efficacy and safety of baricitinib for GLS and to compare the value of dermoscopy and RCM assessments in GLS. Participants were treated with baricitinib for 6months and assessed at week 0, 2, 4, 6, 8, and every 4weeks for the next 16weeks. All patients were evaluated for clinical, dermoscopic, and RCM variables, with numeric scores assigned to each parameter. Twenty-six GLS patients were included in this study. All patients achieved Investigator's Global Assessment score ≤ 1 (with ≥ 2-grade improvement) at week 20. The scores of pruritus and pain decreased since week 2 (both P < 0.05). The DLQI and VQLI scores significantly decreased since week 4 (both P < 0.0001). White structureless areas improved at week 2 and white shiny streaks and follicular plugs improved at week 4 under dermoscopic examination. Vessels (P < 0.001) and brown structureless areas (P = 0.003) increased at week 8. In RCM, inflammatory cells count significantly decreased at week 2 (100.03 ± 33.24, P < 0.0001), with substantial regression at week 8 (16.98 ± 5.54, P < 0.0001). Epidermal thickness increased at week 12 (157.44 ± 37.87μm versus 134.13 ± 36.60μm, P = 0.0284). Irregular papillae, spongiosis, and fiber structures improved at week 20, week 4, and week 6 (all P < 0.01). Transient hypercholesterolemia (11.54%), thrombocytosis (7.69%), and elevated alanine aminotransferase (7.69%) occurred during treatment. Both dermoscopy and RCM can be useful and non-invasive adjuvant tools for the evaluation and therapeutic monitoring of GLS. We recommended white structureless areas under dermoscopy and inflammatory cells count under RCM as variables for dermatologic imaging evaluation for GLS. Baricitinib is effective and safe for GLS, while randomized controlled trials are warranted.

Generation of a Melanoma and Nevus Data Set From Unstandardized Clinical Photographs on the Internet

Artificial intelligence (AI) training for diagnosing dermatologic images requires large amounts of clean data. Dermatologic images have different compositions, and many are inaccessible due to privacy concerns, which hinder the development of AI. To build a training data set for discriminative and generative AI from unstandardized internet images of melanoma and nevus. In this diagnostic study, a total of 5619 (CAN5600 data set) and 2006 (CAN2000 data set; a manually revised subset of CAN5600) cropped lesion images of either melanoma or nevus were semiautomatically annotated from approximately 500 000 photographs on the internet using convolutional neural networks (CNNs), region-based CNNs, and large mask inpainting. For unsupervised pretraining, 132 673 possible lesions (LESION130k data set) were also created with diversity by collecting images from 18 482 websites in approximately 80 countries. A total of 5000 synthetic images (GAN5000 data set) were generated using the generative adversarial network (StyleGAN2-ADA; training, CAN2000 data set; pretraining, LESION130k data set). The area under the receiver operating characteristic curve (AUROC) for determining malignant neoplasms was analyzed. In each test, 1 of the 7 preexisting public data sets (total of 2312 images; including Edinburgh, an SNU subset, Asan test, Waterloo, 7-point criteria evaluation, PAD-UFES-20, and MED-NODE) was used as the test data set. Subsequently, a comparative study was conducted between the performance of the EfficientNet Lite0 CNN on the proposed data set and that trained on the remaining 6 preexisting data sets. The EfficientNet Lite0 CNN trained on the annotated or synthetic images achieved higher or equivalent mean (SD) AUROCs to the EfficientNet Lite0 trained using the pathologically confirmed public data sets, including CAN5600 (0.874 [0.042]; P = .02), CAN2000 (0.848 [0.027]; P = .08), and GAN5000 (0.838 [0.040]; P = .31 [Wilcoxon signed rank test]) and the preexisting data sets combined (0.809 [0.063]) by the benefits of increased size of the training data set. The synthetic data set in this diagnostic study was created using various AI technologies from internet images. A neural network trained on the created data set (CAN5600) performed better than the same network trained on preexisting data sets combined. Both the annotated (CAN5600 and LESION130k) and synthetic (GAN5000) data sets could be shared for AI training and consensus between physicians.

40791 Analysis of fine-art photography to enhance observational skills measured through dermatological images

40791 Analysis of fine-art photography to enhance observational skills measured through dermatological images

Leveraging Machine Learning for Accurate Detection and Diagnosis of Melanoma and Nevi: An Interdisciplinary Study in Dermatology.

This study explores the application of machine learning and deep learning algorithms to facilitate the accurate diagnosis of melanoma, a type of malignant skin cancer, and benign nevi. Leveraging a dataset of 793 dermatological images from the Kaggle online platform (Google LLC, Mountain View, California, United States), we developed a model that can accurately differentiate between these lesions based on their distinctive features. The dataset was divided into training (80%), validation (10%), and testing (10%) sets to optimize model performance and ensure its generalizability. Our findings demonstrate the potential of machine learning algorithms in enhancing the efficiency and accuracy of melanoma and nevi detection, with the developed model exhibiting robust performance metrics. Nonetheless, limitations exist due to the potential lack of comprehensive representation of melanoma and nevi cases in the dataset, and variations in image quality and acquisition methods, which may influence the model's performance in real-world clinical settings. Therefore, further research, validation studies, and integration into clinical practice are necessary to ensure the reliability and generalizability of these models. This study underscores the promise of artificial intelligence in advancing dermatologic diagnostics, aiming to improve patient outcomes by supporting early detection and treatment initiation for melanoma.

Automated detection and classification of psoriasis types using deep neural networks from dermatology images

Automated detection and classification of psoriasis types using deep neural networks from dermatology images

Using artificial intelligence on dermatology conditions in Uganda: a case for diversity in training data sets for machine learning.

In pursuit of applying universal non-biased Artificial Intelligence (AI) in healthcare, it is essential that data from different geographies are represented. To assess the diagnostic performance of an AI-powered dermatological algorithm called Skin Image Search on Fitzpatrick 6 skin type (dark skin) dermatological conditions. 123 dermatological images selected from a total of 173 images were retrospectively extracted from the electronic database of a Ugandan telehealth company, The Medical Concierge Group (TMCG) after getting their consent. Details of age, gender, and dermatological clinical diagnosis were analysed using R on R studio software to assess the diagnostic accuracy of the AI app along with disease diagnosis and body part. Predictability levels of the AI app were graded on a scale of 0 to 5, where 0- no prediction was made and 1-5 demonstrated a reduction incorrect diagnosis prediction rate of the AI. 76 (62%) of the dermatological images were from females and 47 (38%) from males. Overall diagnostic accuracy of the AI app on black dermatological conditions was low at 17% (21 out of 123 predictable images) compared to 69.9% performance on Caucasian skin type as reported from the training results. There were varying predictability levels correctness i.e., 1-8.9%, 2-2.4%, 3-2.4%, 4-1.6%, 5-1.6% with performance along individual diagnosis highest with dermatitis (80%). There is need for diversity of image datasets used to train dermatology algorithms for AI applications to increase accuracy across skin types and geographies.

Cutaneous Squamous Cell Carcinoma: An Up-to-Date Comprehensive Review with a Focus on Contemporary Optical Imaging Diagnostic Modalities

Cutaneous squamous cell carcinoma (cSCC) arises from the abnormal proliferation of keratinocytes of the epidermis, most commonly due to UV-light-induced DNA damage. Although histopathological assessment is the gold standard for diagnosing cSCC, nascent optical imaging diagnostic modalities enable clinicians to perform “optical or virtual biopsy” in real-time. We aim to report advances in optical imaging diagnostics for cSCC, along with an updated review of the literature. A comprehensive literature review was performed using PubMed, Embase, and Cochrane databases for manuscripts published from 2008 to 2022. The search yielded a total of 9581 articles, out of which 136 relevant articles were included in the literature review after fulfilling screening and eligibility criteria. This review highlights the current optical imaging devices used for diagnosing cSCC and their diagnostic features. These devices include in vivo and ex vivo reflectance confocal microscopy, optical coherence tomography, line-field confocal optical coherence tomography, multiphoton tomography, and high-frequency ultrasonography. Although surgical excision or Mohs micrographic surgery is considered the gold standard, the latest developments in nonsurgical management of cSCC are discussed. Based on he review of the literature, we conclude that contemporary optical imaging devices such as confocal microscopy, optical coherence tomography, line-field confocal optical coherence tomography and multiphoton tomography have revolutionized real-time diagnostic imaging in dermatology, particularly within the realm of skin cancer. These devices enable rapid diagnoses and allow for a faster initiation of therapy. The application of newer imaging devices to cSCC management may benefit high-risk patients (e.g., chronic UV radiation exposure or organ transplant recipients) or patients with multifocal cSCC, for whom multiple biopsies would be impractical, thus avoiding unnecessary biopsies. Together with dermoscopy, optical imaging technologies can help to improve the efficiency of diagnosis by reducing the turnaround time and the need for extensive laboratory processing resources.

Automatic body part identification in real-world clinical dermatological images using machine learning.

Dermatological conditions are prevalent across all population sub-groups. The affected body part is of importance to their diagnosis, therapy, and research. The automatic identification of body parts in dermatological clinical pictures could therefore improve clinical care by providing additional information for clinical decision-making algorithms, discovering hard-to-treat areas, and research by identifying new patterns of disease. In this study, we used 6,219 labelled dermatological images from our clinical database, which were used to train and validate a convolutional neural network. As a use case, qualitative heatmaps for the body part distribution in common dermatological conditions was generated using this system. The algorithm reached a mean balanced accuracy of 89% (range 74.8%-96.5%). Non-melanoma skin cancer photos were mostly of the face and torso, while hotspots of eczema and psoriasis image distribution included the torso, legs, and hands. The accuracy of this system is comparable to the best to-date published algorithms for image classification challenges, suggesting this algorithm could boost diagnosis, therapy, and research of dermatological conditions.

Classification of clinical skin lesions with double-branch networks.

Malignant skin lesions pose a great threat to the health of patients. Due to the limitations of existing diagnostic techniques, such as poor accuracy and invasive operations, malignant skin lesions are highly similar to other skin lesions, with low diagnostic efficiency and high misdiagnosis rates. Automatic medical image classification using computer algorithms can effectively improve clinical diagnostic efficiency. However, existing clinical datasets are sparse and clinical images have complex backgrounds, problems with noise interference such as light changes and shadows, hair occlusions, etc. In addition, existing classification models lack the ability to focus on lesion regions in complex backgrounds. In this paper, we propose a DBN (double branch network) based on a two-branch network model that uses a backbone with the same structure as the original network branches and the fused network branches. The feature maps of each layer of the original network branch are extracted by our proposed CFEBlock (Common Feature Extraction Block), the common features of the feature maps between adjacent layers are extracted, and then these features are combined with the feature maps of the corresponding layers of the fusion network branch by FusionBlock, and finally the total prediction results are obtained by weighting the prediction results of both branches. In addition, we constructed a new dataset CSLI (Clinical Skin Lesion Images) by combining the publicly available dataset PAD-UFES-20 with our collected dataset, the CSLI dataset contains 3361 clinical dermatology images for six disease categories: actinic keratosis (730), cutaneous basal cell carcinoma (1136), malignant melanoma (170) cutaneous melanocytic nevus (391), squamous cell carcinoma (298) and seborrheic keratosis (636). We divided the CSLI dataset into a training set, a validation set and a test set, and performed accuracy, precision, sensitivity, specificity, f1score, balanced accuracy, AUC summary, visualisation of different model training, ROC curves and confusion matrix for various diseases, ultimately showing that the network performed well overall on the test data. The DBN contains two identical feature extraction network branches, a structure that allows shallow feature maps for image classification to be used with deeper feature maps for information transfer between them in both directions, providing greater flexibility and accuracy and enhancing the network's ability to focus on lesion regions. In addition, the dual branch structure of DBN provides more possibilities for model structure modification and feature transfer, and has great potential for development.

Visual Learning Equity: A Course Auditing System of Skin Color in Preclinical Medical Education.

Visual learning equity is a health justice effort in response to the lack of representation of brown and black skin images in medical education. This paucity creates a knowledge gap and decreases providers' competence in managing skin disease in minoritized populations. Herein, we aimed to create a standardized course auditing system to assess the use of brown and black skin images in medical education. We performed a cross-sectional analysis of the 2020-2021 preclinical curriculum at one US medical school. All human images in the learning material were analyzed. Skin color was categorized as light/white, medium/brown, and dark/black using the Massey-Martin New Immigrant Survey Skin Color Scale. We included 1,660 unique images in our analysis; 71.3%, (n=1,183) were light/white, 16.1% (n=267) were medium/brown and 12.7% (n=210) were dark/black. Dermatologic images of skin, hair, nails, or mucosal disease made up 62.1% (n=1,031) of the images and 68.1% (n=702) were light/white. The pulmonary course presented the highest proportion of light/white skin (88.0%, n=44/50) and the dermatology course presented the lowest proportion of light/white skin (59.0%, n=301/510). Images of infectious diseases were more frequently presented in darker skin colors (χ2 [2]=15.46, P&lt;.001). Light/white skin was the standard used for visual learning images in the medical school curriculum at this institution. The authors outline steps to perform a curriculum audit and diversify medical curricula to ensure the next generation of physicians are educated to care for all patients.

Inflammatory and infectious skin diseases in line-field confocal optical coherence tomography: state of the art.

Line-field confocal optical coherence tomography (LC-OCT) is a new non-invasive dermatological imaging device. We summarized the available data on the use of LC-OCT for inflammatory and infectious diseases. In February 2023, we searched for all articles concerning the use of LC-OCT in inflammatory and infectious diseases. A total number of 14 papers were found and evaluated, and relevant information was extracted. LC-OCT can reveal architectural changes in the skin. Inflammatory cells are barely visible. It can highlight the level of fluid accumulation, the thickness of different epidermal layers, and the presence of ''foreign bodies'' such as parasites.

Serial Dependence in Dermatological Judgments.

Serial Dependence is a ubiquitous visual phenomenon in which sequentially viewed images appear more similar than they actually are, thus facilitating an efficient and stable perceptual experience in human observers. Although serial dependence is adaptive and beneficial in the naturally autocorrelated visual world, a smoothing perceptual experience, it might turn maladaptive in artificial circumstances, such as medical image perception tasks, where visual stimuli are randomly sequenced. Here, we analyzed 758,139 skin cancer diagnostic records from an online app, and we quantified the semantic similarity between sequential dermatology images using a computer vision model as well as human raters. We then tested whether serial dependence in perception occurs in dermatological judgments as a function of image similarity. We found significant serial dependence in perceptual discrimination judgments of lesion malignancy. Moreover, the serial dependence was tuned to the similarity in the images, and it decayed over time. The results indicate that relatively realistic store-and-forward dermatology judgments may be biased by serial dependence. These findings help in understanding one potential source of systematic bias and errors in medical image perception tasks and hint at useful approaches that could alleviate the errors due to serial dependence.

Few‐shot learning for dermatological conditions with Lesion Area Aware Swin Transformer

AbstractSkin is the largest organ of the human body and participates in the functional activities of the human body all the time. Therefore, human beings have a large risk of getting skin diseases. The diseased skin lesion image shows visually different characteristics from the normal skin image, and sometimes unusual skin color may indicate human viscera or autoimmune issues. However, the current recognition and classification of dermatological conditions still rely on expert visual diagnosis rather than a visual algorithm. This is because there are many kinds of lesion features of skin diseases, and the lesion accounts for a small proportion of the skin image, so it is difficult to learn the required lesion features; meanwhile, some dermatology images have too few samples to deal with the problem of small samples. In view of the above limitations, we propose a model named Lesion Area Aware Shifted windows Transformer for dermatological conditions classification rely on the powerful performance and excellent result of Swin transformer recently proposed. For brief notation, we use its abbreviation later. Our main contributions are as follows. First, we modify the Swin transformer and use it in the automatic classification of dermatological conditions. Using the self‐attention mechanism of the transformer, our method can mine more long‐distance correlations between diseased tissue image features. Using its shifting windows, we can fuse local features and global features, so it is possible to get better classification results with a flexible receptive field. Second, we use a skip connection to grasp and reinforce global features from the previous block and use Swin transformer to extract detailed local features, which will excavate and merge global features and local features further. Third, considering Swin transformer is a lightweight model compared with traditional transformers, our model is compact for deployment and more favorable to resource‐strict medical devices.

IMAGE SEGMENTATION WITH A CONVOLUTIONAL NEURAL NETWORK WITHOUT POOLING LAYERS IN DERMATOLOGICAL DISEASE DIAGNOSTICS SYSTEMS

Context. The problem of automating of the segmentation of spectral-statistical texture images is considered. The object of research is image processing in dermatological disease diagnostic systems.&#x0D; Objective. The aim of the research is to improve the segmentation performance of color images of psoriasis lesions by elaboration of a deep learning convolutional neural network without pooling layers.&#x0D; Method. The convolutional neural network is proposed to process a three-channel psoriasis image with a specified size. The initial color images were scaled to the specified size and then inputed on the neural network. The architecture of the proposed neural network consists of four convolutional layers with batch normalization layers and ReLU activation function. Feature maps from the output of these layers were inputted to the 1*1 convolutional layer with the Softmax activation function. The resulting feature maps were inputted to the image pixel classification layer. When segmenting images, convolutional and pooling layers extract the features of image fragments, and fully connected layers classify the resulting feature vectors, forming a partition of the image into homogeneous segments. The segmentation features are evaluated as a result of network training using ground-truth images which segmented by an expert. Such features are robust to noise and distortion in images. The combination of segmentation results at different scales is determined by the network architecture. Pooling layers were not included in the architecture of the proposed convolutional neural network since they reduce the size of feature maps compared to the size of the original image and can decrease the segmentation performance of small psoriasis lesions and psoriasis lesions of complex shape.&#x0D; Results. The proposed convolutional neural network has been implemented in software and researched for solving the problem of psoriasis images segmentation.&#x0D; Conclusions. The use of the proposed convolutional neural network made it possible to enhance the segmentation performance of plaque and guttate psoriasis images, especially at the edges of the lesions. Prospects for further research are to study the performance of the proposed CNN then abrupt changes in color and illumination, blurring, as well as the complex background areas are present on dermatological images, for example, containing clothes or fragments of the interior. It is advisable to use the proposed CNN in other problems of color image processing to segment statistical or spectral-statistical texture regions on a uniform or textured background.

Rapid Cellular-Resolution Skin Imaging with Optical Coherence Tomography Using All-Glass Multifocal Metasurfaces.

Cellular-resolution optical coherence tomography (OCT) is a powerful tool offering noninvasive histology-like imaging. However, like other optical microscopy tools, a high numerical aperture (N.A.) lens is required to generate a tight focus, generating a narrow depth of field, which necessitates dynamic focusing and limiting the imaging speed. To overcome this limitation, we developed a metasurface platform that generates multiple axial foci, which multiplies the volumetric OCT imaging speed by offering several focal planes. This platform offers accurate and flexible control over the number, positions, and intensities of axial foci generated. All-glass metasurface optical elements 8 mm in diameter are fabricated from fused-silica wafers and implemented into our scanning OCT system. With a constant lateral resolution of 1.1 μm over all depths, the multifocal OCT triples the volumetric acquisition speed for dermatological imaging, while still clearly revealing features of stratum corneum, epidermal cells, and dermal-epidermal junctions and offering morphological information as diagnostic criteria for basal cell carcinoma. The imaging speed can be further improved in a sparse sample, e.g., 7-fold with a seven-foci beam. In summary, this work demonstrates the concept of metasurface-based multifocal OCT for rapid virtual biopsy, further providing insights for developing rapid volumetric imaging systems with high resolution and compact volume.

Outlier detection in dermatology: Performance of different convolutional neural networks for binary classification of inflammatory skin diseases.

Artificial intelligence (AI) and convolutional neural networks (CNNs) represent rising trends in modern medicine. However, comprehensive data on the performance of AI practices in clinical dermatologic images are non-existent. Furthermore, the role of professional data selection for training remains unknown. The aims of this study were to develop AI applications for outlier detection of dermatological pathologies, to evaluate CNN architectures' performance on dermatological images and to investigate the role of professional pre-processing of the training data, serving as one of the first anchor points regarding data selection criteria in dermatological AI-based binary classification tasks of non-melanoma pathologies. Six state-of-the-art CNN architectures were evaluated for their accuracy, sensitivity and specificity for five dermatological diseases and using five data subsets, including data selected by two dermatologists, one with 5 and the other with 11 years of clinical experience. Overall, 150 CNNs were evaluated on up to 4051 clinical images. The best accuracy was reached for onychomycosis (accuracy=1.000), followed by bullous pemphigoid (accuracy=0.951) and lupus erythematosus (accuracy=0.912). The CNNs InceptionV3, Xception and ResNet50 achieved the best accuracy in 9, 8 and 6 out of 25 data sets, respectively (36.0%, 32.0% and 24.0%). On average, the data set provided by the senior physician and the data set provided in accordance with both dermatologists performed the best (accuracy=0.910). This AI approach for the detection of outliers in dermatological diagnoses represents one of the first studies to evaluate the performance of different CNNs for binary decisions in clinical non-dermatoscopic images of a variety of dermatological diseases other than melanoma. The selection of images by an experienced dermatologist during pre-processing had substantial benefits for the performance of the CNNs. These comparative results might guide future AI approaches to dermatology diagnostics, and the evaluated CNNs might be applicable for the future training of dermatology residents.

AI-assisted mole detection for online dermatology triage in telemedicine settings

Skin moles are one of the most critical conditions for early diagnosis of severe conditions such as melanoma. Early identification of moles has become crucial nowadays, primarily due to malignant melanoma, a dangerous type of skin cancer. Most of the recent advances concerning this domain of dermatology deal mostly with either classifying moles as benign or malignant or with methods that help delineate moles from skin images. However, there are minimal sources of exploration in just determining whether moles are present in a given query image. In this paper, we have employed the latest state-of-the-art neural networks to identify the presence of moles in dermatological images uploaded by patients on an online teledermatology platform. This filter flags if a mole is detected, which can be employed as a triage system that helps the dermatologist diagnose and ease the follow-up treatment procedure in a physical setting if the patient has a mole in their image. A comparative study of the prediction performance of the different models has been provided for different performance metrics of interest. The results presented in this paper have been obtained from two sets of data, consisting of more than 26,000 clinical pictures with different conditions combined. Multiple experiments using different models yielded a macro average recall value as high as 0.955, along with overall accuracy and macro average precision values of 0.962 and 0.958, respectively.