A deep-learning algorithm to classify skin lesions from mpox virus infection

Thieme, Alexander; Zheng, Yuanning; Machiraju, Gautam; Sadée, Christoph; Mittermaier, Mirja; Gertler, Maximilian; Salinas, Jorge; Srinivasan, Krithika; Gyawali, Prashnna Kumar; Carrillo-Pérez, Francisco; Capodici, Angelo; Uhlig, Maximilian; Habenicht, Daniel; Löser, Anastassia; Kohler, Maja; Schuessler, Maximilian; Kaul, David; Gollrad, Johannes; Ma, Jackie; Lippert, Christoph; Billick, Kendall; Bogoch, Isaac I.; Hernandez‐Boussard, Tina; Geldsetzer, Pascal; Gevaert, Olivier

doi:10.1038/s41591-023-02225-7

Cited by 36 publications

(20 citation statements)

References 33 publications

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“…An image-based deep convolutional neural network for pox (MPXV-CNN) has also been developed based on the dataset from 139,198 skin lesion images, including lesions in persons of color and involving different anatomical locations. MPXV-CNN has a promising potential for early diagnoses and decreasing transmission, which will be valuable in an outbreak setting in future [ 16 ].…”

Section: Discussionmentioning

confidence: 99%

The diagnostic dilemma for atypical presentation of progressive human Mpox

Sahra,

Villalobos,

Scott

et al. 2023

BMC Infect Dis

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Background Human mpox has increasingly been reported worldwide since May 2022, with higher incidence in men who have sex with men (MSM) and persons living with HIV (PLHIV) with presentation typical for generalized macules and papules. Case presentation We are describing a case of human mpox, which presented as widespread, atypical round verrucous lesions that went undiagnosed in the community for six months and was treated with antibacterials and antifungals given the similarity to skin manifestations associated with endemic mycoses. Conclusions Suspicion for human mpox should be high in young MSM and PLHIV who present with rash and mpox should be ruled out earlier.

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Section: Discussionmentioning

confidence: 99%

The diagnostic dilemma for atypical presentation of progressive human Mpox

Sahra,

Villalobos,

Scott

et al. 2023

BMC Infect Dis

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“…ment of various tools and techniques to aid dermatologists in their diagnosis [13], [14], [15]. For example, the development of artificial intelligence tools to aid in the diagnosis of skin disorders from images has been made possible by recent advancements in deep learning [16], [17], such as skin cancer classification [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], dermatopathology [28], [29], [30], predicting novel risk factors or epidemiology [31], [32], identifying onychomycosis [33], quantifying alopecia areata [34], classify skin lesions from mpox virus infection [35], and so on [4]. Among these, most studies have predominantly concentrated on identifying skin lesions through dermoscopic images [36], [37], [38].…”

Section: (Which Was Not Certified By Peer Review)mentioning

confidence: 99%

Pre-trained Multimodal Large Language Model Enhances Dermatological Diagnosis using SkinGPT-4

Zhou

Sun

et al. 2023

Preprint

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Skin and subcutaneous diseases rank high among the leading contributors to the global burden of nonfatal diseases, impacting a considerable portion of the population. Nonetheless, the field of dermatology diagnosis faces three significant hurdles. Firstly, there is a shortage of dermatologists accessible to diagnose patients, particularly in rural regions. Secondly, accurately interpreting skin disease images poses a considerable challenge. Lastly, generating patient-friendly diagnostic reports is usually a time-consuming and labor-intensive task for dermatologists. To tackle these challenges, we present SkinGPT-4, which is the world's first interactive dermatology diagnostic system powered by an advanced visual large language model. SkinGPT-4 leverages a fine-tuned version of MiniGPT-4, trained on an extensive collection of skin disease images (comprising 52,929 publicly available and proprietary images) along with clinical concepts and doctors' notes. We designed a two-step training process to allow SkinGPT to express medical features in skin disease images with natural language and make accurate diagnoses of the types of skin diseases. With SkinGPT-4, users could upload their own skin photos for diagnosis, and the system could autonomously evaluate the images, identifies the characteristics and categories of the skin conditions, performs in-depth analysis, and provides interactive treatment recommendations. Meanwhile, SkinGPT-4's local deployment capability and commitment to user privacy also render it an appealing choice for patients in search of a dependable and precise diagnosis of their skin ailments. To demonstrate the robustness of SkinGPT-4, we conducted quantitative evaluations on 150 real-life cases, which were independently reviewed by certified dermatologists, and showed that SkinGPT-4 could provide accurate diagnoses of skin diseases. Though SkinGPT-4 is not a substitute for doctors, it could enhance users' comprehension of their medical conditions, facilitate improve communication between patients and doctors, expedite the diagnostic process for dermatologists, and potentially promote human-centred care and healthcare equity in underdeveloped areas.

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“…Convolutional Neural Network technology serves as an example of deep learning [6], [7]. Four main layers make up the Convolutional Neural Network architecture: the convolutional layer define characteristics from input; nonlinear activation layer improves the CNN's nonlinear representations; the pooling layer, which chooses and filters features; and fully connected layer generates final predicted result [8], [9]. CNN is capable of representation learning in addition to extracting features from images [10,11].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The employed three CNN models, thus e=3. The voting system for proposed work describes as in equation (8,9):…”

Section: Proposed Ensemble Modelmentioning

confidence: 99%

An Ensemble Novel Deep Learning Technique for Chest Radiograph-Based Pneumonia Prediction

2023

Preprint

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Pneumonia continues to be a prominent treatable cause of global mortality, stressing the importance of early identification to enable prompt intervention. Chest X-rays (CXRs) are an essential diagnostic tool, however determining their exact interpretation is still very difficult. By addressing both medical experts and individuals who are new to the area, the proposed work aims to improve prediction of pneumonia. The Synthetic Minority Over-sampling Technique has been utilised to cope with imbalanced dataset because the used dataset does not have balanced distribution among all classes. A pneumonia prediction model that makes use of convolutional neural networks including CustomVGG19, CustomResNet-50 and CustomDenseNet121 and the proposed ensemble model to improve diagnosis of pneumonia has been proposed. These models are trained and improved in experiments. The optimization of each model's performance was achieved through the systematic exploration of diverse configurations and hyperparameters. The ultimate outcomes were derived by employing the ensemble technique, which involved amalgamating the predictions of CNN models during the analysis. Results demonstrate the superiority of the proposed model, which achieved a 97.68% prediction accuracy.

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A deep-learning algorithm to classify skin lesions from mpox virus infection

Cited by 36 publications

References 33 publications

The diagnostic dilemma for atypical presentation of progressive human Mpox

The diagnostic dilemma for atypical presentation of progressive human Mpox

Pre-trained Multimodal Large Language Model Enhances Dermatological Diagnosis using SkinGPT-4

An Ensemble Novel Deep Learning Technique for Chest Radiograph-Based Pneumonia Prediction

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