Development of an artificial intelligence algorithm for the diagnosis of infantile hemangiomas

Zhang, April J; Lindberg, Nick; Chamlin, Sarah L.; Haggstrom, Anita N.; Mancini, Anthony J.; Siegel, Dawn H.; Drolet, Beth A.

doi:10.1111/pde.15149

Cited by 7 publications

(8 citation statements)

References 8 publications

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“…This algorithm demonstrated a diagnostic accuracy of 91.7% for facial hemangiomas in infants. [41] Cluster 8 primarily referred to computer vision systems and skin cancer dermatologists. Friedman RJ and colleagues conducted a blinded comparison study to evaluate the diagnostic performance of dermoscopic and automatic multispectral computer vision systems for detecting small, pigmented skin lesions.…”

Section: Research Hotspots and Future Directionsmentioning

confidence: 99%

Past, present, and future of global research on artificial intelligence applications in dermatology: A bibliometric analysis

Wang,

Meng,

Zhang

2023

Medicine

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In recent decades, artificial intelligence (AI) has played an increasingly important role in medicine, including dermatology. Worldwide, numerous studies have reported on AI applications in dermatology, rapidly increasing interest in this field. However, no bibliometric studies have been conducted to evaluate the past, present, or future of this topic. This study aimed to illustrate past and present research and outline future directions for global research on AI applications in dermatology using bibliometric analysis. We conducted an online search of the Web of Science Core Collection database to identify scientific papers on AI applications in dermatology. The bibliometric metadata of each selected paper were extracted, analyzed, and visualized using VOS viewer and Cite Space. A total of 406 papers, comprising 8 randomized controlled trials and 20 prospective studies, were deemed eligible for inclusion. The United States had the highest number of papers (n = 166). The University of California System (n = 24) and Allan C. Halpern (n = 11) were the institution and author with the highest number of papers, respectively. Based on keyword co-occurrence analysis, the studies were categorized into 9 distinct clusters, with clusters 2, 3, and 7 containing keywords with the latest average publication year. Wound progression prediction using machine learning, the integration of AI into teledermatology, and applications of the algorithms in skin diseases, are the current research priorities and will remain future research aims in this field.

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Section: Research Hotspots and Future Directionsmentioning

confidence: 99%

Past, present, and future of global research on artificial intelligence applications in dermatology: A bibliometric analysis

Wang,

Meng,

Zhang

2023

Medicine

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“…Tognetti et al [60] applied dermoscopic images to develop a DL-CNN model in the classification between atypical nevi from early melanomas, which achieved adequate accuracy (AUC: 90.3, sensitivity: 86.5%, and specificity: 73.6%) and eliminated the influence from dermatologists' experience. Several AI-based algorithms on novel resources also achieved accurate diagnostic performance for skin neoplasms including photos of skin neoplasms [61], umbilical cord blood sera [62], and electronic colorimeters [63].…”

Section: Extracranial Tumor Diagnosismentioning

confidence: 99%

Artificial intelligence applications in pediatric oncology diagnosis

Zhang

2023

Exploration of Targeted Anti-Tumor Therapy

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Artificial intelligence (AI) algorithms have been applied in abundant medical tasks with high accuracy and efficiency. Physicians can improve their diagnostic efficiency with the assistance of AI techniques for improving the subsequent personalized treatment and surveillance. AI algorithms fundamentally capture data, identify underlying patterns, achieve preset endpoints, and provide decisions and predictions about real-world events with working principles of machine learning and deep learning. AI algorithms with sufficient graphic processing unit power have been demonstrated to provide timely diagnostic references based on preliminary training of large amounts of clinical and imaging data. The sample size issue is an inevitable challenge for pediatric oncology considering its low morbidity and individual heterogeneity. However, this problem may be solved in the near future considering the exponential advancements of AI algorithms technically to decrease the dependence of AI operation on the amount of data sets and the efficiency of computing power. For instance, it could be a feasible solution by shifting convolutional neural networks (CNNs) from adults and sharing CNN algorithms across multiple institutions besides original data. The present review provides important insights into emerging AI applications for the diagnosis of pediatric oncology by systematically overviewing of up-to-date literature.

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“…Currently, the demand for pediatric dermatologists far exceeds the available workforce, with wait times for clinical visits surpassing all other pediatric specialties. 6,7 Patients often have to travel long distances to see a specialized provider. A study conducted in 2020 showed that 98% of all board-certified pediatric dermatologist practiced in metropolitan counties, 0% practiced in rural counties, and nine states had no pediatric dermatologist.…”

Section: Most Dermatology-related Ai Publications Involve Adult Datasetsmentioning

confidence: 99%

“…To overcome this disparity, AI‐based technology needs to be extended to pediatric patients. Currently, the demand for pediatric dermatologists far exceeds the available workforce, with wait times for clinical visits surpassing all other pediatric specialties 6,7 . Patients often have to travel long distances to see a specialized provider.…”

mentioning

confidence: 99%

“…In addition, adequately trained machine learning models have the potential to aid pediatric dermatologists/dermatologists with clinical decision support and diagnostic resolution of difficult cases, improving performance 1 . A recent example of applying machine learning to pediatric dermatology is a CNN designed to diagnose facial infantile hemangiomas, with a reported diagnostic accuracy of 91.7% 6 . The training dataset included images collected from medical specialists (physicians, nurses, medical assistants) and families 6 .…”

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confidence: 99%

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A call for implementing augmented intelligence in pediatric dermatology

Issa

Reimer‐Taschenbrecker

Paller

2023

Pediatric Dermatology

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Augmented intelligence (AI), the combination of artificial based intelligence with human intelligence from a practitioner, has become an increased focus of clinical interest in the field of dermatology. Technological advancements have led to the development of deep-learning based models to accurately diagnose complex dermatological diseases such as melanoma in adult datasets. Models for pediatric dermatology remain scarce, but recent studies have shown applications in the diagnoses of facial infantile hemangiomas and X-linked hypohidrotic ectodermal dysplasia; however, we see unmet needs in other complex clinical scenarios and rare diseases, such as diagnosing squamous cell carcinoma in patients with epidermolysis bullosa. Given the still limited number of pediatric dermatologists, especially in rural areas, AI has the potential to help overcome health disparities by helping primary care physicians treat or triage patients.

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Development of an artificial intelligence algorithm for the diagnosis of infantile hemangiomas

Cited by 7 publications

References 8 publications

Past, present, and future of global research on artificial intelligence applications in dermatology: A bibliometric analysis

Past, present, and future of global research on artificial intelligence applications in dermatology: A bibliometric analysis

Artificial intelligence applications in pediatric oncology diagnosis

A call for implementing augmented intelligence in pediatric dermatology

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