SynthesisSensors (Basel, Switzerland)2022
New Trends in Melanoma Detection Using Neural Networks: A Systematic Review.
Synthesis in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
31 citing papers in PubMed, 4 syntheses or guidelines pooled it, 87 citations in OpenAlex.
- Diagnosis melanoma with artificial intelligence systems: A meta-analysis study and systematic review.Journal of the European Academy of Dermatology and Venereology : JEADV · 2025Pooled it
- Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews.Journal of medical Internet research · 2025Pooled it
- AI Quality Standards in Health Care: Rapid Umbrella Review.Journal of medical Internet research · 2024Pooled it
- Distinguish the Value of the Benign Nevus and Melanomas Using Machine Learning: A Meta-Analysis and Systematic Review.Mediators of inflammation · 2022Pooled it
- How is Artificial Intelligence Transforming the Skin Cancer Screening Pathway? An Umbrella Review.Research square · 2026Article
- Multimodal artificial intelligence for enhanced skin cancer diagnosis and prognosis.Discover oncology · 2026Review
- Deep Learning Techniques for Prostate Cancer Analysis and Detection: Survey of the State of the Art.Journal of imaging · 2025Review
- Review of Non-Invasive Imaging Technologies for Cutaneous Melanoma.Biosensors · 2025Review
- Optimizing Digital Image Quality for Improved Skin Cancer Detection.Journal of imaging · 2025Article
- Advanced Deep Learning Models for Melanoma Diagnosis in Computer-Aided Skin Cancer Detection.Sensors (Basel, Switzerland) · 2025Article
- Article
- Intelligent system based on multiple networks for accurate ovarian tumor semantic segmentation.Heliyon · 2024Article
- Pre-trained multimodal large language model enhances dermatological diagnosis using SkinGPT-4.Nature communications · 2024Article
- Development and Validation of Adaptable Skin Cancer Classification System Using Dynamically Expandable Representation.Healthcare informatics research · 2024Article
- Review
- circTADA2A inhibited SLC38A1 expression and suppresses melanoma progression through the prevention of CNBP trans-activation.PloS one · 2024Article
- Artificial Intelligence on Diagnostic Aid of Leprosy: A Systematic Literature Review.Journal of clinical medicine · 2023Review
- A Review of Recent Advances in Computer-Aided Detection Methods Using Hyperspectral Imaging Engineering to Detect Skin Cancer.Cancers · 2023Review
- Recent Advancements and Perspectives in the Diagnosis of Skin Diseases Using Machine Learning and Deep Learning: A Review.Diagnostics (Basel, Switzerland) · 2023Review
- A Spitzoid Tumor dataset with clinical metadata and Whole Slide Images for Deep Learning models.Scientific data · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Due to its increasing incidence, skin cancer, and especially melanoma, is a serious health disease today. The high mortality rate associated with melanoma makes it necessary to detect the early stages to be treated urgently and properly. This is the reason why many researchers in this domain wanted to obtain accurate computer-aided diagnosis systems to assist in the early detection and diagnosis of such diseases. The paper presents a systematic review of recent advances in an area of increased interest for cancer prediction, with a focus on a comparative perspective of melanoma detection using artificial intelligence, especially neural network-based systems. Such structures can be considered intelligent support systems for dermatologists. Theoretical and applied contributions were investigated in the new development trends of multiple neural network architecture, based on decision fusion. The most representative articles covering the area of melanoma detection based on neural networks, published in journals and impact conferences, were investigated between 2015 and 2021, focusing on the interval 2018-2021 as new trends. Additionally presented are the main databases and trends in their use in teaching neural networks to detect melanomas. Finally, a research agenda was highlighted to advance the field towards the new trends.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.