ArticleBioengineering (Basel, Switzerland)2025
Hyperspectral Imaging for Enhanced Skin Cancer Classification Using Machine Learning.
Article in Bioengineering (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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
18 citing papers in PubMed.
- The Evolving Role of Artificial Intelligence in Dermatology: A Meta-Analysis of Diagnostic Performance, Clinical Applications, and Implementation Challenges (2003-2025).Diagnostics (Basel, Switzerland) · 2026Review
- Non-Invasive Skin Cancer Diagnosis by Electrical Impedance Spectroscopy: Biophysics, Devices, Clinical Evidence, and Future Directions.Diagnostics (Basel, Switzerland) · 2026Review
- Deep Learning-Assisted Accuracy Improvement in Bladder Cancer Staging of Spectrum-Aided Visual Enhanced Cystoscopy Images.Biosensors · 2026Article
- SAVE: Spectrum-Aided Visual Enhancement for AI-Based Skin Cancer Detection.Diagnostics (Basel, Switzerland) · 2026Article
- Overview of Aging, Skin Health, Estrogen, Menopause and HRT.Life (Basel, Switzerland) · 2026Article
- DermaCalibra: A Robust and Explainable Multimodal Framework for Skin Lesion Diagnosis via Bayesian Uncertainty and Dynamic Modulation.Diagnostics (Basel, Switzerland) · 2026Article
- A Treatment Decision Model for Cutaneous Squamous Cell Carcinoma Based on Bayesian Networks.Cancers · 2026Article
- Foundation-Model-Driven Skin Lesion Segmentation and Classification Using SAM-Adapters and Vision Transformers.Diagnostics (Basel, Switzerland) · 2026Article
- Skin tumor identification by means of convolutional neural network and improved gray wolf optimizer.Frontiers in oncology · 2026Article
- An update on the diagnosis and treatment of seborrheic keratosis.Frontiers in medicine · 2026Review
- Skin lesions in patients treated with growth hormone and those with growth hormone excess: a current overview.Frontiers in medicine · 2026Review
- Streamlined Management of Basal Cell Carcinoma with Dermoscopy: A Retrospective Case-Control Study.Journal of clinical medicine · 2025Article
- Advanced Cutaneous Squamous Cell Carcinoma: Biology, Immunotherapy, and Evolving Prognostic Factors.Biomedicines · 2025Review
- Artificial Intelligence and New Technologies in Melanoma Diagnosis: A Narrative Review.Cancers · 2025Review
- The Role of Prevention and Early Detection in Skin Tumors: Correlation Between Educational Level and Tumor Stage at Diagnosis.Journal of clinical medicine · 2025Article
- Review
- Enhancing dermatological diagnosis for differentiating actinic from seborrheic keratosis using deep learning model.Frontiers in medicine · 2025Article
- Advances in intelligent recognition and diagnosis of skin scar images: concepts, methods, challenges, and future trends.Frontiers in medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
PubMed holds no abstract for this paper.
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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.