Diagnosis and Routing of Patients with Suspected Skin Cancer in Primary Care Settings: Gaps and Perspectives DOI Creative Commons
T. A. Gaydina, А. С. Дворников, В. Н. Ларина

et al.

The Russian Archives of Internal Medicine, Journal Year: 2024, Volume and Issue: 14(6), P. 419 - 434

Published: Nov. 27, 2024

Early accurate detection of skin cancer is a growing global problem health’s services throughout the world. Malignant formation can be suspected by using an anamnesis, visual inspection skin, and diffrent types investigations in primary care settings. The dermatoscopic examination necessary for exclusion or confirmation cancer, which performed dermatovenerologist. patient referred futher to oncologist case cannot excluded. Well-organized identification patients with accociated favorable prognosis. However, order reduce rates high neglect malignant tumors optimize routing after visiting phisician, it worth pay attention following points: annual medical check-up examinations, especially among people age over than 40 years; complete physical examination, including thorough history full body general practition as part clinical settings; use mandatory dermoscopic dermatovenerologist early diagnosis and, if possible, dynamic mapping artificial intelligence analysis; increasing professional communicative skills, needed managing newly diagnosed since psychosocial factors influence patient’s attitude towards his/her own health; maintaining continuity between practitioners dermatovenerologists improve quality care; creation “Healthy Skin” schools clinics increase literacy population concerning education regarding danger training self-examination skills; e-health technologies additional source information.

Language: Английский

Avaliação do uso de inteligência artificial na detecção precoce de melanoma DOI Creative Commons
Henrique Noguez da Cunha, Marcus Miranda Lessa,

Isadora Guimarães Muzzi

et al.

Caderno Pedagógico, Journal Year: 2025, Volume and Issue: 22(1), P. e13330 - e13330

Published: Jan. 14, 2025

Introdução: A detecção precoce do melanoma é crucial para a redução da mortalidade associada essa forma agressiva de câncer pele, cuja incidência tem aumentado globalmente, com estimativas 324.635 novos casos e 57.043 mortes em 2020. Objetivo: Avaliar aplicação inteligência artificial (IA) na melanoma, explorando suas implicações clínicas, éticas sociais. Metodologia: Trata-se uma revisão narrativa literatura, abrangendo estudos publicados entre 2014 2024, português, inglês espanhol, que abordam relação IA melanoma. pesquisa foi realizada bases dados eletrônicas como PubMed, Scopus Web of Science, utilizando descritores controlados Medical Subject Headings (MeSH) Descritores Ciências Saúde (DeCS). Após triagem rigorosa, 16 artigos foram selecionados análise, considerando critérios relevância adequação à pergunta norteadora. Resultados: pode melhorar significativamente precisão diagnóstica, algoritmos demonstrando taxas sensibilidade superiores 90% alguns estudos. No entanto, eficácia dos sistemas diretamente influenciada pela qualidade diversidade utilizados no treinamento, muitos conjuntos carecendo representatividade, especialmente tonalidade pele. Além disso, falta formação profissionais saúde as incertezas legais associadas ao uso dessas tecnologias emergem barreiras sua adoção. Conclusão: Ressalta-se necessidade colaboração interdisciplinar especialistas ciência computação, além importância diretrizes claras sobre responsabilidade legal. capacitação contínua essencial maximizar os benefícios prática clínica, garantindo implementação ética responsável possa, fato, contribuir e, consequentemente, melhoria desfechos saúde.

Citations

0
Deep Learning and Multidisciplinary Imaging in Pediatric Surgical Oncology: A Scoping Review DOI Creative Commons
Myrthe A. D. Buser,

J. K. van der Rest,

Marc H. W. A. Wijnen

et al.

Cancer Medicine, Journal Year: 2025, Volume and Issue: 14(2)

Published: Jan. 1, 2025

ABSTRACT Background Medical images play an important role in diagnosis and treatment of pediatric solid tumors. The field radiology, pathology, other image‐based diagnostics are getting increasingly advanced. This indicates a need for advanced image processing technology such as Deep Learning (DL). Aim Our review focused on the use DL multidisciplinary imaging surgical oncology. Methods A search was conducted within three databases (Pubmed, Embase, Scopus), 2056 articles were identified. Three separate screenings performed each identified subfield. Results In total, we 36 articles, divided between radiology ( n = 22), pathology 9), 5). Four types tasks our review: classification, prediction, segmentation, synthesis. General statements about studies'’ performance could not be made due to inhomogeneity included studies. To implement clinical practice, both technical validation uttermost importance. Conclusion conclusion, provided overview all research more status adults should used guide move oncology further, keep improving outcomes children with cancer.

Language: Английский

Citations

0
Gender Disparities in Melanoma: Advances in Diagnosis, Treatment, and the Role of Artificial Intelligence DOI Creative Commons
Diala Ra’Ed Kamal Kakish, Jehad Feras AlSamhori,

Lana N. Qaqish

et al.

Dermatological Reviews, Journal Year: 2025, Volume and Issue: 6(1)

Published: Feb. 1, 2025

ABSTRACT Background Melanoma, a highly aggressive skin cancer, demonstrates significant gender disparities, with men facing later‐stage diagnoses, more tumor characteristics, and worse survival rates. This review examines the biological, behavioral, environmental factors driving these alongside recent advancements in diagnosis treatment. Additionally, it explores how artificial intelligence (AI) can address gender‐specific differences melanoma incidence outcomes. Results Gender disparities stem from biological factors, such as hormonal genetic differences, behavioral patterns like delayed health‐seeking among men. AI‐driven diagnostic tools, including convolutional neural networks (CNNs), show promise but often reflect biases training data sets, underrepresenting darker tones patterns. Ensuring diverse integrating “super‐prompts” or region‐specific demographic prompts, utilizing bias‐aware algorithms help mitigate biases, thereby improving accuracy equity. Conclusion Reducing requires innovative technologies equitable healthcare policies education. Early detection using inclusive AI models tailored to genders, targeted therapeutic strategies, is critical outcomes for high‐risk groups, particularly underserved populations.

Language: Английский

Citations

0
Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews DOI Creative Commons
Haishan Xu,

Ting‐Ting Gong,

Xin‐Jian Song

et al.

Journal of Medical Internet Research, Journal Year: 2025, Volume and Issue: 27, P. e53567 - e53567

Published: April 1, 2025

Background Artificial intelligence (AI) has the potential to transform cancer diagnosis, ultimately leading better patient outcomes. Objective We performed an umbrella review summarize and critically evaluate evidence for AI-based imaging diagnosis of cancers. Methods PubMed, Embase, Web Science, Cochrane, IEEE databases were searched relevant systematic reviews from inception June 19, 2024. Two independent investigators abstracted data assessed quality evidence, using Joanna Briggs Institute (JBI) Critical Appraisal Checklist Systematic Reviews Research Syntheses. further in each meta-analysis by applying Grading Recommendations, Assessment, Development, Evaluation (GRADE) criteria. Diagnostic performance synthesized narratively. Results In a comprehensive analysis 158 included studies evaluating AI algorithms noninvasive across 8 major human system cancers, accuracy classifiers central nervous cancers varied widely (ranging 48% 100%). Similarities observed diagnostic head neck, respiratory system, digestive urinary female-related systems, skin, other sites. Most meta-analyses demonstrated positive summary performance. For instance, 9 meta-analyzed sensitivity specificity esophageal cancer, showing ranges 90%-95% 80%-93.8%, respectively. case breast detection, calculated pooled within 75.4%-92% 83%-90.6%, Four reported ovarian both 75%-94%. Notably, lung was relatively low, primarily distributed between 65% 80%. Furthermore, 80.4% (127/158) high according JBI Checklist, with remaining classified as medium quality. The GRADE assessment indicated that overall moderate low. Conclusions Although shows great achieving accelerated, accurate, more objective diagnoses multiple there are still hurdles overcome before its implementation clinical settings. present findings highlight concerted effort research community, clinicians, policymakers is required existing translate this into improved outcomes health care delivery. Trial Registration PROSPERO CRD42022364278; https://www.crd.york.ac.uk/PROSPERO/view/CRD42022364278

Language: Английский

Citations

0
Enhancing skin lesion classification: a CNN approach with human baseline comparison DOI Creative Commons
Deep Ajabani, Zaffar Ahmed Shaikh, Amr Yousef

et al.

PeerJ Computer Science, Journal Year: 2025, Volume and Issue: 11, P. e2795 - e2795

Published: April 15, 2025

This study presents an augmented hybrid approach for improving the diagnosis of malignant skin lesions by combining convolutional neural network (CNN) predictions with selective human interventions based on prediction confidence. The algorithm retains high-confidence CNN while replacing low-confidence outputs expert assessments to enhance diagnostic accuracy. A model utilizing EfficientNetB3 backbone is trained datasets from ISIC-2019 and ISIC-2020 SIIM-ISIC melanoma classification challenges evaluated a 150-image test set. model’s are compared against 69 experienced medical professionals. Performance assessed using receiver operating characteristic (ROC) curves area under curve (AUC) metrics, alongside analysis resource costs. baseline achieves AUC 0.822, slightly below performance experts. However, improves true positive rate 0.782 reduces false 0.182, delivering better minimal involvement. offers scalable, resource-efficient solution address variability in image analysis, effectively harnessing complementary strengths humans CNNs.

Language: Английский

Citations

0
Diagnosis and Routing of Patients with Suspected Skin Cancer in Primary Care Settings: Gaps and Perspectives DOI Creative Commons
T. A. Gaydina, А. С. Дворников, В. Н. Ларина

et al.

The Russian Archives of Internal Medicine, Journal Year: 2024, Volume and Issue: 14(6), P. 419 - 434

Published: Nov. 27, 2024

Early accurate detection of skin cancer is a growing global problem health’s services throughout the world. Malignant formation can be suspected by using an anamnesis, visual inspection skin, and diffrent types investigations in primary care settings. The dermatoscopic examination necessary for exclusion or confirmation cancer, which performed dermatovenerologist. patient referred futher to oncologist case cannot excluded. Well-organized identification patients with accociated favorable prognosis. However, order reduce rates high neglect malignant tumors optimize routing after visiting phisician, it worth pay attention following points: annual medical check-up examinations, especially among people age over than 40 years; complete physical examination, including thorough history full body general practition as part clinical settings; use mandatory dermoscopic dermatovenerologist early diagnosis and, if possible, dynamic mapping artificial intelligence analysis; increasing professional communicative skills, needed managing newly diagnosed since psychosocial factors influence patient’s attitude towards his/her own health; maintaining continuity between practitioners dermatovenerologists improve quality care; creation “Healthy Skin” schools clinics increase literacy population concerning education regarding danger training self-examination skills; e-health technologies additional source information.

Language: Английский

Citations

0