Third Eye? The Assistance of Artificial Intelligence (AI) in the Endoscopy of Gastrointestinal Neoplasms

Leśniewska, Magdalena; Patryn, Rafał; Kopystecka, Agnieszka; Kozioł, Ilona; Budzyńska, Julia

doi:10.3390/jcm12216721

Cited by 3 publications

(2 citation statements)

References 81 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recurrent neoplastic lesions may also harbor new molecular aberrations, distinct from the primary tumor, potentially leading to treatment resistance. 189 AI applications in this domain are primarily focused on the detection, characterization, and monitoring of tumors. A notable example is the AI-based model the artificial intelligence risk model (Mirai), which predicted 5-year cancer risk from screening mammograms with high accuracy, as demonstrated in a study involving 128 793 mammograms from 62 185 patients.…”

Section: Challenges and Future Perspectivesmentioning

confidence: 99%

Application of 3D, 4D, 5D, and 6D bioprinting in cancer research: what does the future look like?

Khorsandi,

Rezayat,

Sezen

et al. 2024

J. Mater. Chem. B

View full text Add to dashboard Cite

show abstract

Section: Challenges and Future Perspectivesmentioning

confidence: 99%

Application of 3D, 4D, 5D, and 6D bioprinting in cancer research: what does the future look like?

Khorsandi,

Rezayat,

Sezen

et al. 2024

J. Mater. Chem. B

View full text Add to dashboard Cite

show abstract

“…The integration of deep-learning techniques (DLT) in the realm of medical science, particularly within the context of coeliac disease diagnosis and management, has gained significant attention. Deep learning, a subfield of machine learning, has displayed remarkable potential in analyzing complex medical data, including endoscopic images, to discern significant patterns and diagnostic markers [7,8].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning in Coeliac Disease: A Systematic Review on Novel Diagnostic Approaches to Disease Diagnosis

Sharif,

David,

Omar

et al. 2023

JCM

View full text Add to dashboard Cite

Background: Coeliac disease affects approximately 1% of the global population with the diagnosis often relying on invasive and time-demanding methods. Deep learning, a powerful tool in medical science, shows potential for non-invasive, accurate coeliac disease diagnosis, though challenges remain. Objective: This systematic review aimed to evaluate the current state of deep-learning applications in coeliac disease diagnosis and identify potential areas for future research that could enhance diagnostic accuracy, sensitivity, and specificity. Methods: A systematic review was conducted using the following databases: PubMed, Embase, Web of Science, and Scopus. PRISMA guidelines were applied. Two independent reviewers identified research articles using deep learning for coeliac disease diagnosis and severity assessment. Only original research articles with performance metrics data were included. The quality of the diagnostic accuracy studies was assessed using the QUADAS-2 tool, categorizing studies based on risk of bias and concerns about applicability. Due to heterogeneity, a narrative synthesis was conducted to describe the applications and efficacy of the deep-learning techniques (DLT) in coeliac disease diagnosis. Results: The initial search across four databases yielded 417 studies with 195 being removed due to duplicity. Finally, eight studies were found to be suitable for inclusion after rigorous evaluation. They were all published between 2017 and 2023 and focused on using DLT for coeliac disease diagnosis or assessing disease severity. Different deep-learning architectures were applied. Accuracy levels ranged from 84% to 95.94% with the GoogLeNet model achieving 100% sensitivity and specificity for video capsule endoscopy images. Conclusions: DLT hold substantial potential in coeliac disease diagnosis. They offer improved accuracy and the prospect of mitigating clinician bias. However, key challenges persist, notably the requirement for more extensive and diverse datasets, especially to detect milder forms of coeliac disease. These methods are in their nascent stages, underscoring the need of integrating multiple data sources to achieve comprehensive coeliac disease diagnosis.

show abstract

Trends and hotspots in gastrointestinal neoplasms risk assessment: A bibliometric analysis from 1984 to 2022

Fu,

Ma,

Niu

et al. 2024

World J Gastrointest Oncol

View full text Add to dashboard Cite

BACKGROUND Gastrointestinal neoplasm (GN) significantly impact the global cancer burden and mortality, necessitating early detection and treatment. Understanding the evolution and current state of research in this field is vital. AIM To conducts a comprehensive bibliometric analysis of publications from 1984 to 2022 to elucidate the trends and hotspots in the GN risk assessment research, focusing on key contributors, institutions, and thematic evolution. METHODS This study conducted a bibliometric analysis of data from the Web of Science Core Collection database using the "bibliometrix" R package, VOSviewer, and CiteSpace. The analysis focused on the distribution of publications, contributions by institutions and countries, and trends in keywords. The methods included data synthesis, network analysis, and visualization of international collaboration networks. RESULTS This analysis of 1371 articles on GN risk assessment revealed a notable evolution in terms of research focus and collaboration. It highlights the United States' critical role in advancing this field, with significant contributions from institutions such as Brigham and Women's Hospital and the National Cancer Institute. The last five years, substantial advancements have been made, representing nearly 45% of the examined literature. Publication rates have dramatically increased, from 20 articles in 2002 to 112 in 2022, reflecting intensified research efforts. This study underscores a growing trend toward interdisciplinary and international collaboration, with the Journal of Clinical Oncology standing out as a key publication outlet. This shift toward more comprehensive and collaborative research methods marks a significant step in addressing GN risks. CONCLUSION This study underscores advancements in GN risk assessment through genetic analyses and machine learning and reveals significant geographical disparities in research emphasis. This calls for enhanced global collaboration and integration of artificial intelligence to improve cancer prevention and treatment accuracy, ultimately enhancing worldwide patient care.

show abstract

Third Eye? The Assistance of Artificial Intelligence (AI) in the Endoscopy of Gastrointestinal Neoplasms

Cited by 3 publications

References 81 publications

Application of 3D, 4D, 5D, and 6D bioprinting in cancer research: what does the future look like?

Application of 3D, 4D, 5D, and 6D bioprinting in cancer research: what does the future look like?

Deep Learning in Coeliac Disease: A Systematic Review on Novel Diagnostic Approaches to Disease Diagnosis

Trends and hotspots in gastrointestinal neoplasms risk assessment: A bibliometric analysis from 1984 to 2022

Contact Info

Product

Resources

About