Artificial Intelligence in Endoscopic Ultrasound for Pancreatic Cancer: Where Are We Now and What Does the Future Entail?

Dahiya, Dushyant Singh; Al‐Haddad, Mohammad; Chandan, Saurabh; Gangwani, Manesh Kumar; Aziz, Muhammad; Mohan, Babu P.; Ramai, Daryl; Canakis, Andrew; Bapaye, Jay; Sharma, Neil

doi:10.3390/jcm11247476

Cited by 19 publications

(25 citation statements)

References 69 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, more large multicentric high-quality trials are needed. Until then, AI should serve as a ‘second set of eyes’ for endo-sonologists [ 141 ].…”

Section: Recent Advancement In Eus Ta In Pancreatic Carcinomamentioning

confidence: 99%

Role of Endoscopic Ultrasound in Diagnosis of Pancreatic Ductal Adenocarcinoma

Chatterjee,

Shah

2023

Diagnostics

View full text Add to dashboard Cite

Pancreatic ductal adenocarcinoma (PDAC) is the most common (90%) type of solid pancreatic neoplasm. Due to its late presentation and poor survival rate, early diagnosis and timely treatment is of utmost importance for better clinical outcomes. Endoscopic ultrasound provides high-resolution images of the pancreas and has excellent sensitivity in the diagnosis of even small (<2 cm) pancreatic lesions. Apart from imaging, it also has an advantage of tissue acquisition (EUS fine-needle aspiration, FNA; or fine-needle biopsy, FNB) for definitive diagnoses. EUS-guided tissue acquisition plays a crucial role in genomic and molecular studies, which in today’s era of personalized medicine, are likely to become important components of PDAC management. With the use of better needle designs and technical advancements, EUS has now become an indispensable tool in the management of PDAC. Lastly, artificial intelligence for the detection of pancreatic lesions and newer automated needles for tissue acquisition will obviate observer dependency in the near future, resulting in the wider dissemination and adoption of this technology for improved outcomes in patients with PDAC.

show abstract

“…However, more large multicentric high-quality trials are needed. Until then, AI should serve as a ‘second set of eyes’ for endo-sonologists [ 141 ].…”

Section: Recent Advancement In Eus Ta In Pancreatic Carcinomamentioning

confidence: 99%

Role of Endoscopic Ultrasound in Diagnosis of Pancreatic Ductal Adenocarcinoma

Chatterjee,

Shah

2023

Diagnostics

View full text Add to dashboard Cite

show abstract

“…Another major concern is the need for more standardization of the input data used to train AI models [86]. Establishing uniform data collection, processing, storage, reproduction, and analysis protocols is essential to ensure consistency.…”

Section: The Limitations and Shortages Of Artificial Intelligence In ...mentioning

confidence: 99%

“…As such, there is increasing interest in using AI-assisted EUS for the diagnosis of pancreatic lesions, mainly due to its relatively low cost and minimal invasiveness. Although an increasing body of research supports the superiority of AI-assisted EUS in diagnostic accuracy compared to traditional human interpretation, most clinicians remain cautious about its widespread application in clinical practice [86]. However, with ongoing improvements in AI algorithms and the quality of EUS images, AI-assisted EUS models have the potential to replace traditional EUS-FNA/B as the gold standard for diagnosing SPLs.…”

Section: The Limitations and Shortages Of Artificial Intelligence In ...mentioning

confidence: 99%

Artificial Intelligence in Endoscopic Ultrasonography-Guided Fine-Needle Aspiration/Biopsy (EUS-FNA/B) for Solid Pancreatic Lesions: Opportunities and Challenges

Qin,

Ran,

Chen

et al. 2023

Diagnostics

View full text Add to dashboard Cite

Solid pancreatic lesions (SPLs) encompass a variety of benign and malignant diseases and accurate diagnosis is crucial for guiding appropriate treatment decisions. Endoscopic ultrasonography-guided fine-needle aspiration/biopsy (EUS-FNA/B) serves as a front-line diagnostic tool for pancreatic mass lesions and is widely used in clinical practice. Artificial intelligence (AI) is a mathematical technique that automates the learning and recognition of data patterns. Its strong self-learning ability and unbiased nature have led to its gradual adoption in the medical field. In this paper, we describe the fundamentals of AI and provide a summary of reports on AI in EUS-FNA/B to help endoscopists understand and realize its potential in improving pathological diagnosis and guiding targeted EUS-FNA/B. However, AI models have limitations and shortages that need to be addressed before clinical use. Furthermore, as most AI studies are retrospective, large-scale prospective clinical trials are necessary to evaluate their clinical usefulness accurately. Although AI in EUS-FNA/B is still in its infancy, the constant input of clinical data and the advancements in computer technology are expected to make computer-aided diagnosis and treatment more feasible.

show abstract

“…These diseases are pretty lethal and have significant morbidity. For example, pancreatic cancer is the seventh leading cause of cancer-related deaths worldwide 1 , 2 . More so, pancreatic cancer’s overall 5-year survival rate stands at 11.5%.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence assisted endoscopic ultrasound for detection of pancreatic space occupying lesion: A systematic review and meta-analysis

Dhali,

Kipkorir,

Srichawla

et al. 2023

International Journal of Surgery

View full text Add to dashboard Cite

Background: Diagnosing pancreatic lesions, including chronic pancreatitis, autoimmune pancreatitis, and pancreatic cancer, poses a challenge and, as a result, is time-consuming. To tackle this issue, artificial intelligence (AI) has been increasingly utilized over the years. AI can analyze large data sets with heightened accuracy, reduce inter-observer variability, and can standardize the interpretation of radiologic and histopathologic lesions. Therefore, this study aims to review the use of AI in the detection and differentiation of pancreatic space-occupying lesions and to compare AI-assisted endoscopic ultrasound (EUS) with conventional EUS in terms of their detection capabilities. Methods: Literature searches were conducted through PubMed/Medline, SCOPUS, and Embase to identify studies eligible for inclusion. Original articles, including observational studies, randomized control trials, systematic reviews, meta-analyses, and case series specifically focused on AI-assisted EUS in adults, were included. Data were extracted and pooled, and a meta-analysis was conducted using Meta-xl. For results exhibiting significant heterogeneity, a random-effects model was employed; otherwise, a fixed-effects model was utilized. Results: A total of 21 studies were included in the review with 4 studies pooled for a meta-analysis. A pooled accuracy of 93.6% (CI 90.4-96.8%) was found using the random-effects model on four studies that showed significant heterogeneity (P<0.05) in the Cochrane’s Q test. Further, a pooled sensitivity of 93.9% (CI 92.4-95.3%) was found using a fixed-effects model on seven studies that showed no significant heterogeneity in the Cochrane’s Q test. When it came to pooled specificity, a fixed-effects model was utilized in six studies that showed no significant heterogeneity in the Cochrane’s Q test and determined as 93.1% (CI 90.7-95.4%). The pooled positive predictive value which was done using the random-effects model on six studies that showed significant heterogeneity was 91.6% (CI 87.3-95.8%). The pooled negative predictive value which was done using the random-effects model on six studies that showed significant heterogeneity was 93.6% (CI 90.4-96.8%). Conclusion: AI-assisted EUS shows a high degree of accuracy in the detection and differentiation of pancreatic space-occupying lesions over conventional EUS. Its application may promote prompt and accurate diagnosis of pancreatic pathologies.

show abstract

Artificial Intelligence in Endoscopic Ultrasound for Pancreatic Cancer: Where Are We Now and What Does the Future Entail?

Cited by 19 publications

References 69 publications

Role of Endoscopic Ultrasound in Diagnosis of Pancreatic Ductal Adenocarcinoma

Role of Endoscopic Ultrasound in Diagnosis of Pancreatic Ductal Adenocarcinoma

Artificial Intelligence in Endoscopic Ultrasonography-Guided Fine-Needle Aspiration/Biopsy (EUS-FNA/B) for Solid Pancreatic Lesions: Opportunities and Challenges

Artificial intelligence assisted endoscopic ultrasound for detection of pancreatic space occupying lesion: A systematic review and meta-analysis

Contact Info

Product

Resources

About