User login
PHILADELPHIA —
This was a transatlantic collaborative effort involving researchers in Portugal, Spain, the United States, and Brazil, and the AI tool “works on different platforms and different devices,” Miguel Mascarenhas, MD, PhD, with Centro Hospitalar Universitário de São João, Porto, Portugal, said in a presentation at the annual meeting of the American College of Gastroenterology.
Mascarenhas noted that pancreatic cystic lesions (PCLs) are a common incidental finding during imaging and are differentiated by whether they’re mucinous PCLs (M-PCLs) or non-mucinous PCLs (NM-PCLs). The malignancy risk is almost exclusive of PCL with a mucinous phenotype.
Pancreatic solid lesions are also prevalent, and differentiation is challenging. Pancreatic ductal adenocarcinoma (P-DAC) is the most common pancreatic solid lesion and has a poor prognosis because of late-stage disease at diagnosis. Pancreatic neuroendocrine tumors (P-NETs) are less common but have malignant potential.
EUS is the “gold standard” for pancreatic lesion evaluation, but its diagnostic accuracy is suboptimal, particularly for lesions < 10 mm, Mascarenhas noted.
With an eye toward improving diagnostic accuracy, he and colleagues developed a convolutional neural network for detecting and differentiating cystic (M-PCL and NM-PCL) and solid (P-DAC and P-NET) pancreatic lesions.
They leveraged data from 378 EUS exams with 126,000 still images — 19,528 M-PCL, 8175 NM-PCL, 64,286 P-DAC, 29,153 P-NET, and 4858 normal pancreas images.
The AI tool demonstrated 99.1% accuracy for identifying normal pancreatic tissue, and it showed 99% and 99.8% accuracy for M-PCL and NM-PCL, respectively.
For pancreatic solid lesions, P-DAC and P-NET were distinguished with 94% accuracy, with 98.7% and 83.6% sensitivity for P-DAC and P-NET, respectively.
Real-Time Validation Next
“AI is delivering promising results throughout medicine, but particularly in gastroenterology, which is one of the most fertile areas of AI research. This comes mostly from the deployment of deep-learning models, most of them convolutional neural networks, which are highly efficient for image analysis,” Mascarenhas told attendees.
This is the “first worldwide convolutional neural network” capable of detecting and differentiating both cystic and solid pancreatic lesions. The use of a large dataset from four centers in two continents helps minimize the impact of demographic bias, Mascarenhas added.
The study is based on still images, not full videos, he noted. As a next step, the team is conducting a multicenter study focused on real-time clinical validation of the model during EUS procedures.
“AI has the potential to improve the diagnostic accuracy of endoscopic ultrasound. We’re just on the tip of the iceberg. There is enormous potential to harness AI, and we welcome all the groups that might want to join our research,” Mascarenhas said.
Brennan Spiegel, MD, MSHS, AGAF, director of Health Services Research at Cedars-Sinai Medical Center, Los Angeles, who wasn’t involved in the study, is optimistic about emerging applications for AI.
“AI holds incredible promise in gastroenterology, especially for diagnosing complex pancreatic lesions where early, accurate differentiation can be lifesaving,” Spiegel said in an interview.
“This study’s high accuracy across diverse datasets is encouraging; however, as a retrospective analysis, it leaves the real-time clinical impact still to be proven. Prospective studies will be essential to confirm AI’s role in enhancing our diagnostic capabilities,” Spiegel cautioned.
“More generally, AI is rapidly transforming gastroenterology by enhancing our ability to detect, differentiate, and monitor conditions with unprecedented precision. From improving early cancer detection to guiding complex diagnostic procedures, AI stands to become an invaluable tool that complements clinical expertise. As we refine these technologies, the potential for AI to elevate both diagnostic accuracy and patient outcomes in GI is truly remarkable,” Spiegel said.
The study had no specific funding. Mascarenhas and Spiegel have declared no conflicts of interest.
A version of this article appeared on Medscape.com.
PHILADELPHIA —
This was a transatlantic collaborative effort involving researchers in Portugal, Spain, the United States, and Brazil, and the AI tool “works on different platforms and different devices,” Miguel Mascarenhas, MD, PhD, with Centro Hospitalar Universitário de São João, Porto, Portugal, said in a presentation at the annual meeting of the American College of Gastroenterology.
Mascarenhas noted that pancreatic cystic lesions (PCLs) are a common incidental finding during imaging and are differentiated by whether they’re mucinous PCLs (M-PCLs) or non-mucinous PCLs (NM-PCLs). The malignancy risk is almost exclusive of PCL with a mucinous phenotype.
Pancreatic solid lesions are also prevalent, and differentiation is challenging. Pancreatic ductal adenocarcinoma (P-DAC) is the most common pancreatic solid lesion and has a poor prognosis because of late-stage disease at diagnosis. Pancreatic neuroendocrine tumors (P-NETs) are less common but have malignant potential.
EUS is the “gold standard” for pancreatic lesion evaluation, but its diagnostic accuracy is suboptimal, particularly for lesions < 10 mm, Mascarenhas noted.
With an eye toward improving diagnostic accuracy, he and colleagues developed a convolutional neural network for detecting and differentiating cystic (M-PCL and NM-PCL) and solid (P-DAC and P-NET) pancreatic lesions.
They leveraged data from 378 EUS exams with 126,000 still images — 19,528 M-PCL, 8175 NM-PCL, 64,286 P-DAC, 29,153 P-NET, and 4858 normal pancreas images.
The AI tool demonstrated 99.1% accuracy for identifying normal pancreatic tissue, and it showed 99% and 99.8% accuracy for M-PCL and NM-PCL, respectively.
For pancreatic solid lesions, P-DAC and P-NET were distinguished with 94% accuracy, with 98.7% and 83.6% sensitivity for P-DAC and P-NET, respectively.
Real-Time Validation Next
“AI is delivering promising results throughout medicine, but particularly in gastroenterology, which is one of the most fertile areas of AI research. This comes mostly from the deployment of deep-learning models, most of them convolutional neural networks, which are highly efficient for image analysis,” Mascarenhas told attendees.
This is the “first worldwide convolutional neural network” capable of detecting and differentiating both cystic and solid pancreatic lesions. The use of a large dataset from four centers in two continents helps minimize the impact of demographic bias, Mascarenhas added.
The study is based on still images, not full videos, he noted. As a next step, the team is conducting a multicenter study focused on real-time clinical validation of the model during EUS procedures.
“AI has the potential to improve the diagnostic accuracy of endoscopic ultrasound. We’re just on the tip of the iceberg. There is enormous potential to harness AI, and we welcome all the groups that might want to join our research,” Mascarenhas said.
Brennan Spiegel, MD, MSHS, AGAF, director of Health Services Research at Cedars-Sinai Medical Center, Los Angeles, who wasn’t involved in the study, is optimistic about emerging applications for AI.
“AI holds incredible promise in gastroenterology, especially for diagnosing complex pancreatic lesions where early, accurate differentiation can be lifesaving,” Spiegel said in an interview.
“This study’s high accuracy across diverse datasets is encouraging; however, as a retrospective analysis, it leaves the real-time clinical impact still to be proven. Prospective studies will be essential to confirm AI’s role in enhancing our diagnostic capabilities,” Spiegel cautioned.
“More generally, AI is rapidly transforming gastroenterology by enhancing our ability to detect, differentiate, and monitor conditions with unprecedented precision. From improving early cancer detection to guiding complex diagnostic procedures, AI stands to become an invaluable tool that complements clinical expertise. As we refine these technologies, the potential for AI to elevate both diagnostic accuracy and patient outcomes in GI is truly remarkable,” Spiegel said.
The study had no specific funding. Mascarenhas and Spiegel have declared no conflicts of interest.
A version of this article appeared on Medscape.com.
PHILADELPHIA —
This was a transatlantic collaborative effort involving researchers in Portugal, Spain, the United States, and Brazil, and the AI tool “works on different platforms and different devices,” Miguel Mascarenhas, MD, PhD, with Centro Hospitalar Universitário de São João, Porto, Portugal, said in a presentation at the annual meeting of the American College of Gastroenterology.
Mascarenhas noted that pancreatic cystic lesions (PCLs) are a common incidental finding during imaging and are differentiated by whether they’re mucinous PCLs (M-PCLs) or non-mucinous PCLs (NM-PCLs). The malignancy risk is almost exclusive of PCL with a mucinous phenotype.
Pancreatic solid lesions are also prevalent, and differentiation is challenging. Pancreatic ductal adenocarcinoma (P-DAC) is the most common pancreatic solid lesion and has a poor prognosis because of late-stage disease at diagnosis. Pancreatic neuroendocrine tumors (P-NETs) are less common but have malignant potential.
EUS is the “gold standard” for pancreatic lesion evaluation, but its diagnostic accuracy is suboptimal, particularly for lesions < 10 mm, Mascarenhas noted.
With an eye toward improving diagnostic accuracy, he and colleagues developed a convolutional neural network for detecting and differentiating cystic (M-PCL and NM-PCL) and solid (P-DAC and P-NET) pancreatic lesions.
They leveraged data from 378 EUS exams with 126,000 still images — 19,528 M-PCL, 8175 NM-PCL, 64,286 P-DAC, 29,153 P-NET, and 4858 normal pancreas images.
The AI tool demonstrated 99.1% accuracy for identifying normal pancreatic tissue, and it showed 99% and 99.8% accuracy for M-PCL and NM-PCL, respectively.
For pancreatic solid lesions, P-DAC and P-NET were distinguished with 94% accuracy, with 98.7% and 83.6% sensitivity for P-DAC and P-NET, respectively.
Real-Time Validation Next
“AI is delivering promising results throughout medicine, but particularly in gastroenterology, which is one of the most fertile areas of AI research. This comes mostly from the deployment of deep-learning models, most of them convolutional neural networks, which are highly efficient for image analysis,” Mascarenhas told attendees.
This is the “first worldwide convolutional neural network” capable of detecting and differentiating both cystic and solid pancreatic lesions. The use of a large dataset from four centers in two continents helps minimize the impact of demographic bias, Mascarenhas added.
The study is based on still images, not full videos, he noted. As a next step, the team is conducting a multicenter study focused on real-time clinical validation of the model during EUS procedures.
“AI has the potential to improve the diagnostic accuracy of endoscopic ultrasound. We’re just on the tip of the iceberg. There is enormous potential to harness AI, and we welcome all the groups that might want to join our research,” Mascarenhas said.
Brennan Spiegel, MD, MSHS, AGAF, director of Health Services Research at Cedars-Sinai Medical Center, Los Angeles, who wasn’t involved in the study, is optimistic about emerging applications for AI.
“AI holds incredible promise in gastroenterology, especially for diagnosing complex pancreatic lesions where early, accurate differentiation can be lifesaving,” Spiegel said in an interview.
“This study’s high accuracy across diverse datasets is encouraging; however, as a retrospective analysis, it leaves the real-time clinical impact still to be proven. Prospective studies will be essential to confirm AI’s role in enhancing our diagnostic capabilities,” Spiegel cautioned.
“More generally, AI is rapidly transforming gastroenterology by enhancing our ability to detect, differentiate, and monitor conditions with unprecedented precision. From improving early cancer detection to guiding complex diagnostic procedures, AI stands to become an invaluable tool that complements clinical expertise. As we refine these technologies, the potential for AI to elevate both diagnostic accuracy and patient outcomes in GI is truly remarkable,” Spiegel said.
The study had no specific funding. Mascarenhas and Spiegel have declared no conflicts of interest.
A version of this article appeared on Medscape.com.
FROM ACG 2024