Products You May Like
An artificial intelligence (AI) based approach demonstrated diagnostic performance comparable to experienced radiologists in detecting gall bladder cancer at a hospital in Chandigarh, according to a study published in The Lancet Regional Health – Southeast Asia journal.
Gallbladder cancer (GBC) is a highly aggressive malignancy with a poor detection and a high mortality rate. Early diagnosis is challenging because benign gallbladder lesions can have similar imaging features, the researchers said.
The team at Postgraduate Institute of Medical Education and Research (PGIMER) in Chandigarh and Indian Institute of Technology (IIT), New Delhi, aimed to develop and validate a deep learning (DL) model for GBC detection using abdominal ultrasound and compare its performance with radiologists.
Deep learning is a method in AI that teaches computers to process data in a way that is inspired by the human brain.
The study used abdominal ultrasound data from patients with gallbladder lesions acquired between August 2019 and June 2021 at PGIMER, a tertiary care hospital.
A deep learning (DL) model was trained on a dataset of 233 patients, validated on 59 patients, and tested on 273 patients.
The DL model’s performance was evaluated in terms of sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC), which is widely used to measure the accuracy of diagnostic tests.
Two radiologists also independently reviewed the ultrasound images, and their diagnostic performance was compared to the DL model.
In the test set, the DL model had a sensitivity of 92.3 per cent, specificity of 74.4 per cent, and an AUC of 0.887 for detecting GBC, which was comparable to both radiologists, according to the study.
The DL-based approach showed high sensitivity and AUC for detecting GBC in the presence of stones, contracted gallbladders, small lesion size (less than 10 mm), and neck lesions, which were also comparable to the radiologists, the researchers said.
The DL model exhibited higher sensitivity for detecting the mural thickening type of GBC compared to one of the radiologists, despite a reduced specificity, they said.
“The DL-based approach demonstrated diagnostic performance comparable to experienced radiologists in detecting GBC using ultrasound,” the authors of the study noted.
“Further multicentre studies are recommended to fully explore the potential of DL-based GBC diagnosis,” they added.
The authors acknowledged some limitations of the study. The findings are based on a single-centre dataset, and multicentre studies are needed for broader validation.
The study has a knowledge cutoff date in 2021, and subsequent developments in DL and GBC diagnosis may not be reflected, they added.