Health
Deep learning model may help distinguish between acute diverticulitis and colon cancer, according to CT study
Noting that overlapping image features in contrast-enhanced computed tomography (CT) can make it difficult to distinguish between acute diverticulitis and colon cancer, the researchers believe that new deep learning models can improve the sensitivity and sensitivity of these conditions. It states that it may improve specificity.
In a recently published retrospective study, JAMA network open, the researchers analyzed the three-dimensional (3D) convolutional neural network of 585 patients (mean age 63.2 years) who underwent surgery for colon cancer or acute diverticulitis between July 1, 2005, and October 1, 2020. Developed and tested a network (CNN). Within 60 days before surgery, he had a CT scan that showed segmental thickening of the colon regardless of stage.
The authors of the study noted that the sensitivity and specificity of the 3D CNN model were 83.3% and 86.6%, while the mean sensitivity and specificity of the radiologist reader were 77.6% and 81.6%, respectively. bottom. The findings show that the combination of deep learning models and radiologist assessment has an 8% (85.6%) increase in sensitivity and a 9.7% (91.3%) increase in specificity over radiologist assessment.
The study authors also noted a reduction in false negative rates with the 3D CNN model. According to this study, the supplemental use of the 3D CNN algorithm reduced the overall false negative rate for radiology readers in this study from 22% to 14.3%. Specifically, the 3D CNN model reduced the false-negative rate for certified radiologists by 4% (14% to 10%) and the false-negative rate for radiology residents by 9.9% (26% to 16.1%). bottom.
The researchers said the reduction in false-negative findings has “major clinical implications” for patients with colon cancer or acute diverticulitis.
“At the perforation stage, both entities require emergency surgery. However, the surgical strategies differ. A limited resection of the affected bowel is sufficient in the case of diverticulitis).A high level of certainty in surgical planning would improve patient stratification and thus limit postoperative complications and potentially ,” Technical University of Munich, Germany and colleagues.
(Editor’s Note: For related content, seeDiagnosis of Pancreatic Lesions on Abdominal CT: Study Finds Deep Learning System Comparable to Radiologist Evaluation” When “Can new deep learning tools enhance CT detection of pancreatic cancer?”)
In the case of early-stage colon cancer and acute diverticulitis, the study authors found that subtle CT findings, such as adjacent fat stranding and focal thickening of the intestinal wall, could be misinterpreted as peristalsis or due to intestinal filling. I warned you that it could be confusing. Braren et al. also noted that secondary changes such as mesenteric stranding and abscess formation may dominate CT features in cases with advanced colon cancer or complicated acute diverticulitis. Did.
Regarding study limitations, the study authors acknowledged that the AI model was trained and tested on a single institutional dataset, which may limit the broader application of the study’s findings. also noted that imaging features such as fat stranding may have been masked by adversarial noise (0.01 variance threshold) that impacted AI model performance. Noting that this study focused on the most frequent benign and malignant entities of intestinal wall thickening, the authors of the study suggest that future studies will assess a broader range of malignant and benign entities, We argued that multiparameter data integration should be incorporated to assess and possibly improve intestinal wall function. AI model.
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