Ignite Creation Date:
2024-10-26 @ 3:42 PM
Last Modification Date:
2024-10-26 @ 3:42 PM
Study NCT ID:
NCT06641947
Status:
COMPLETED
Last Update Posted:
None
First Post:
2024-10-12
Brief Title:
Differentiation Benign and Malignant Pancreatic Lesions
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Enhancing the Accuracy of Classifying Benign and Malignant Pancreatic Lesions Using the MVIT-MLKA Model A Comprehensive Evaluation and Comparative Study
Status:
COMPLETED
Status Verified Date:
2024-10
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
D
Brief Summary:
The MVIT-MLKA model with its complex architecture combining CNNs and Transformers excels in image feature extraction and capturing long-range dependencies This gives it strong adaptability and robustness in lesion detection and classification tasks Compared to traditional machine learning methods and other deep learning models MVIT-MLKA not only performs better in terms of accuracy sensitivity and specificity but also helps reduce inter-observer variability enhancing diagnostic consistency among physicians
Although the model showed slight fluctuations in performance on external datasets it still outperforms other models overall and holds significant potential for clinical applications With further optimization to improve its generalization capabilities MVIT-MLKA could become a powerful tool for diagnosing benign and malignant lesions providing more consistent and accurate support in clinical practice
Although the model showed slight fluctuations in performance on external datasets it still outperforms other models overall and holds significant potential for clinical applications With further optimization to improve its generalization capabilities MVIT-MLKA could become a powerful tool for diagnosing benign and malignant lesions providing more consistent and accurate support in clinical practice
Detailed Description:
Accurate differentiation between benign and malignant pancreatic lesions is critical for patient management This study aimed to develop and validate a novel deep learning network using baseline computed tomography images to predict benign and malignant pancreatic lesions This retrospective study across three medical centers constituted a training cohort an internal testing cohort and an external validation cohorts A novel hybrid model Multi-Scale Large Kernel Attention with Mobile Vision Transformer MVIT-MLKA integrating CNN and Transformer architectures was developed to classify pancreatic lesions We compared the models performance with traditional machine learning and deep learning methods Moreover we evaluated radiologists diagnostic accuracy with and without the optimal model assistanceThe MVIT-MLKA model demonstrated superior performance for predicting pancreatic lesions outperforming traditional models and standard CNNs and Transformers Radiologists assisted by the MVIT-MLKA model showed significant improvements in diagnostic performance compared to those without model assistance with notable increases in both accuracy and sensitivity Model interpretability was enhanced through Grad-CAM visualization effectively highlighting key lesion areasThe MVIT-MLKA model effectively differentiates between benign and malignant pancreatic lesions surpassing traditional methods and enhancing radiologist performance This suggests that integrating advanced deep learning model into clinical practice has the potential to reduce diagnostic errors and optimize treatment strategies in clinical practices
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None