# An integrative deep learning model based on dual-mode ultrasound for diagnosing gallbladder polyps

## TL;DR

This paper introduces a deep learning model that uses ultrasound to automatically diagnose gallbladder polyps and distinguish between benign and malignant cases.

## Contribution

The novel contribution is an integrative deep learning model using dual-mode ultrasound for accurate gallbladder polyp diagnosis and segmentation.

## Key findings

- The model achieved Dice coefficient of 0.912 and IoU of 0.838 for lesion segmentation.
- The model showed AUCs of 0.829 and 0.802 for differentiating non-neoplastic from neoplastic polyps.
- The model improved junior radiologists' diagnostic performance and could reduce unnecessary surgeries.

## Abstract

The aim of this study was to develop an artificial intelligence model to automatically differentiate between non-neoplastic and neoplastic gallbladder polyps, while also distinguishing benign from malignant polyps.

Patients with gallbladder polyps who underwent cholecystectomy from January 2022 to June 2023 were recruited from two hospitals retrospectively. Conventional ultrasound findings and clinical characteristics of patients before cholecystectomy were acquired. Ultrasound image blocks of gallbladder lesions were automatically segmented by the Unet network for diagnosis. A fusion deep learning model based on dual-mode ultrasound (grey-scale ultrasound and colour Doppler flow imaging) was established to diagnose gallbladder polyps and validated in the validation and test set. Finally, we compared the diagnostic efficiency of the model with that of radiologists and guidelines.

A total of 339 patients (mean ages 53.17 ± 15.89, 182 females) were enroled in this study. The Dice coefficient and intersection over union (IoU) value of the automatic segmentation based on the Unet-efficientnet-b4 network were 0.912 and 0.838. In differentiating non-neoplastic from neoplastic polyps, the integrative deep learning (IDL) model showed area under the curves (AUCs) of 0.829 and 0.802 in validation and test sets, respectively. In differentiating benign and malignant polyps, the IDL model showed AUCs of 0.844 and 0.839 in validation and test sets, respectively. In the test set, the diagnostic performance of two junior radiologists was improved with the assistance of the IDL model.

The IDL model based on dual-mode ultrasound could achieve accurate and automatic segmentation of gallbladder lesions, and showed excellent diagnostic performance for diagnosing gallbladder polyps.

We developed a deep learning model based on conventional ultrasound that performs gallbladder segmentation while differentiating neoplastic from non-neoplastic polyps and benign from malignant polyps.

Diagnosing gallbladder polyps through a deep learning model based on conventional ultrasound presents challenges.IDL model enables automated segmentation of the gallbladder and diagnosis of gallbladder polyps.The IDL model is a reliable tool to assist junior radiologists in diagnosis and has potential for reducing unnecessary cholecystectomies.

Diagnosing gallbladder polyps through a deep learning model based on conventional ultrasound presents challenges.

IDL model enables automated segmentation of the gallbladder and diagnosis of gallbladder polyps.

The IDL model is a reliable tool to assist junior radiologists in diagnosis and has potential for reducing unnecessary cholecystectomies.