肛瘘诊治过程中多模态数据应用初探

柳奕诚; 叶倩倩; 徐进; 赵文俊; 吴闯; 汪庆明

doi:10.16766/j.cnki.issn.1674-4152.003896

肛瘘诊治过程中多模态数据应用初探

doi: 10.16766/j.cnki.issn.1674-4152.003896

柳奕诚¹,
叶倩倩²,
徐进¹,
赵文俊¹,
吴闯¹,
汪庆明^{1, 2, ,}

1.
上海市宝山区中西医结合医院肛肠科，上海 201901
2.
上海中医药大学附属曙光医院肛肠科, 上海 201203

基金项目:

“十四五”中医特色专科项目 ZYTSZK1-8

上海市科学技术委员会医学创新研究专项项目 23Y11921800

详细信息

通讯作者:
汪庆明，E-mail：davidwqm@163.com

中图分类号: R657.16
计量
- 文章访问数: 11
- HTML全文浏览量: 7
- PDF下载量: 1
- 被引次数: 0
出版历程
- 收稿日期: 2024-02-15
- 网络出版日期: 2025-03-27

A preliminary study on the application of multimodal data in diagnosing and treating anal leakage

1.
Department of Anorectal Surgery, Shanghai Baoshan Hospital of Intergrated Traditonal Chinese and Western Medicine, Shanghai 201901, China

摘要

摘要: 肛瘘是一种常见肛周疾病，手术是唯一的治疗途径，但术后常伴有复发率高、失禁率高和致畸率高的特点。术前四诊参与辅助检查有助于完善治疗方案，制定精准化的手术路径，改善临床预后。近年来多模态领域发展迅速，在医学研究中得到广泛应用，而肛瘘的诊治过程也涵盖多种模态数据分析，为其客观化研究进行补充。本文将结合中西多模态研究与肛瘘的交叉部分，从传统多模态与四诊多模态2个角度进行切入，分析当前研究现状与局限：(1)传统多模态基于图像包括核磁共振(magnetic resonance imaging, MRI)与超声(ultrasound)，初步探索上述技术在肛瘘术前、术中与预后研究中的开展; (2)四诊多模态数据囊括“望、闻、问、切”在肛瘘诊治中的应用，回顾当前四诊技术的实践。通过横向比对传统与四诊多模态数据的多维应用，总结当前其他疾病领域中多模态数据的前沿应用，对比机器学习与深度学习技术等多层次融合，以期为肛瘘诊治方案开拓研究视野，为多模态、跨模态在临床的研究应用中提供参考，以期更好地改善肛瘘预后。
- 肛瘘 /
- 多模态 /
- 核磁共振 /
- B超 /
- 四诊
Abstract: Anal fistula is a common perianal disease, with surgical treatment being the only effective treatment approach. However, it is often associated with high recurrence, incontinence, and teratogenicity after surgery. The information provided by preoperative quadruple diagnosis and participation in auxiliary examinations can improve the treatment planning, guide precise surgical pathway, and improve clinical prognosis. In recent years, the multimodal approaches have advanced rapidly and are increasingly applied in medical research. This includes the diagnosis and treatment of anal fistula, where multimodal data analysis enhances objectivity. In this paper, we explores the intersection of Chinese and Western multimodal research and anal fistula, analyzing the current status and limitations of the research from two perspectives: (1) Traditional multimodal imaging, including magnetic resonance imaging (MRI) and ultrasound, with preliminary explorations of the above techniques in the preoperative, intraoperative, and prognostic assessment of anal fistula. (2) The application of four diagnostic multimodalities "looking, smelling, questioning, and feeling" in the diagnosis and treatment of anal fistula, with a review of current practices in these diagnostic techniques. By comparing the multidimensional applications of traditional and four-diagnosis multimodal data, examining the latest advancements in multimodal data in other disease areas, and comparing the multilevel integration of machine learning and deep learning techniques, we hope to expand research perspectives in the diagnosis and treatment of anal fistula. Additionally, this paper will provide references for the application of multimodal and intermodal research in clinical practice to improve the prognosis of anal fistula.
- Anal fistula /
- Multimodal /
- Magnetic resonance imaging /
- Ultrasound /
- Four-diagnosis

HTML全文

表 1 望舌、望面的多模态研究进展

Table 1. Advances in multimodal studies of tongue and face reading

望诊分类	研究	疾病/分类	方法	样本量
望舌	JIANG T^[41]	NAFLD	R-CNN	1 778
	ZHANG G^[42]	COVID19	AlexNet	488
	BALASUBRAMANIYAN S^[43]	2型糖尿病	ResNet50	3 477
	MA J^[44]	9种体质	ResNet50, VGG-16	22 482
	WEN G^[45]	9种体质	ResNet18	46 753
望面	ABDALMAGEED W^[46]	肾上腺性变态征	DNN	246
	PORRAS A R^[47]	努南综合征、威廉姆斯综合征	LBP, SVM	447
	RAJNOHA M^[48]	帕金森病	XGBoost	100
	ZHANG B^[49]	多种疾病	SVM, K-NN	1 180
	UMEDA-KAMEYAMA Y^[50]	阿尔茨海默病	Xception, SENet50, ResNet50, VGG16, CNN	238
注：DNN为深度神经网络(deep neural networks); LBP为局部二值模式(local binary patterns); SVM为支持向量机(support vector machine); XGboost为分布式梯度增强模型(eXtreme gradient boosting); K-NN为K最近邻算法(k-nearest neighbors algorithm); Xception为一种结合STOA残差网络的极端Inception模型; SENet为压缩与激励网络(squeeze-and-excitation networks); ResNet为残差网络(residual network); VGG为视觉几何组(visual geometry group); CNN为卷积神经网络(convolutional neural networks); R-CNN为区域卷积神经网络(region convolutional neural networks); AlexNet由多伦多大学Alex Krizhevsky等所设计，用于图像识别的卷积神经网络，为2012年ImageNet冠军模型。

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