基于卷积神经网络的肝细粒棘球蚴病超声分型辅助诊断系统多中心效能研究

doi:10.12140/j.issn.1000-7423.2025.05.004

中国寄生虫学与寄生虫病杂志 ›› 2025, Vol. 43 ›› Issue (5): 619-626.doi: 10.12140/j.issn.1000-7423.2025.05.004

基于卷积神经网络的肝细粒棘球蚴病超声分型辅助诊断系统多中心效能研究

田静¹(), 王晓荣¹, 伊力亚斯·阿卜杜艾尼², 杨凌菲¹, 陈璐¹, 高红英³, 周娜⁴, 李玉玲⁵, 崔亮⁶, 宋涛¹^,⁷^,^*()()

1 新疆医科大学第一附属医院，新疆乌鲁木齐 830054
2 新疆医科大学医学工程技术学院，新疆乌鲁木齐 830011
3 新疆伊犁州友谊医院，新疆伊宁 835000
4 新疆哈密市中心医院，新疆哈密 839000
5 新疆阿勒泰地区人民医院，新疆阿勒泰 836500
6 新疆新源县人民医院，新疆新源 835800
7 省部共建中亚高发病成因与防治国家重点实验室，新疆乌鲁木齐 830000

收稿日期:2025-05-29 修回日期:2025-09-15 出版日期:2025-10-30 发布日期:2025-10-28
通讯作者: *宋涛（ORCID：0000-0002-7189-6671），女，博士，教授，从事腹部脏器及浅表器官的超声诊断。E-mail：doctorsongtao@163.com
作者简介:田静，女，硕士研究生，从事腹部脏器及浅表器官的超声诊断。E-mail：2186741450@qq.com
基金资助:
中央引导地方科技发展专项资金(ZYYD2024JD12)

Effectiveness of an auxiliary diagnosis system for ultrasound classification of cystic echinococcosis based on the convolution neural network: A multicenter study

TIAN Jing¹(), WANG Xiaorong¹, YILIYASI Abuduaini², YANG Lingfei¹, CHEN Lu¹, GAO Hongying³, ZHOU Na⁴, LI Yuling⁵, CUI Liang⁶, SONG Tao¹^,⁷^,^*()()

1 First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, Xinjiang, China
2 School of Medical Engineering Technology, Xinjiang Medical University, Urumqi 830011, Xinjiang, China
3 Friendship Hospital of Yili Prefecture, Yining 835000, Xinjiang, China
4 Central Hospital of Hami City, Hami 839000, Xinjiang, China
5 People’s Hospital of Altay Region, Altay 836500, Xinjiang, China
6 People’s Hospital of Xinyuan County, Xinyuan 835800, Xinjiang, China
7 State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Urumqi 830000, Xinjiang, China

Received:2025-05-29 Revised:2025-09-15 Online:2025-10-30 Published:2025-10-28
Contact: *E-mail: doctorsongtao@163.com
Supported by:
Central Government Special Fund for Guiding Local Science and Technology Development(ZYYD2024JD12)

摘要/Abstract

摘要：

目的探讨基于卷积神经网络的ResNet50模型辅助肝细粒棘球蚴病（HCE）超声分型诊断的应用价值。方法回顾性收集2020年1月至2024年6月新疆医科大学第一附属医院等5家医院（分别为A~E组，其中A组为省级三级甲等医院，B~D组为地州级三级甲等医院，E组为县级二级甲等医院；另将4家基层医院合并为F组，5家医院合并为G组）经术后病理或多种影像学长期随访确诊为HCE患者的肝脏内病灶超声图像，分别由1名低年资超声医师和1名高年资超声医师按照CE1（单囊型）、CE2（多子囊型）、CE3（内囊塌陷型）、CE4（坏死实变型）和CE5（钙化型）等5种类型进行分型诊断，再上传至基于ResNet50模型的HCE智能分型诊断超声影像云平台进行分型诊断。采用卡方检验比较各组间低、高年资超声医师和ResNet50模型HCE分型诊断准确率，3组比较有统计学意义时，进一步采用Bonferroni校正进行两两比较；采用kappa分析评价诊断一致性。结果共纳入952例HCE患者的1 596张HCE超声图像，各组低、高年资超声医师和ResNet50模型诊断准确率分别为：A组88.3%（594/673）、95.7%（644/673）、89.2%（600/673），B组72.7%（157/216）、86.6%（187/216）、89.8%（194/216），C组77.6%（232/299）、87.6%（262/299）、91.3%（273/299），D组74.5%（164/220）、85.5%（188/220）、90.5%（199/220），E组69.1%（130/188）、84.0%（158/188）、88.3%（166/188），F组75.1%（693/923）、86.1%（795/923）、90.1%（832/923），G组80.6%（1 287/1 596）、90.2%（1 439/1 596）、89.7%（1 432/1 596）。A组ResNet50模型诊断总体准确率低于高年资超声医师（χ² = 27.140，P < 0.05）；B、C、D与E组的ResNet50模型总体诊断准确率均高于低年资超声医师（χ² = 25.381、24.307、21.121、24.213，均P < 0.05）；F组ResNet50模型总体诊断准确率高于低、高年资超声医师（χ² = 82.654，P < 0.05）；G组ResNet50模型总体诊断准确率高于低年资超声医师（χ² = 80.749，P < 0.05）；以上经Bonferroni校正后两两比较均P < 0.025。各组中ResNet50模型与高年资超声医师诊断一致性最高（kappa值均 ≥ 0.836），低年资与高年资超声医师间诊断一致性稍低（kappa值为0.754~0.863），ResNet50模型与低年资超声医师间诊断一致性最低（kappa值为0.674~0.855）。ResNet50模型中CE1最易被误诊为CE3，占CE1误诊的68.6%（24/35）；CE2易被误诊为 CE4、CE3，分别占CE2 误诊的37.9%（11/29）、31.0%（9/29）；CE3最易被误诊为CE2，占CE3误诊的65.6%（21/32）；CE4与CE5相互容易被误诊（61.9%，26/42；57.9%，22/38）。结论基于ResNet50架构的卷积神经网络模型可有效解析HCE分型的超声特征，为基层医疗机构医师提供辅助诊断支持，提升分型判读的准确性。

关键词: 肝细粒棘球蚴病, 人工智能, 卷积神经网络, 超声检查, 诊断准确性

Abstract:

Objective To examine the value of the convolution neural network-based ResNet50 model in assisting ultrasound classification of hepatic cystic echinococcosis (HCE). Methods Ultrasound images of intrahepatic lesions were retrospectively collected from HCE patients that were definitely diagnosed with postoperative pathology or long-term follow-up of multiple imaging tools admitted to five medical institutions including First Affiliated Hospital of Xinjiang Medical University (groups A to E, among them, group A is a provincial-level tertiary grade A hospital, groups B to D are prefecture level tertiary grade A hospitals, and group E is a county-level secondary Grade A hospital; and four grassroots hospitals were merged into group F and 5 hospitals merged into group G) during the period from January 2020 through June 2024. All images were classified by a junior sonographer and a senior sonographer according to five types of CE1 (univesicular cysts), CE2 (multiple daughter cysts), CE3 (cyst with detachment of laminated membrane), CE4 (heterogeneous or hyperechoic degenerative contents), and CE5 (calcified cysts), and uploaded to the ResNet50 model-based intelligent classification diagnosis ultrasound imaging cloud platform for classification diagnosis of HCE. The diagnostic accuracy of HCE classification by junior sonographers, senior sonographers and ResNet50 model was compared among groups using chi-square test, and pairwise comparisons were conducted with Bonferroni correction. In addition, the diagnostic consistency was evaluated with kappa analysis. Results A total of 1 596 ultrasound images of HCE were included from 952 HCE patients. The diagnostic accuracy of HCE classification by junior sonographers, senior sonographers and ResNet50 model were 88.3% (594/673), 95.7% (644/673) and 89.2% (600/673) in Group A, 72.7% (157/216), 86.6% (187/216), and 89.8% (194/216) in Group B, 77.6% (232/299), 87.6% (262/299) and 91.3% (273/299) in Group C, 74.5% (164/220), 85.5% (188/220) and 90.5% (199/220) in Group D, 69.1% (130/188), 84.0% (158/188) and 88.3% (166/188) in Group E, 75.1% (693/923), 86.1% (795/923) and 90.1% (832/923) in Group F and 80.6% (1 287/1 596), 90.2% (1 439/1 596) and 89.7% (1 432/1 596) in Group G. The overall diagnostic accuracy of HCE classification was lower by the ResNet50 model diagnosis than by senior sonographers in Group A (χ² = 27.140, P < 0.05), and the overall diagnostic accuracy was higher by the ResNet50 model than by junior sonographers in groups B, C, D, and E (χ² = 25.381, 24.307, 21.121 and 24.213; all P < 0.05). The overall diagnostic accuracy of HCE classification was higher by the ResNet50 model than by junior and senior sonographers in Group F (χ² = 82.654, P < 0.05), and the overall diagnostic accuracy was higher by the ResNet50 model than by junior sonographers in Group G (χ² = 80.749, P < 0.05). Moreover, all pairwise comparisons after Bonferroni correction were statistically significant with P < 0.025. The ResNet50 model demonstrated the highest diagnostic consistency with senior sonographers for HCE classification across all groups (kappa ≥ 0.836), followed by between junior and senior sonographers (0.754 ≤ kappa ≤ 0.863), and the lowest diagnostic consistency was seen between the ResNet50 model and junior sonographers (0.674 ≤ kappa ≤ 0.855). In the ResNet50 model, CE1 was most likely to be misdiagnosed as CE3 (68.6%, 24/35), CE2 misdiagnosed as CE4 (37.9%, 11/29) or CE3 (31.0%, 9/29), CE3 misdiagnosed as CE2 (65.6%, 21/32), and CE4 and CE5 were likely to be mutually misdiagnosed (61.9%, 26/42; 57.9%, 22/38). Conclusion The convolutional neural network-based ResNet50 model is effective to decipher the ultrasound features of HCE classification, which provides auxiliary diagnostic supports to doctors in primary medical institutions, and improves the accuracy of classification interpretation.

Key words: Hepatic cystic chinococcosis, Artificial intelligence, Convolutional neural network, Ultrasound examination, Diagnostic accuracy

中图分类号:

R532.32

田静, 王晓荣, 伊力亚斯·阿卜杜艾尼, 杨凌菲, 陈璐, 高红英, 周娜, 李玉玲, 崔亮, 宋涛. 基于卷积神经网络的肝细粒棘球蚴病超声分型辅助诊断系统多中心效能研究[J]. 中国寄生虫学与寄生虫病杂志, 2025, 43(5): 619-626.

TIAN Jing, WANG Xiaorong, YILIYASI Abuduaini, YANG Lingfei, CHEN Lu, GAO Hongying, ZHOU Na, LI Yuling, CUI Liang, SONG Tao. Effectiveness of an auxiliary diagnosis system for ultrasound classification of cystic echinococcosis based on the convolution neural network: A multicenter study[J]. Chinese Journal of Parasitology and Parasitic Diseases, 2025, 43(5): 619-626.

图/表 7

图1

表1

表2

表3

表4

A~G组ResNet50模型HCE分型诊断符合情况

HCE分型（n） HCE category（n）	ResNet50模型 ResNet50 model		低年资医师 Junior physicians		高年资医师Senior physcians		χ²值 χ²value	P值 P value
HCE分型（n） HCE category（n）	病例数 No. case	准确率/% Accuracy rate/%	病例数 No. case	准确率/% Accuracy rate/%	病例数 No. case	准确率/% Accuracy rate/%	χ²值 χ²value	P值 P value
A组 A group
CE1（176）	157	89.2	155	88.1	167	94.9	5.579	0.061
CE2（181）	167	92.3	170	93.9	176	97.2	4.446	0.108
CE3（81）	69	85.2	65	80.2	77	95.1	8.062	0.018
CE4（150）	132	88.0	130	86.7	142	94.7	6.005	0.049
CE5（85）	75	88.2	74	87.1	82	96.5	5.244	0.073
合计 Total（673）	600	89.2	594	88.3	644	95.7^a	27.140	< 0.001
B组 B group
CE1（49）	45	91.8	38	77.6	44	89.8	4.977	0.083
CE2（44）	40	90.9	33	75.0	38	86.4	4.417	0.110
CE3（39）	33	84.6	25	64.1	34	87.2	7.427	0.024
CE4（50）	45	90.0	37	74.0	42	84.0	4.560	0.102
CE5（34）	31	91.2	24	70.6	29	85.3	5.262	0.072
合计 Total（216）	194	89.8	157	72.7^b	187	86.6	25.381	< 0.001
C组 C group
CE1（45）	41	91.1	34	75.6	39	86.7	4.399	0.111
CE2（47）	44	93.6	37	78.7	41	87.2	4.501	0.105
CE3（36）	31	86.1	23	63.9	30	83.3	6.107	0.047
CE4（120）	111	92.5	100	83.3	107	89.2	5.013	0.082
CE5（51）	46	90.2	38	74.5	45	88.2	5.634	0.060
合计 Total（299）	273	91.3	232	77.6^b	262	87.6	24.307	< 0.001
D组 D group
CE1（43）	39	90.7	32	74.4	37	86.0	4.437	0.109
CE2（46）	42	91.3	35	76.1	40	87.0	4.381	0.112
CE3（38）	34	89.5	28	73.7	32	84.2	3.396	0.183
CE4（52）	47	90.4	39	75.0	44	84.6	4.523	0.104
CE5（41）	37	90.2	30	73.2	35	85.4	4.479	0.107
合计 Total（220）	199	90.5	164	74.5^b	188	85.5	21.121	< 0.001
E组 E group
CE1（40）	36	90.0	29	72.5	34	85.0	4.502	0.105
CE2（36）	32	88.9	25	69.4	31	86.1	5.278	0.071
CE3（34）	29	85.3	22	64.7	28	82.4	4.828	0.089
CE4（43）	38	88.4	30	69.8	36	83.7	5.160	0.076
CE5（35）	31	88.6	24	68.6	29	82.9	4.643	0.098
合计 Total（188）	166	88.3	130	69.1^b	158	84.0	24.213	< 0.001
F组 F group
CE1（177）	161	91.0	133	75.1^b	154	87.0	18.193	< 0.001
CE2（173）	158	91.3	130	75.1^b	150	86.7	18.257	< 0.001
CE3（147）	127	86.4	108	73.5^b	124	84.4	9.378	0.009
CE4（265）	241	90.9	206	77.7^b	229	86.4	18.757	< 0.001
CE5（161）	145	90.1	116	72.0^b	138	85.7	19.801	< 0.001
合计 Total（923）	832	90.1	693	75.1^b	795	86.1^a	82.654	< 0.001
G组 G group
CE1（353）	318	91.0	288	81.6^b	321	90.9	17.292	< 0.001
CE2（354）	325	91.8	300	84.7^b	326	92.1	13.099	0.001
CE3（228）	196	86.0	173	75.9^b	201	88.2	14.084	< 0.001
CE4（415）	373	89.9	336	81.0^b	371	89.4	18.151	< 0.001
CE5（246）	220	89.4	190	77.2^b	220	89.4	19.524	< 0.001
合计 Total（1 596）	1 432	89.7	1 287	80.6^b	1 439	90.2	80.749	< 0.001

表4

表5

表6

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特征 Feature	病例数 No. case	构成比/% Proportion/%
年龄组/岁 Age group/years
< 40	291	30.6
40~59	440	46.2
≥ 60	221	23.2
性别 Gender
男 Male	509	53.5
女 Female	443	46.5
民族 Nationality
汉族 Han	390	41.0
维吾尔族 Weiwuer	266	27.9
哈萨克族 Hasake	188	19.7
回族 Hui	48	5.0
蒙古族 Menggu	31	3.3
藏族 Zang	19	2.0
锡伯族 Xibo	10	1.1
犬类接触史 History of exposure to dogs
有 Yes	425	44.6
无 No	527	55.4

评价指标 Evaluation metric	CE1	CE2	CE3	CE4	CE5
精确率/% Precision/%	0.946	0.898	0.838	0.908	0.870
召回率/% Recall/%	0.901	0.918	0.860	0.899	0.894
特异度/% Specificity/%	0.986	0.970	0.972	0.968	0.976
F1分数/% F1 score/%	0.923	0.908	0.849	0.904	0.882

组别 Group	Kappa
组别 Group	低年资医师与模型 Junior physicians and model	高年资医师与模型 Senior physicians and model	低年资与高年资医师 Junior and senior physicians
A	0.855	0.888	0.863
B	0.698	0.849	0.791
C	0.716	0.836	0.774
D	0.687	0.857	0.772
E	0.674	0.840	0.754
F	0.699	0.847	0.775
G	0.766	0.865	0.813

基于卷积神经网络的肝细粒棘球蚴病超声分型辅助诊断系统多中心效能研究

Effectiveness of an auxiliary diagnosis system for ultrasound classification of cystic echinococcosis based on the convolution neural network: A multicenter study

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参考文献 22

相关文章 11

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HCE分型 HCE category	A组 A group	B组 B group	C组 C group	D组 D group	E组 E group	F组 F group	G组 G group
CE1	176	49	45	43	40	177	353
CE2	181	44	47	46	36	173	354
CE3	81	39	36	38	34	147	228
CE4	150	50	120	52	43	265	415
CE5	85	34	51	41	35	161	246
合计 Total	673	216	299	220	188	923	1 596

HCE分型 HCE category	ResNet50模型误诊例数 No. ResNet50 model misdiagnosis
HCE分型 HCE category	CE1	CE2	CE3	CE4	CE5	合计 Total
CE1	-	6	24	2	3	35
CE2	6	-	9	11	3	29
CE3	7	21	-	3	1	32
CE4	1	10	5	-	26	42
CE5	4	0	0	22	-	26
合计 Total	18	37	38	38	33	164

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