Study on the Rapid Imaging Diagnosis of Femoral Neck Fracture by Artificial Intelligence

doi:10.3969/j.issn.1671-2072.2024.03.007

Chinese Journal of Forensic Sciences ›› 2024 ›› Issue (3): 49-54.DOI: 10.3969/j.issn.1671-2072.2024.03.007

• Special Topic Discussion：New Quality Productive Forces in the Field of Forensic Appraisal • Previous Articles Next Articles

Study on the Rapid Imaging Diagnosis of Femoral Neck Fracture by Artificial Intelligence

MA Wenjing¹, LIU Fan¹, ZHAO Liang², LIU Hua¹, PEI Jingzhe³, ZHANG Rui⁴, SHI Wei⁴

1. Beijing District Pubic Security Identification Center, Beijing 100192, China; 2. Shanxi Medical University, Taiyuan 030600, China; 3. Beijing Jishuitan Hospital, Beijing 100035, China; 4. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China

Received:2023-10-05 Published:2024-05-15 Online:2024-05-16

股骨颈骨折影像人工智能快速诊断方法研究

马文静¹，刘凡¹，赵亮²，刘华¹，裴京哲³，张睿⁴，施维⁴

1. 北京市公安司法鉴定中心，北京 100192； 2. 山西医科大学，山西太原 030600； 3. 北京积水潭医院，北京 100035；
4. 中国科学院苏州生物医学工程技术研究所，江苏苏州215163

作者简介:马文静（1985—），女，副高级警务，硕士，主要从事法医临床学研究。E-mail: 549836639@qq.com
基金资助:
2019年度国家自然科学基金（201903-24）；公安部“双十计划”重点攻关项目（2021SSGG03）。

Abstract

Abstract: Objective To develop an artificial intelligence (AI) method for the rapid imaging diagnosis of human femoral neck fractures and realize automatic evaluation. Methods A total of 1 018 cases of femoral neck fractures were collected and divided into training set (676 cases)，validation set (112 cases)，and test set (230 cases). The training set and validation set were used to construct the evaluation model of femoral neck fracture through three steps: automatic image pretreatment，fracture diagnosis model construction and model evaluation. The test set was used to validate the model. Results A database of 1 018 cases of femoral neck fracture was established. The accuracy of the established intelligent assessment model in identifying femoral neck fractures reached 78.7%. Conclusion The established intelligent evaluation model will provide technical support for the development of automatic evaluation software of femoral neck fractures.

Key words: forensic imaging, femoral neck fracture, artificial intelligence（AI）, image recognition, deep learning

摘要： 目的旨在构建人体股骨颈骨折影像人工智能（artificial intelligence，AI）快速诊断方法，从而实现股骨颈骨折的自动化评估。方法采集1 018例股骨颈骨折的病例作为研究样本，分为训练集（676例）、验证集（112例）和测试集（230例）。应用训练集和验证集样本通过自动化影像预处理、骨折诊断模型构建和模型评估三个步骤进行股骨颈骨折评估模型构建，应用测试集对模型进行测试。结果建立了1 018例股骨颈骨折样本数据库。建立的智能评估模型识别股骨颈骨折精准率达到78.7%。结论该模型将为股骨颈骨折自动化评估软件的研发提供技术支撑。

关键词: 法医影像学, 股骨颈骨折, 人工智能, 图像识别, 深度学习

CLC Number:

DF795.4
TP18

MA Wenjing, LIU Fan, ZHAO Liang, LIU Hua, PEI Jingzhe, ZHANG Rui, SHI Wei. Study on the Rapid Imaging Diagnosis of Femoral Neck Fracture by Artificial Intelligence[J]. Chinese Journal of Forensic Sciences, 2024(3): 49-54.

马文静, 刘凡, 赵亮, 刘华, 裴京哲, 张睿, 施维. 股骨颈骨折影像人工智能快速诊断方法研究[J]. 中国司法鉴定, 2024(3): 49-54.

References

[ 1 ] HUANG G, LIU Z,VAN DER MAATEN L, et al. Densely connected convolutional networks [C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Washington D.C., USA:IEEE Press, 2017:1-8.
[ 2 ] YANIV Z，LOWEKAMP B C，JOHNSON H J，et al. SimpleITK image-analysis notebooks：A collaborative environment for education and reproducible research[J]. Journal of Digital Imaging，2018，31（3）： 290-303.
[ 3 ] VIRTANEN P，GOMMERS R，OLIPHANT T E，et al. SciPy 1.0： Fundamental algorithms for scientific computing in Python[J]. Nature Methods，2020，17（3）： 261-272.
[ 4 ] HARRIS C R，MILLMAN K J，VAN DER WALT S J，et al. Array programming with NumPy[J]. Nature，2020，585（7825）： 357-362.
[ 5 ] 史忠植 . 人工智能[M]. 北京：机械工业出版社，2016.
[ 6 ] HINTON G E，OSINDEＲO S，TEH Y W. A fast learning algorithm for deep belief nets[J]. Neural Computation，2006，18（7）： 1527-1554．
[ 7 ] 陈先昌. 基于卷积神经网络的深度学习算法与应用研究[D].杭州：浙江工商大学，2014.
[ 8 ] 杨琦帆，孙雪阳，王彦斌，等.基于深度学习的颅脑损伤机制自动化鉴别[J]. 法医学杂志，2022，38（2）：223-230.
[ 9 ] KOICHIRO Y，HIROYUKI A，AKIRA K，et al. Deep lear-ning with convolutional neural network in radiology[J]. Japanese Journal of Radiology，2018，36（4）：257-272.
[10] 孙雪瑞 . 基于深度学习的病理图像细胞核分割[D].成都：电子科技大学，2020.
[11] 白司悦，蔡侃臣，周兰，等. 基于角膜图像的人体死亡时间推断模型初探[J]. 刑事技术，2021， 46（5）： 502-506.
[12] LIPPERT C，SABATINI R，MACHER M C，et al. Identification of individuals by trait prediction using whole-genome sequencing data[J]. Proceedings of the National Academy of Sciences of the United States of America，2017，114（38）：10166-10171.
[13] SPAMPINATO C，PALAZZO S，GIORDANO D，et al. Deep learning for automated skeletal bone age assessment in X-ray images[J]. Medical Image Analysis，2017，36：41-51.
[14] 胡婷鸿，火忠，刘太昂，等. 基于深度学习实现维吾尔族青少年左手腕关节骨龄自动化评估[J].法医学杂志，2018，34（1）：27-32.
[15] 彭丽琴，万雷，汪茂文，等. 运用3种卷积神经网络模型对青少年骨盆骨龄评估的比较[J]. 法医学杂志，2020，36（5）：622-630.
[16] 杨超朋，赵俊彦，何光龙，等. 基于深度学习的人体肋骨骨折智能检测技术[J]. 刑事技术， 2021， 46（2）： 134-139.
[17] 马文静，刘凡，刘华，等.椎体骨折影像AI快速诊断方法研究[J].刑事技术，2023，48（6）：570-576.
[18] 王立鹏. 深度学习在辅助股骨颈骨折诊断及医师培训中的应用研究[D].上海：中国人民解放军海军军医大学，2020.
[19] ADAMS M，CHEN W J，HOLCDORF D，et al. Computer vs human： Deep learning versus perceptual training for the detection of neck of femur fractures[J]. Journal of Medical Imaging and Radiation Oncology，2019，63（1）：27-32.
[20] 叶涛涛. 基于深度卷积神经网络的X光影像分析研究[D].杭州：浙江工业大学，2020.
[21] MUTASA S，VARADA S，GOEL A，et al. Advanced deep learning techniques applied to automated femoral neck fracture detection and classification[J]. Journal of Digital Imaging，2020，33（5）：1209-1217.

[1]	ZHANG Liwei, WANG Tian, YU Daijing, ZHANG Jun, YAN Jiangwei. Status of Forensic Microbiology Research Based on Machine Learning [J]. Chinese Journal of Forensic Sciences, 2024, 0(3): 32-40.
[2]	CHEN Huan , SHI Jiusheng. Effect of Recovery Time and Clinical Characteristics on Forensic Disability Assessment of Femoral Neck Fracture [J]. Chinese Journal of Forensic Sciences, 2024, 0(2): 61-66.
[3]	ZHOU Huiming, LI Danyang, WAN Lei, LI Chengtao, WANG Maowen, WANG Yahui. Research Frontiers and Prospects of Artificial Intelligence Technology in Forensic Bone Age and Dental Age Assessment [J]. Chinese Journal of Forensic Sciences, 2023, 0(5): 31-38.
[4]	JIAO Jianming, FAN Fei, DENG Xiaodong, DAI Xinhua, LIN Yushan, LIU Guangfeng, DING Jie, WANG Junjing, BAI Wanjing, NING Gang, CHEN Hu, DENG Zhenhua. Research Progress of Bone Age Assessment Based on Deep Learning and MRI Technology [J]. Chinese Journal of Forensic Sciences, 2023, 0(1): 38-44.
[5]	LIN Hanguang, JIANG Fenghui, HONG Xiaochun, CHEN Rui, TANG Jianpin. Study on the Same Identification of Lateral Skull X-ray Films [J]. Chinese Journal of Forensic Sciences, 2022, 0(3): 57-62.

Study on the Rapid Imaging Diagnosis of Femoral Neck Fracture by Artificial Intelligence

股骨颈骨折影像人工智能快速诊断方法研究

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 5

Recommended Articles

Metrics