[1]段永飞,高继学,倪建鑫,等.基于人工智能学习模型在结节性硬化症相关肾血管平滑肌脂肪瘤诊疗中的研究[J].医学信息,2023,36(11):171-174.[doi:10.3969/j.issn.1006-1959.2023.11.037]
 DUAN Yong-fei,GAO Ji-xue,NI Jian-xin,et al.Research on the Diagnosis and Treatment of Tuberous Sclerosis Complex-associated Renal Angiomyolipoma Based on Artificial Intelligence Learning Model[J].Journal of Medical Information,2023,36(11):171-174.[doi:10.3969/j.issn.1006-1959.2023.11.037]
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基于人工智能学习模型在结节性硬化症相关肾血管平滑肌脂肪瘤诊疗中的研究()
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医学信息[ISSN:1006-1959/CN:61-1278/R]

卷:
36卷
期数:
2023年11期
页码:
171-174
栏目:
综述
出版日期:
2023-06-01

文章信息/Info

Title:
Research on the Diagnosis and Treatment of Tuberous Sclerosis Complex-associated Renal Angiomyolipoma Based on Artificial Intelligence Learning Model
文章编号:
1006-1959(2023)11-0171-04
作者:
段永飞高继学倪建鑫
(1.延安大学医学院,陕西 延安 716000;2.延安大学附属医院泌尿外科,陕西 延安 716000;3.西安市人民医院泌尿肾脏病院,陕西 西安 710004;4.空军军医大学第二附属医院泌尿外科,陕西 西安 710038;5.西安医学院临床医学院,陕西 西安 710021)
Author(s):
DUAN Yong-feiGAO Ji-xueNI Jian-xinet al.
(1.Medical College of Yan’an University,Yan’an 716000,Shaanxi,China;2.Department of Urology,the Affiliated Hospital of Yan’an University,Yan’an 716000,Shaanxi,China;3.Urology and Kidney Hospital of Xi’an People’s Hospital,Xi’an 710004,Shaanxi,China;4.D
关键词:
人工智能机器学习深度学习结节性硬化症相关肾血管平滑肌脂肪瘤
Keywords:
Artificial intelligenceMachine learningDeep learningTuberous sclerosis complex-associated renal angiomyolipoma
分类号:
R737.11
DOI:
10.3969/j.issn.1006-1959.2023.11.037
文献标志码:
A
摘要:
结节性硬化症相关肾血管平滑肌脂肪瘤(TSC-RAML)是一种罕见的良性实体“三相”肿瘤,破裂出血风险威胁着患者的生命安全。三维立体图像清晰、直观的特点可以很好的用来观察病变、分析肿瘤解剖和预测患者预后。图像分割技术是三维成像的关键步骤,传统分割技术的精度及效率均难以达到实际需求,随着人工智能(AI)的出现,智能化医学影像分割技术在临床中取得快速发展,可有效提高图像分割的精度。本文就智能化学习模型的现状及在TSC-RAML诊疗中的应用进行阐述。
Abstract:
Tuberous sclerosis complex-associated renal angiomyolipoma (TSC-RAML) is a rare benign solid "three-phase" tumor. The risk of rupture and bleeding threatens the life safety of patients. The clear and intuitive characteristics of three-dimensional images can be well used to observe lesions, analyze tumor anatomy and predict the prognosis of patients. Image segmentation technology is a key step of three-dimensional imaging, and the accuracy and efficiency of traditional segmentation technology are difficult to meet the actual needs. With the emergence of artificial intelligence (AI), intelligent medical image segmentation technology has achieved rapid development in clinic in recent years, which can effectively improve the accuracy of image segmentation. This paper expounds the current situation of intelligent learning model and its application in TSC-RAML diagnosis and treatment.

参考文献/References:

[1]Uysal SP,Sahin M.Tuberous sclerosis: a review of the past,present,and future[J].Turk J Med Sci,2020,50(SI-2):1665-1676.[2]Nair N,Chakraborty R,Mahajan Z,et al.Renal Manifestations of Tuberous Sclerosis Complex[J].J Kidney Cancer VHL,2020,7(3):5-19.[3]Kapoor A,Girard L,Lattouf J B,et al.Evolving Strategies in the Treatment of Tuberous Sclerosis Complex-associated Angiomyolipomas (TSC-AML)[J].Urology,2016,89:19-26.[4]中国抗癌协会泌尿男生殖系肿瘤专业委员会结节性硬化协作组.结节性硬化症相关肾血管平滑肌脂肪瘤诊疗与管理专家共识[J].中国癌症杂志,2020,30(1):70-78.[5]Hohne KH,Pflesser B,Pommert A,et al.A realistic model of human structure from the visible human data[J].Methods Inf Med,2001,40(2):83-89.[6]孙圣坤,王威,徐阿祥,等.数字化肾脏在腹腔镜肾部分切除术前规划中的应用[J].中国数字医学,2012,7(10):51-53.[7]杨斌.数字化智能化技术在颅颌面整形外科的应用[J].中华整形外科杂志,2021,37(1):1-6.[8]Mayo RC,Leung J.Artificial intelligence and deep learning-Radiology’s next frontier?[J].Clin Imaging,2018,49:87-88.[9]Le EPV,Wang Y,Huang Y,et al.Artificial intelligence in breast imaging[J].Clin Radiol,2019,74(5):357-366.[10]Chartrand G,Cheng PM,Vorontsov E,et al.Deep Learning: A Primer for Radiologists[J].Radiographics,2017,37(7):2113-2131.[11]Bengio Y,Courville A,Goodfellow I.Deep learning[M].Cambridge,Mass:MIT Press,2016:1-26.[12]周祯,闫超,张辰宇.人工智能生物学——生物学3.0[J].中国科学:生命科学,2022,52(3):291-300.[13]潘晓英,王佳,刘妮,等.机器学习在医疗大数据中的应用[J].西安邮电大学学报,2020,25(1):21-33.[14]Kawakami E,Tabata J,Yanaihara N,et al.Application of Artificial Intelligence for Preoperative Diagnostic and Prognostic Prediction in Epithelial Ovarian Cancer Based on Blood Biomarkers[J].Clin Cancer Res,2019,25(10):3006-3015.[15]Shen D,Wu G,Suk HI.Deep Learning in Medical Image Analysis[J].Annu Rev Biomed Eng,2017,19:221-248.[16]Erickson BJ,Korfiatis P,Akkus Z,et al.Machine Learning for Medical Imaging[J].Radiographics,2017,37(2):505-515.[17]Niel O,Bastard P.Artificial Intelligence in Nephrology: Core Concepts,Clinical Applications,and Perspectives[J].Am J Kidney Dis,2019,74(6):803-810.[18]Kim T,Lee KH,Ham S,et al.Active learning for accuracy enhancement of semantic segmentation with CNN-corrected label curations: Evaluation on kidney segmentation in abdominal CT[J].Sci Rep,2020,10(1):366.[19]刘煦阳,段潮舒,蔡文生,等.可解释深度学习在光谱和医学影像分析中的应用[J].化学进展,2022,34(12):2561-2572.[20]Lopes VC,Parada VC,Dejulio T J,et al.Differentiation of Solid Renal Tumors with Multiparametric MR Imaging[J].Radiographics,2017,37(7):2026-2042.[21]Mühlbauer J,Egen L,Kowalewski KF,et al.Radiomics in Renal Cell Carcinoma-A Systematic Review and Meta-Analysis[J].Cancers (Basel),2021,13(6):1348.[22]Feng Z,Rong P,Cao P,et al.Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma[J].Eur Radiol,2018,28(4):1625-1633.[23]Cui EM,Lin F,Li Q,et al.Differentiation of renal angiomyolipoma without visible fat from renal cell carcinoma by machine learning based on whole-tumor computed tomography texture features[J].Acta Radiol,2019,60(11):1543-1552.[24]Lecun Y,Bengio Y,Hinton G.Deep learning[J].Nature,2015,521(7553):436-444.[25]陈挺,张赫,夏铁男,等.数字化技术在软组织肉瘤新辅助治疗前后肿瘤退缩评估中的预测价值[J].中国肿瘤外科杂志,2021,13(6):598-600.[26]Chen MY,Woodruff MA,Kua B,et al.Rapid Segmentation of Renal Tumours to Calculate Volume Using 3D Interpolation[J].J Digit Imaging,2021,34(2):351-356.[27]Lin Z,Cui Y,Liu J,et al.Automated segmentation of kidney and renal mass and automated detection of renal mass in CT urography using 3D U-Net-based deep convolutional neural network[J].Eur Radiol,2021,31(7):5021-5031.[28]Houshyar R,Glavis-Bloom J,Bui TL,et al.Outcomes of Artificial Intelligence Volumetric Assessment of Kidneys and Renal Tumors for Preoperative Assessment of Nephron-Sparing Interventions[J].J Endourol,2021,35(9):1411-1418.[29]龚进慧.水平集图像分割及其在医学图像处理中的应用[D].兰州:西北师范大学,2020.[30]Da CL,Araujo J,Ferreira JL,et al.Kidney segmentation from computed tomography images using deep neural network[J].Comput Biol Med,2020,123:103906.[31]van Sloun R,Wildeboer RR,Mannaerts CK,et al.Deep Learning for Real-time,Automatic,and Scanner-adapted Prostate (Zone) Segmentation of Transrectal Ultrasound,for Example,Magnetic Resonance Imaging-transrectal Ultrasound Fusion Prostate Biopsy[J].Eur Urol Focus,2021,7(1):78-85.[32]Parkhomenko E,O’Leary M,Safiullah S,et al.Pilot Assessment of Immersive Virtual Reality Renal Models as an Educational and Preoperative Planning Tool for Percutaneous Nephrolithotomy[J].J Endourol,2019,33(4):283-288.[33]von Rundstedt FC,Scovell JM,Agrawal S,et al.Utility of patient-specific silicone renal models for planning and rehearsal of complex tumour resections prior to robot-assisted laparoscopic partial nephrectomy[J].BJU Int,2017,119(4):598-604.[34]Hsiao CH,Sun TL,Lin PC,et al.A deep learning-based precision volume calculation approach for kidney and tumor segmentation on computed tomography images[J].Comput Methods Programs Biomed,2022,221:106861.

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更新日期/Last Update: 1900-01-01