骨质疏松人工智能技术研究进展及发展趋势
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摘要
目的探讨人工智能(artificial intelligence,AI)技术在骨质疏松早期诊断、早期预防、标准化治疗及科学化随访中的应用现状及发展前景。方法查阅近十年人工智能在医学中的应用现状,分析探讨骨质疏松AI技术开发的可行性及其关键技术限制瓶颈。结果开发优质AI技术的重要前提是大量准确知识的学习。骨质疏松筛查AI技术需要大量的骨密度数据和流行病学因素调查作为筛查系统的数据基础,诊治和随访AI技术需要大量的专业术语、影像学数据、血液、尿液生化指标的采集学习。因此,学习和验证过程中重要的是需要大量的骨密度数据、流行病学调查因素、血液尿液生化指标数据、骨密度测定影像资料、骨质疏松诊断中的专业术语资料、骨质疏松治疗过程中的用药及治疗效果资料等。这些相关资料的收集过程可以通过骨质疏松生物样本库的构建完成。结论骨质疏松AI的开发离不开骨质疏松体检生物样本库的建设,高质量多中心大规模骨质疏松生物样本库构建过程中收集的大量可供机器人学习及再学习的资料是决定骨质疏松AI技术开发成败的关键。
Objective To investigate the application status and development prospects of artificial intelligence(AI) in early diagnosis, early prevention, standardized treatment, and scientific follow-up of osteoporosis. Methods The application status of AI in medicine in the past 10 years was reviewed. The feasibility of osteoporosis AI technology development and its key technical bottlenecks were analyzed. Results An important prerequisite for the development of high-quality AI technology is the learning of a large amount of accurate knowledge. Osteoporosis screening AI technology requires many bone mineral density data and epidemiological factor survey as the data basis of the screening system. Diagnosis and follow-up AI technology requires many professional terms, imaging data, blood and urine biochemical indicator collection. Therefore, it is important in the learning and verification process to require a large amount of bone mineral density data, epidemiological investigation factors, blood and urine biochemical indicator data, bone mineral density imaging data, terminology data in osteoporosis diagnosis and treatment. The collection process of these related data can be completed through the construction of a database of osteoporosis biological samples. Conclusion The development of osteoporosis AI is inseparable from the construction of a biological sample bank for osteoporosis physical examination. The data collected during the construction of a high-quality, multi-center, and large-scale osteoporosis biological sample library for robot learning and re-learning are the key to the success of osteoporosis AI technology development.
Objective To investigate the application status and development prospects of artificial intelligence(AI) in early diagnosis, early prevention, standardized treatment, and scientific follow-up of osteoporosis. Methods The application status of AI in medicine in the past 10 years was reviewed. The feasibility of osteoporosis AI technology development and its key technical bottlenecks were analyzed. Results An important prerequisite for the development of high-quality AI technology is the learning of a large amount of accurate knowledge. Osteoporosis screening AI technology requires many bone mineral density data and epidemiological factor survey as the data basis of the screening system. Diagnosis and follow-up AI technology requires many professional terms, imaging data, blood and urine biochemical indicator collection. Therefore, it is important in the learning and verification process to require a large amount of bone mineral density data, epidemiological investigation factors, blood and urine biochemical indicator data, bone mineral density imaging data, terminology data in osteoporosis diagnosis and treatment. The collection process of these related data can be completed through the construction of a database of osteoporosis biological samples. Conclusion The development of osteoporosis AI is inseparable from the construction of a biological sample bank for osteoporosis physical examination. The data collected during the construction of a high-quality, multi-center, and large-scale osteoporosis biological sample library for robot learning and re-learning are the key to the success of osteoporosis AI technology development.
引文
[1] 贾清萍, 肖伟, 聂志鹏. 契约式社区医疗服务中社区医生激励优化策略研究[J]. 中国卫生统计, 2017,34(6):982-984.
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[4] 张泽洪.分级诊疗体系中基层医疗服务能力建构路径[J]. 中华医院管理杂志,2017, 33(2): 102-105.
[5] 徐若然,周博雅,朱伯健,等. 一体化智慧医疗体系的构建与发展策略研究[J]. 中国医院管理, 2018,38(1):72-74.
[6] 项高悦,曾智,沈永健.我国智慧医疗建设的现状及发展趋势探究[J].中国全科医学,2016,19(24): 2998-3000.
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[8] Sibreeht B, Chen W, Feijs L, et al. Smart jacket design for neonatal monitoring with wearable sensors. Sixth International Workshop on Wearable and Implantable Body Sensor Networks[M]. California: IEEEE Computer Society, 2009: 162-167.
[9] Lee Y, Lee B, Lee M. Wearable sensor glove based on conducting fabric using electrodernml activity and pulse wave sensors for e-health application[J]. Telemed J E Health, 2010,16(2):209-217.
[10] 国务院.新一代人工智能发展规划[EB/OL].(2017-09-28)[2018-11-16].http://baijiahao.baidu.com/s?id=1579761854937818502&wfr=spider&for=pc.
[11] 李蕊. 智慧医疗在糖尿病防治中的意义[J]. 现代医学与健康研究电子杂志,2017,1(2):172-173.
[12] 金洵,丁曙晴,丁义江,等. 基于盆底功能障碍性疾病的智慧医疗多功能一体化信息管理系统的设计[J]. 中华胃肠外科杂志,2015,18(12):1281-1283.
[13] 千开峰,张大敏,谭敏. 基于移动互联网的高血压智慧健康服务系统设计[J].合肥学院学报(自然科学版),2014,24(1):54-58.
[14] Hofbauer LC, Rachner TD. More DATA to guide sequential osteoporosis therapy[J]. Lancet, 2015, 386:1116-1118.
[15] Pisani P, Renna MD, Conversano F, et al. Major osteoporotic fragility fractures: Risk factor updates and societal impact[J]. World J Orthopaed, 2016, 7(3):171-181.
[16] Si L, Winzenberg TM, Jiang Q, et al. Projection of osteoporosis-related fractures and costs in China: 2010-2050[J]. Osteoporos Int, 2015, 26(7):1929-1937.
[17] Yinghua Li, Yougen Wu, Tong Lu, et al. Polymorphisms in the osteoprotegrin gene with risk of osteoporosis and urinary calcium level in a chinese population[J]. J Osteoporos Physical Activity, 2016,176(4): 1000176
[18] 沈丽宁,庐兵兵,徐彪. 美国卫生信息标准研制与落地协调推进战略及启示[J].医学信息杂志,2015,36(1):2-8.
[19] Carola Fink-Anthe, 凌栋,方红娟. 美国及欧洲的医疗卫生服务体系现状分析[J].中华医院管理杂志,2009,25(9):581-584.
[20] 陈玉琪. 卫生信息学在澳大利亚全科医学中的应用和启示[J].预防医学情报杂志,2010,26(3):226-228.
[21] 高莹,杨建,王舒. 生物样本库的发展现状[J].转化医学杂志,2015,4(6):329-331.
[22] 陈波,朱余兵,戚建伟,等. 应用DIH智能麻醉药品管理柜提高麻醉药品管理质量[J].中国卫生质量管理,2015,22(5):38-39.
[23] 谢红生. 计算机、大数据与医、护专业发展趋势[J]. 贵阳学院学报(自然科学版),2015,10(3):72-76.
[2] 国务院医改办,国家卫生计生委,国家发展改革委,等. 关于印发推进家庭医生签约服务的指导意见的通知[EB/OL]. (2016-06-06)[2018-11-16]. http://www.nhfpc.gov.cn/tigs/s3577/201606/e3e7d2670a8b4163b1fe8e409c7887af.shtml.
[3] 国务院办公厅.关于推进分级诊疗制度建设的指导意见[EB/OL]. (2015-09-11)[2018-11-16]. http://www.gov.cn/zhengce/content/2015-09/11/content_10158.htm.
[4] 张泽洪.分级诊疗体系中基层医疗服务能力建构路径[J]. 中华医院管理杂志,2017, 33(2): 102-105.
[5] 徐若然,周博雅,朱伯健,等. 一体化智慧医疗体系的构建与发展策略研究[J]. 中国医院管理, 2018,38(1):72-74.
[6] 项高悦,曾智,沈永健.我国智慧医疗建设的现状及发展趋势探究[J].中国全科医学,2016,19(24): 2998-3000.
[7] 李海阳. IBM 智慧医疗伴随新医改启程[J].中国数字医学,2009,4(5):57-59.
[8] Sibreeht B, Chen W, Feijs L, et al. Smart jacket design for neonatal monitoring with wearable sensors. Sixth International Workshop on Wearable and Implantable Body Sensor Networks[M]. California: IEEEE Computer Society, 2009: 162-167.
[9] Lee Y, Lee B, Lee M. Wearable sensor glove based on conducting fabric using electrodernml activity and pulse wave sensors for e-health application[J]. Telemed J E Health, 2010,16(2):209-217.
[10] 国务院.新一代人工智能发展规划[EB/OL].(2017-09-28)[2018-11-16].http://baijiahao.baidu.com/s?id=1579761854937818502&wfr=spider&for=pc.
[11] 李蕊. 智慧医疗在糖尿病防治中的意义[J]. 现代医学与健康研究电子杂志,2017,1(2):172-173.
[12] 金洵,丁曙晴,丁义江,等. 基于盆底功能障碍性疾病的智慧医疗多功能一体化信息管理系统的设计[J]. 中华胃肠外科杂志,2015,18(12):1281-1283.
[13] 千开峰,张大敏,谭敏. 基于移动互联网的高血压智慧健康服务系统设计[J].合肥学院学报(自然科学版),2014,24(1):54-58.
[14] Hofbauer LC, Rachner TD. More DATA to guide sequential osteoporosis therapy[J]. Lancet, 2015, 386:1116-1118.
[15] Pisani P, Renna MD, Conversano F, et al. Major osteoporotic fragility fractures: Risk factor updates and societal impact[J]. World J Orthopaed, 2016, 7(3):171-181.
[16] Si L, Winzenberg TM, Jiang Q, et al. Projection of osteoporosis-related fractures and costs in China: 2010-2050[J]. Osteoporos Int, 2015, 26(7):1929-1937.
[17] Yinghua Li, Yougen Wu, Tong Lu, et al. Polymorphisms in the osteoprotegrin gene with risk of osteoporosis and urinary calcium level in a chinese population[J]. J Osteoporos Physical Activity, 2016,176(4): 1000176
[18] 沈丽宁,庐兵兵,徐彪. 美国卫生信息标准研制与落地协调推进战略及启示[J].医学信息杂志,2015,36(1):2-8.
[19] Carola Fink-Anthe, 凌栋,方红娟. 美国及欧洲的医疗卫生服务体系现状分析[J].中华医院管理杂志,2009,25(9):581-584.
[20] 陈玉琪. 卫生信息学在澳大利亚全科医学中的应用和启示[J].预防医学情报杂志,2010,26(3):226-228.
[21] 高莹,杨建,王舒. 生物样本库的发展现状[J].转化医学杂志,2015,4(6):329-331.
[22] 陈波,朱余兵,戚建伟,等. 应用DIH智能麻醉药品管理柜提高麻醉药品管理质量[J].中国卫生质量管理,2015,22(5):38-39.
[23] 谢红生. 计算机、大数据与医、护专业发展趋势[J]. 贵阳学院学报(自然科学版),2015,10(3):72-76.