人工智能在老年慢性疼痛诊治中的应用

doi:10.3969/j.issn.1003-9198.2024.01.003

摘要/Abstract

摘要： 慢性疼痛在老年群体中的发病率越来越高,这种顽固的老年综合征亟需临床医生去解决。人工智能在近些年迅猛发展,许多文献已经报道了其在疼痛诊断、治疗、护理方面的作用。本文就人工智能在老年慢性疼痛诊治中的应用进行综述。

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洪霞, 李陆琦, 刘继业. 人工智能在老年慢性疼痛诊治中的应用[J]. 实用老年医学, 2024, 38(1): 7-10.

[1] DOMENICHIELLO A F, RAMSDEN C E. The silent epidemic of chronic pain in older adults[J]. Prog Neuropsychopharmacol Biol Psychiatry, 2019, 93:284-290.
[2] JACKSON T, THOMAS S, STABILE V, et al. A systematic review and meta-analysis of the global burden of chronic pain without clear etiology in low-and middle-income countries: trends in heterogeneous data and a proposal for new assessment methods[J]. Anesth Analg, 2016, 123(3): 739-748.
[3] PITCHER M H, VON KORFF M, BUSHNELL M C, et al. Prevalence and profile of high-impact chronic pain in the United States[J]. J Pain, 2019, 20(2): 146-160.
[4] LEVEILLE S G, JONES R N, KIELY D K, et al. Chronic musculoskeletal pain and the occurrence of falls in an older population[J]. JAMA, 2009, 302(20): 2214-2221.
[5] WHITLOCK E L, DIAZ-RAMIREZ L G, GLYMOUR M M, et al. Association between persistent pain and memory decline and dementia in a longitudinal cohort of elders[J]. JAMA Intern Med, 2017, 177(8): 1146-1153.
[6] MACFARLANE G J, BARNISH M S, JONES G T. Persons with chronic widespread pain experience excess mortality: longitudinal results from UK Biobank and meta-analysis[J]. Ann Rheum Dis, 2017, 76(11): 1815-1822.
[7] RAMESH A N, KAMBHAMPATI C, MONSON J R, et al. Artificial intelligence in medicine[J]. Ann R Coll Surg Engl, 2004, 86(5): 334-338.
[8] MINTZ Y, BRODIE R. Introduction to artificial intelligence in medicine[J]. Minim Invasive Ther Allied Technol, 2019, 28(2): 73-81.
[9] LIU P R, LU L, ZHANG J Y, et al. Application of artificial intelligence in medicine: An Overview[J]. Curr Med Sci, 2021, 41(6): 1105-1115.
[10] SHORTLIFFE E H, DAVIS R, AXLINE S G, et al. Computer-based consultations in clinical therapeutics: explanation and rule acquisition capabilities of the MYCIN system[J]. Comput Biomed Res, 1975, 8(4): 303-320.
[11] MILLER R A, POPLE H E Jr, MYERS J D. Internist-1, an experimental computer-based diagnostic consultant for general internal medicine[J]. N Engl J Med, 1982, 307(8): 468-476.
[12] KAUL V, ENSLIN S, GROSS S A. History of artificial intelligence in medicine[J]. Gastrointest Endosc, 2020, 92(4): 807-812.
[13] KULIKOWSKI C A. Beginnings of artificial intelligence in medicine (AIM): computational artifice assisting scientific inquiry and clinical art-with reflections on present AIM challenges[J]. Yearb Med Inform, 2019, 28(1): 249-256.
[14] NENSA F, DEMIRCIOGLU A, RISCHPLER C. Artificial intelligence in nuclear medicine[J]. J Nucl Med, 2019, 60(Suppl 2): 29s-37s.
[15] D’ANTONI F, RUSSO F, AMBROSIO L, et al. Artificial intelligence and computer vision in low back pain: a systematic review[J]. Int J Environ Res Public Health, 2021, 18(20): 10909.
[16] HOSNY A, PARMAR C, QUACKENBUSH J, et al. Artificial intelligence in radiology[J]. Nat Rev Cancer, 2018, 18(8): 500-510.
[17] HAQ R, ARAS R, BESACHIO D A, et al. 3D lumbar spine intervertebral disc segmentation and compression simulation from MRI using shape-aware models[J]. Int J Comput Assist Radiol Surg, 2015, 10(1): 45-54.
[18] KOREZ R, IBRAGIMOV B, LIKAR B, et al. A framework for automated spine and vertebrae interpolation-based detection and model-based segmentation[J]. IEEE Trans Med Imaging, 2015, 34(8): 1649-1662.
[19] KIM K B, PARK H J, SONG D H. Automatic characterizations of lumbar multifidus muscle and intramuscular fat with Fuzzy C-Means based quantization from ultrasound images[J]. Curr Med Imaging, 2020, 16(5): 592-600.
[20] LI Y, LIANG W, ZHANG Y, et al. Automatic lumbar vertebrae detection based on feature fusion deep learning for partial occluded C-arm X-ray images[J]. Annu Int Conf IEEE Eng Med Biol Soc, 2016, 2016:647-650.
[21] MALINDA V, LEE D. Lumbar vertebrae synthetic segmentation in computed tomography images using hybrid deep generative adversarial networks[J]. Annu Int Conf IEEE Eng Med Biol Soc, 2020, 2020:1327-1330.
[22] JHA N, LEE K S, KIM Y J. Diagnosis of temporomandibular disorders using artificial intelligence technologies: a systematic review and meta-analysis[J]. PLoS One, 2022, 17(8): e0272715.
[23] KREINER M, VILORIA J. A novel artificial neural network for the diagnosis of orofacial pain and temporomandibular disorders[J]. J Oral Rehabil, 2022, 49(9): 884-889.
[24] LATYPOV T H, SO M C, HUNG P S, et al. Brain imaging signatures of neuropathic facial pain derived by artificial intelligence[J]. Sci Rep, 2023, 13(1): 10699.
[25] ITOH N, MISHIMA H, YOSHIDA Y, et al. Evaluation of the effect of patient education and strengthening exercise therapy using a mobile messaging App on work productivity in Japanese patients with chronic low back pain: open-label, randomized, parallel-group trial[J]. JMIR Mhealth Uhealth, 2022, 10(5): e35867.
[26] GKOLIAS V, AMANITI A, TRIANTAFYLLOU A, et al. Reduced pain and analgesic use after acoustic binaural beats therapy in chronic pain-A double-blind randomized control cross-over trial[J]. Eur J Pain, 2020, 24(9): 1716-1729.
[27] ALZOUHAYLI K, SCHILATY N D, NAGAI T, et al. The effectiveness of clinic versus home-based, artificial intelligence-guided therapy in patients with low back pain: non-randomized clinical trial[J]. Clin Biomech:Bristol, Avon, 2023, 109:106069.
[28] JANSEN M P, SALZLECHNER C, BARNES E, et al. Artificial intelligence in osteoarthritis: repair by knee joint distraction shows association of pain, radiographic and immunological outcomes[J]. Rheumatology:Oxford, 2023, 62(8): 2789-2796.
[29] BARREVELD A M, ROSEN KLEMENT M L, CHEUNG S, et al. An artificial intelligence-powered, patient-centric digital tool for self-management of chronic pain: a prospective, multicenter clinical trial[J]. Pain Med, 2023, 24(9): 1100-1110.
[30] PIETTE J D, NEWMAN S, KREIN S L, et al. Patient-centered pain care using artificial intelligence and mobile health tools: A Randomized Comparative Effectiveness Trial[J]. JAMA Intern Med, 2022, 182(9): 975-983.
[31] MARCUZZI A, NORDSTOGA A L, BACH K, et al. Effect of an artificial intelligence-based self-management App on musculoskeletal health in patients with neck and／or low back pain referred to specialist care: A Randomized Clinical Trial[J]. JAMA Netw Open, 2023, 6(6): e2320400.

人工智能在老年慢性疼痛诊治中的应用

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