北京大学学报(医学版) ›› 2023, Vol. 55 ›› Issue (5): 771-774. doi: 10.19723/j.issn.1671-167X.2023.05.001
• 专家笔谈 • 下一篇
中图分类号:
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KaulV , EnslinS , GrossSA .History of artificial intelligence in medicine[J].Gastrointest Endosc,2020,92(4):807-812.
doi: 10.1016/j.gie.2020.06.040 |
2 |
JiangF , JiangY , ZhiH ,et al.Artificial intelligence in healthcare: Past, present and future[J].Stroke Vasc Neurol,2017,2(4):230-243.
doi: 10.1136/svn-2017-000101 |
3 |
KheneZE , RichardC , HascoetJ ,et al.Contrast-enhanced CT texture parameters as predictive markers of high-risk urodynamic features in adult patients with Spina bifida[J].Urology,2019,134,84-89.
doi: 10.1016/j.urology.2019.09.023 |
4 |
KarmonikC , BooneT , KhavariR .Data-driven machine-learning quantifies differences in the voiding initiation network in neurogenic voiding dysfunction in women with multiple sclerosis[J].Int Neurourol J,2019,23(3):195-204.
doi: 10.5213/inj.1938058.029 |
5 | NekooeimehrI , Lai-YuenS , BaoP ,et al.Automated contour tracking and trajectory classification of pelvic organs on dynamic MRI[J].J Med Imaging (Bellingham),2018,5(1):014008. |
6 |
FengF , Ashton-MillerJA , DeLanceyJOL ,et al.Convolutional neural network: Based pelvic floor structure segmentation using magnetic resonance imaging in pelvic organ prolapse[J].Med Phys,2020,47(9):4281-4293.
doi: 10.1002/mp.14377 |
7 |
ColarietiA , ThiruchelvamN , BarrettT .Evaluation of image-based prognostic parameters of post-prostatectomy urinary incontinence: A literature review[J].Int J Urol,2021,28(9):890-897.
doi: 10.1111/iju.14609 |
8 |
ShaoIH , KanHC , ChenHY ,et al.Recognition of postoperative cystography features by artificial intelligence to predict recovery from postprosta-tectomy urinary incontinence: A rapid and easy way to predict functional outcome[J].J Pers Med,2023,13(1):126.
doi: 10.3390/jpm13010126 |
9 |
SumitomoM , TeramotoA , TodaR ,et al.Deep learning using preoperative magnetic resonance imaging information to predict early recovery of urinary continence after robot-assisted radical prostatectomy[J].Int J Urol,2020,27(10):922-928.
doi: 10.1111/iju.14325 |
10 | ThimanssonE , BengtssonJ , BaubetaE ,et al.Publisher correction: Deep learning algorithm performs similarly to radiologists in the assessment of prostate volume on MRI[J].Eur Radiol,2023,33(4):3004. |
11 |
RosierPFWM , SchaeferW , LoseG ,et al.International continence society good urodynamic practices and terms 2016: Urodynamics, uroflow-metry, cystometry, and pressure-flow study[J].Neurourol Urodyn,2017,36(5):1243-1260.
doi: 10.1002/nau.23124 |
12 | AbramsP .Describing bladder storage function: Overactive bladder syndrome and detrusor overactivity[J].Urology,2003,62(5 Suppl 2):28-37. |
13 |
WangHS , CahillD , PanagidesJ ,et al.Pattern recognition algorithm to identify detrusor overactivity on urodynamics[J].Neurourol Urodyn,2021,40(1):428-434.
doi: 10.1002/nau.24578 |
14 |
CullingsworthZE , KellyBB , DeebelNA ,et al.Automated quantification of low amplitude rhythmic contractions (LARC) during real-world urodynamics identifies a potential detrusor overactivity subgroup[J].PLoS One,2018,13(8):e0201594.
doi: 10.1371/journal.pone.0201594 |
15 |
NiederhauserT , GafnerES , CantieniT ,et al.Detection and quantification of overactive bladder activity in patients: Can we make it better and automatic?[J].Neurourol Urodyn,2018,37(2):823-831.
doi: 10.1002/nau.23357 |
16 |
SheynD , JuM , ZhangS ,et al.Development and validation of a machine learning algorithm for predicting response to anticholinergic medications for overactive bladder syndrome[J].Obstet Gynecol,2019,134(5):946-957.
doi: 10.1097/AOG.0000000000003517 |
17 |
JinJ , ChungY , KimW ,et al.Classification of bladder emptying patterns by LSTM neural network trained using acoustic signatures[J].Sensors (Basel),2021,21(16):5328.
doi: 10.3390/s21165328 |
18 |
BangS , TukhtaevS , KoKJ ,et al.Feasibility of a deep learning-based diagnostic platform to evaluate lower urinary tract disorders in men using simple uroflowmetry[J].Investig Clin Urol,2022,63(3):301-308.
doi: 10.4111/icu.20210434 |
19 |
YamadaM , MatsukawaY , KameyaY ,et al.Development of an artificial intelligence diagnostic system for lower urinary tract dysfunction in men[J].Int J Urol,2021,28(11):1143-1148.
doi: 10.1111/iju.14661 |
20 |
BalkiI , AmirabadiA , LevmanJ ,et al.Sample-size determination methodologies for machine learning in medical imaging research: A systematic review[J].Can Assoc Radiol J,2019,70(4):344-353.
doi: 10.1016/j.carj.2019.06.002 |
21 |
HungAJ , JianC , GillIS .Automated performance metrics and machine learning algorithms to measure surgeon performance and anticipate clinical outcomes in robotic surgery[J].JAMA Surg,2018,153(8):770-771.
doi: 10.1001/jamasurg.2018.1512 |
22 | Aneeq Z, Andrew H, Irfan E, et al. Surgical activity recognition in robot-assisted radical prostatectomy using deep learning [C]// Frangi A, Schnabel J, Davatzikos C, et al. Medical image computing and computer assisted intervention: MICCAI. Switzerland: Springer, 2018: 273-280. |
23 |
ClemensJQ , EricksonDR , VarelaNP ,et al.Diagnosis and treatment of interstitial cystitis/bladder pain syndrome[J].J Urol,2022,208(1):34-42.
doi: 10.1097/JU.0000000000002756 |
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