[1]
|
Hamet, P. and Tremblay, J. (2017) Artificial Intelligence in Medicine. Metabolism, 69, S36-S40.
https://doi.org/10.1016/j.metabol.2017.01.011
|
[2]
|
Greener, J.G., Kandathil, S.M., Moffat, L. and Jones, D.T. (2022) A Guide to Machine Learning for Biologists. Nature Reviews Molecular Cell Biology, 23, 40-55. https://doi.org/10.1038/s41580-021-00407-0
|
[3]
|
Mehrholz, J., Pohl, M., Platz, T., Kugler, J. and Elsner, B. (2018) Electromechanical and Robot-Assisted Arm Training for Improving Activities of Daily Living, Arm Function, and Arm Muscle Strength after Stroke. Cochrane Database of Systematic Reviews, 9, CD006876. https://doi.org/10.1002/14651858.CD006876.pub5
|
[4]
|
Hashimoto, D.A., Rosman, G., Rus, D., et al. (2018) Ar-tificial Intelligence in Surgery: Promises and Perils. Annals of Surgery, 268, 70-76. https://doi.org/10.1097/SLA.0000000000002693
|
[5]
|
Kim, Y.J., Kelley, B.P., Nasser, J.S. and Chung, K.C. (2019) Implementing Precision Medicine and Artificial Intelligence in Plastic Surgery: Concepts and Future Prospects. Plastic and Reconstructive Surgery—Global Open, 7, e2113.
https://doi.org/10.1097/GOX.0000000000002113
|
[6]
|
Garrow, C.R., Kowalewski, K., Li, L., et al. (2021) Ma-chine Learning for Surgical Phase Recognition. Annals of Surgery, 273, 684-693. https://doi.org/10.1097/SLA.0000000000004425
|
[7]
|
Hashimoto, D.A., Rosman, G., Witkowski, E.R., et al. (2019) Computer Vision Analysis of Intraoperative Video. Annals of Surgery, 270, 414-421. https://doi.org/10.1097/SLA.0000000000003460
|
[8]
|
Madani, A., Namazi, B., Altieri, M.S., et al. (2022) Artificial Intelligence for Intraoperative Guidance. Annals of Surgery, 276, 363-369. https://doi.org/10.1097/SLA.0000000000004594
|
[9]
|
Esteva, A., Kuprel, B., Novoa, R.A., et al. (2017) Derma-tologist-Level Classification of Skin Cancer with Deep Neural Networks. Nature, 542, 115-118. https://doi.org/10.1038/nature21056
|
[10]
|
Collins, G.S., Reitsma, J.B., Altman, D.G. and Moons, K.G.M. (2015) Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement. Annals of Internal Medicine, 162, W1-73. https://doi.org/10.7326/L15-0078-4
|
[11]
|
Kim, J.S., Merrill, R.K., Arvind, V., et al. (2018) Examining the Ability of Artificial Neural Networks Machine Learning Models to Accurately Predict Complications Following Posterior Lumbar Spine Fusion. Spine, 43, 853-860.
https://doi.org/10.1097/BRS.0000000000002442
|
[12]
|
Twinanda, A.P., Shehata, S., Mutter, D., et al. (2017) EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos. IEEE Transactions on Medical Imaging, 36, 86-97. https://doi.org/10.1109/TMI.2016.2593957
|
[13]
|
Gulshan, V., Peng, L., Coram, M., et al. (2016) Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA, 316, 2402-2410.
https://doi.org/10.1001/jama.2016.17216
|
[14]
|
Shademan, A., Decker, R.S., Opfermann, J.D., et al. (2016) Super-vised Autonomous Robotic Soft Tissue Surgery. Science Translational Medicine, 8, 337ra64. https://doi.org/10.1126/scitranslmed.aad9398
|
[15]
|
Hung, A.J., Chen, J. and Gill, I.S. (2018) Automated Perfor-mance Metrics and Machine Learning Algorithms to Measure Surgeon Performance and Anticipate Clinical Outcomes in Robotic Surgery. JAMA Surgery, 153, 770-771.
https://doi.org/10.1001/jamasurg.2018.1512
|
[16]
|
Yang, J.H., Goodman, E.D., Dawes, A.J., et al. (2023) Using AI and Computer Vision to Analyze Technical Proficiency in Robotic Surgery. Surgical Endoscopy, 37, 3010-3017. https://doi.org/10.1007/s00464-022-09781-y
|
[17]
|
Ryu, S., Goto, K., Kitagawa, T., et al. (2023) Real-Time Artifi-cial Intelligence Navigation-Assisted Anatomical Recognition in Laparoscopic Colorectal Surgery. Journal of Gastro-intestinal Surgery, 27, 3080-3082.
https://doi.org/10.1007/s11605-023-05819-1
|
[18]
|
Khan, D.Z., Luengo, I., Barbarisi, S., et al. (2022) Automated Operative Workflow Analysis of Endoscopic Pituitary Surgery Using Machine Learning: Development and Preclinical Evaluation (IDEAL Stage 0). Journal of Neurosurgery, 137, 51-58. https://doi.org/10.3171/2021.6.JNS21923
|
[19]
|
Soguero-Ruiz, C., Hindberg, K., Mora-Jiménez, I., et al. (2016) Predicting Colorectal Surgical Complications Using Heterogeneous Clinical Data and Kernel Methods. Journal of Bi-omedical Informatics, 61, 87-96.
https://doi.org/10.1016/j.jbi.2016.03.008
|
[20]
|
Wagner, M., Brandenburg, J.M., Bodenstedt, S., et al. (2022) Surgomics: Personalized Prediction of Morbidity, Mortality and Long-Term Outcome in Surgery Using Machine Learning on Multimodal Data. Surgical Endoscopy, 36, 8568-8591. https://doi.org/10.1007/s00464-022-09611-1
|
[21]
|
Chen, T., Li, X., Mao, Q., et al. (2022) An Artificial Intelligence Method to Assess the Tumor Microenvironment with Treatment Outcomes for Gastric Cancer Patients after Gastrectomy. Journal of Translational Medicine, 20, Article No. 100. https://doi.org/10.1186/s12967-022-03298-7
|
[22]
|
严律南. 人工智能在医学领域应用的现状与展望[J]. 中国普外基础与临床杂志, 2018, 25(5): 513-514.
|
[23]
|
Hinton, G.E. and Salakhutdinov, R.R. (2006) Reducing the Dimensionality of Data with Neural Networks. Science, 313, 504-507. https://doi.org/10.1126/science.1127647
|
[24]
|
Lecun, Y., Bengio, Y. and Hinton, G. (2015) Deep Learning. Nature, 521, 436-444.
https://doi.org/10.1038/nature14539
|
[25]
|
Jaradat, A.S., Al Mamlook, R.E., Almakayeel, N., et al. (2023) Auto-mated Monkeypox Skin Lesion Detection Using Deep Learning and Transfer Learning Techniques. International Journal of Environmental Research and Public Health, 20, Article 4422. https://doi.org/10.3390/ijerph20054422
|
[26]
|
Karhade, A.V., Bongers, M.E.R., Groot, O.Q., et al. (2021) Devel-opment of Machine Learning and Natural Language Processing Algorithms for Preoperative Prediction and Automated Identification of Intraoperative Vascular Injury in Anterior Lumbar Spine Surgery. The Spine Journal, 21, 1635-1642. https://doi.org/10.1016/j.spinee.2020.04.001
|
[27]
|
Zhuang, F.Z., et al. (2020) A Comprehensive Survey on Transfer Learning. Proceedings of the IEEE, 109, 43-76.
https://doi.org/10.1109/JPROC.2020.3004555
|
[28]
|
Khan, A., Brouwer, N., Blank, A., et al. (2023) Comput-er-Assisted Diagnosis of Lymph Node Metastases in Colorectal Cancers Using Transfer Learning with an Ensemble Model. Modern Pathology, 36, Article ID: 100118.
https://doi.org/10.1016/j.modpat.2023.100118
|
[29]
|
Ho, T.Y., Chao, C.H., Chin, S.C., et al. (2020) Classifying Neck Lymph Nodes of Head and Neck Squamous Cell Carcinoma in MRI Images with Radiomic Features. Journal of Digital Imaging, 33, 613-618.
https://doi.org/10.1007/s10278-019-00309-w
|
[30]
|
Yin, P., Mao, N., Zhao, C., et al. (2019) A Triple-Classification Radiomics Model for the Differentiation of Primary Chordoma, Giant Cell Tumor, and Metastatic Tumor of Sacrum Based on T2-Weighted and Contrast-Enhanced T1-Weighted MRI. Journal of Magnetic Resonance Imaging, 49, 752-759. https://doi.org/10.1002/jmri.26238
|
[31]
|
Ehteshami, B.B., Veta, M., Johannes, V.D.P., et al. (2017) Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women with Breast Cancer. JAMA, 318, 2199-2210.
https://doi.org/10.1001/jama.2017.14585
|
[32]
|
刘晓鹏, 周海英, 胡志雄, 等. 人工智能识别技术在T1期肺癌诊断中的临床应用研究[J]. 中国肺癌杂志, 2019, 22(5): 319-323. https://doi.org/10.3779/J.Issn.1009-3419.2019.05.09
|
[33]
|
Mazaki, J., Katsumata, K., Ohno, Y., et al. (2021) A Novel Predictive Model for Anastomotic Leakage in Colorectal Cancer Using Auto-Artificial Intelligence. Anticancer Research, 41, 5821-5825.
https://doi.org/10.21873/anticanres.15400
|
[34]
|
Han, T., Zhu, J., Chen, X., et al. (2022) Application of Artificial Intelligence in a Real-World Research for Predicting the Risk of Liver Metastasis in T1 Colorectal Cancer. Cancer Cell International, 22, Article No. 28.
https://doi.org/10.1186/s12935-021-02424-7
|
[35]
|
Jiang, Y., Zhang, Z., Yuan, Q., et al. (2022) Predicting Perito-neal Recurrence and Disease-Free Survival from CT Images in Gastric Cancer with Multitask Deep Learning: A Retro-spective Study. The Lancet Digital Health, 4, E340-E350.
https://doi.org/10.1016/S2589-7500(22)00040-1
|
[36]
|
Zhao, M., Tang, Y., Kim, H., et al. (2018) Machine Learning with K-Means Dimensional Reduction for Predicting Survival Outcomes in Patients with Breast Cancer. Cancer Informatics, 17.
https://doi.org/10.1177/1176935118810215
|
[37]
|
Veldhuizen, G.P., Röcken, C., Behrens, H., et al. (2023) Deep Learning-Based Subtyping of Gastric Cancer Histology Predicts Clinical Outcome: A Multi-Institutional Retrospective Study. Gastric Cancer, 26, 708-720.
https://doi.org/10.1007/s10120-023-01398-x
|
[38]
|
Ribeiro, M.T., Singh, S. and Guestrin, C. (2016) “Why Should I Trust You?”: Explaining the Predictions of Any Classifie. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, San Diego, June 2016, 97-101. https://doi.org/10.18653/v1/N16-3020
|
[39]
|
Wang, F., Casalino, L.P. and Khullar, D. (2019) Deep Learning in Medicine-Promise, Progress, and Challenges. JAMA Internal Medicine, 179, 293-294. https://doi.org/10.1001/jamainternmed.2018.7117
|
[40]
|
Cabitza, F., Rasoini, R. and Gensini, G.F. (2017) Unin-tended Consequences of Machine Learning in Medicine? JAMA, 318, 517-518. https://doi.org/10.1001/jama.2017.7797
|