[1] |
Skovgaards DM, Diab HMH, Midtgaard HG, et al. Causes of prolonged hospitalization after open incisional hernia repair: an observational single-center retrospective study of a prospective database[J]. Hernia, 2021, 25(4): 1027-1034.
|
[2] |
Pechman DM, Cao L, Fong C, et al. Laparoscopic versus open emergent ventral hernia repair: utilization and outcomes analysis using the ACSNSQIP database[J]. Surg Endosc, 2018, 32(12): 4999-5005.
|
[3] |
Drissi F, Jurczak F, Cossa J P, et al. Outpatient groin hernia repair: assessment of 9330 patients from the French "Club Hernie" database[J]. Hernia, 2018, 22(3): 427-435.
|
[4] |
Rimmer L, Howard C, Picca L, et al. The automaton as a surgeon: the future of artificial intelligence in emergency and general surgery[J]. Eur J Trauma Emerg Surg, 2021, 47(3): 757-762.
|
[5] |
Zhou XY, Guo Y, Shen M, et al. Application of artificial intelligence in surgery[J]. Front Med, 2020, 14(4): 417-430.
|
[6] |
Le Berre C, Sandborn WJ, Aridhi S, et al. Application of Artificial Intelligence to Gastroenterology and Hepatology[J]. Gastroenterology, 2020, 158(1): 76-94 e2.
|
[7] |
Kilic A, Goyal A, Miller J K, et al. Predictive Utility of a Machine Learning Algorithm in Estimating Mortality Risk in Cardiac Surgery[J]. Ann Thorac Surg, 2020, 109(6): 1811-1819.
|
[8] |
Seymour CW, Kennedy JN, Wang S, et al. Derivation, Validation, and Potential Treatment Implications of Novel Clinical Phenotypes for Sepsis[J]. JAMA, 2019, 321(20): 2003-2017.
|
[9] |
Loftus TJ, Tighe PJ, Filiberto AC, et al. Artificial Intelligence and Surgical Decision-making[J]. JAMA Surg, 2020, 155(2): 148-158.
|
[10] |
Wu J, Chen J, Cai J. Application of Artificial Intelligence in Gastrointestinal Endoscopy[J]. J Clin Gastroenterol, 2021, 55(2): 110-120.
|
[11] |
Loftus TJ, Brakenridge SC, Croft CA, et al. Neural network prediction of severe lower intestinal bleeding and the need for surgical intervention[J]. J Surg Res, 2017, 212: 42-47.
|
[12] |
Singh PP, Zeng IS, Srinivasa S, et al. Systematic review and meta-analysis of use of serum C-reactive protein levels to predict anastomotic leak after colorectal surgery[J]. Br J Surg, 2014, 101(4): 339-346.
|
[13] |
Pepys MB, Hirschfield GM, Tennent GA, et al. Targeting C-reactive protein for the treatment of cardiovascular disease[J]. Nature, 2006, 440(7088): 1217-1221.
|
[14] |
Schwartz WB. Medicine and the computer. The promise and problems of change[J]. N Engl J Med, 1970, 283(23): 1257-1264.
|
[15] |
Shung DL, Au B, Taylor RA, et al. Validation of a Machine Learning Model That Outperforms Clinical Risk Scoring Systems for Upper Gastrointestinal Bleeding[J]. Gastroenterology, 2020, 158(1): 160-167.
|
[16] |
Que SJ, Chen QY, Qing Z, et al. Application of preoperative artificial neural network based on blood biomarkers and clinicopathological parameters for predicting long-term survival of patients with gastric cancer[J]. World J Gastroenterol, 2019, 25(43): 6451-6464.
|
[17] |
Gao J, Zagadailov P, Merchant AM. The Use of Artificial Neural Network to Predict Surgical Outcomes After Inguinal Hernia Repair[J]. J Surg Res, 2021, 259: 372-378.
|
[18] |
Garrow CR, Kowalewski KF, Li L, et al. Machine Learning for Surgical Phase Recognition: A Systematic Review[J]. Ann Surg, 2021, 273(4): 684-693.
|
[19] |
Madani A, Namazi B, Altieri MS, et al. Artificial Intelligence for Intraoperative Guidance: Using Semantic Segmentation to Identify Surgical Anatomy During Laparoscopic Cholecystectomy[J]. Ann Surg, 276(2): 363-369.
|
[20] |
Mascagni P, Vardazaryan A, Alapatt D, et al. Artificial Intelligence for Surgical Safety: Automatic Assessment of the Critical View of Safety in Laparoscopic Cholecystectomy Using Deep Learning[J]. Ann Surg, 2022, 275(5): 955-961.
|
[21] |
Harangi B, Hajdu A, Lampe R, et al. Recognizing ureter and uterine artery in endoscopic images using a convolutional neural network[C]. In: 2017 IEEE 30th International Symposium on Computer-Based Medical Systems(CBMS), 2017, 726-727.
|
[22] |
Grasa OG, BernaL E, Casado S. Visual SLAM for handheld monocular endoscope[J]. IEEE Trans Med Imaging, 2014, 33(1): 135-146.
|
[23] |
Kitaguchi D, Takeshita N, Matsuzaki H, et al. Real-time automatic surgical phase recognition in laparoscopic sigmoidectomy using the convolutional neural network-based deep learning approach[J]. Surg Endosc, 2020, 34(11): 4924-4931.
|
[24] |
Guédon ACP, Meij SEP, Osman KNMMH, et al. Deep learning for surgical phase recognition using endoscopic videos[J]. Surg Endosc, 2021, 35(11): 6150-6157.
|
[25] |
Escobar Dominguez JE, Gonzalez A, Donkor C. Robotic inguinal hernia repair[J]. J Surg Oncol, 2015, 112(3): 310-314.
|
[26] |
Prabhu AS, Carbonell A, Hope W, et al. Robotic Inguinal vs Transabdominal Laparoscopic Inguinal Hernia Repair: The RIVAL Randomized Clinical Trial[J]. JAMA Surg, 2020, 155(5): 380-387.
|
[27] |
Olavarria OA, Bernardi K, Shah SK, et al. Robotic versus laparoscopic ventral hernia repair: multicenter, blinded randomized controlled trial[J]. BMJ, 2020, 370: m2457.
|
[28] |
Shademan A, Decker RS, Opfermann JD, et al. Supervised autonomous robotic soft tissue surgery[J]. Sci Transl Med, 2016, 8(337): 337ra64.
|
[29] |
Hong N, Kim M, Lee C, et al. Head-mounted interface for intuitive vision control and continuous surgical operation in a surgical robot system[J]. Med Biol Eng Comput, 2019, 57(3): 601-614.
|
[30] |
Begum S, Khan MR. Outcome assessment of primary ventral versus incisional hernia repair by laparoscopic approach[J]. Int J Abdom Wall Hernia Surg, 2018, 1: 94-98.
|
[31] |
Nudel J, Bishara AM, De Geus SWL, et al. Development and validation of machine learning models to predict gastrointestinal leak and venous thromboembolism after weight loss surgery: an analysis of the MBSAQIP database[J]. Surg Endosc, 2021, 35(1): 182-191.
|
[32] |
O'brien WJ, Ramos RD, Gupta K, et al. Neural Network Model to Detect Long-Term Skin and Soft Tissue Infection after Hernia Repair[J]. Surg Infect(Larchmt), 2021, 22(7): 668-674.
|
[33] |
Elhage SA, Deerenberg EB, Ayuso SA, et al. Development and Validation of Image-Based Deep Learning Models to Predict Surgical Complexity and Complications in Abdominal Wall Reconstruction[J]. JAMA Surg, 2021, 156(10): 933-940.
|
[34] |
Hassan AM, Lu SC, Asaad M, et al. Novel Machine Learning Approach for the Prediction of Hernia Recurrence, Surgical Complication, and 30-Day Readmission after Abdominal Wall Reconstruction[J]. J Am Coll Surg, 2022, 234(5): 918-927.
|
[35] |
Tang Z, Yang Z, Zhu C, et al. Any-to-Any Generation via Composable Diffusion[J/OL]. arXiv: 2305. 11846, 2023.
|