Kolorektal Cerrahide Yapay Zekanın Yeri
Özet
Kolorektal cerrahi süreçlerinde yapay zeka kullanımı; tanı koyma, operasyon planlama ve ameliyat sonrası risk değerlendirmesi aşamalarında hata payını azaltarak tedavi başarısını optimize etmeyi amaçlamaktadır. Makine öğrenimi ve derin öğrenme algoritmaları, kolonoskopi sırasında polip tespit oranlarını artırmakta ve T1 evresindeki kanserlerde gereksiz ek cerrahi müdahalelerin önüne geçilmesine yardımcı olmaktadır. Ayrıca, ameliyat sırasında gerçek zamanlı mikroperfüzyon analizi ve ameliyat sonrasında kişiye özel gelişebilecek komplikasyonların öngörülmesi sayesinde mortalite ve morbidite oranlarında önemli düşüşler hedeflenmektedir.
The integration of artificial intelligence in colorectal surgery processes aims to optimize treatment success by reducing error margins during diagnosis, operation planning, and postoperative risk assessment. Machine learning and deep learning algorithms increase polyp detection rates during colonoscopy and help prevent unnecessary additional surgeries in T1 stage cancers. Furthermore, significant reductions in mortality and morbidity rates are targeted through real-time microperfusion analysis during surgery and the prediction of patient-specific complications postoperatively.
Referanslar
Francis NK, Luther A, Salib E, et al. The use of artificial neural networks to predict delayed discharge and readmission in enhanced recovery following laparoscopic colorectal cancer surgery. Tech Coloproctol 2015; 19:419-428
Park SH, Park HM, Baek KR, et al. Artificial intelligence based real-time microcirculation analysis system for laparoscopic colorectal surgery. World J Gastroenterol. 2020; 28;26(44):6945-6962. doi: 10.3748/wjg.v26.i44.6945.
Ng ZQ, Jung JK, Theophilus M. Artificial Intelligence in Pre-operative Assessment of Patients in Colorectal Surgery. Turk J Colorectal Dis 2021; 31:99-104.
Skrede OJ, De Raedt S, Kleppe A, et al. Deep learning for prediction of colorectal cancer outcome: a discovery and validation study. Lancet. 2020;1;395(10221):350-360. doi: 10.1016/S0140-6736(19)32998-8.
Winawer SJ, Zauber AG, Ho MN, et al. Prevention of colorectal cancer by colonoscopic polypectomy. N Engl J Med 1993; 329: 1977–1981
Van Rijn JC, Reitsma JB, Stoker J, et al. Polyp miss rate determined by tandem colonoscopy: a systematic review. Am J Gastroenterol 2006; 101: 343–350
Ahmad OF, Soares AS, Mazomenos E, et al. Artificial intelligence and computer-aided diagnosis in colonoscopy: current evidence and future directions. Lancet Gastroenterol Hepatol. 2019;4(1):71-80. doi: 10.1016/S2468-1253(18)30282-6
Hassan C, Spadaccini M, Iannone A, et al. Performance of artificial intelligence in colonoscopy for adenoma and polyp detection: a systematic review and meta-analysis. Gastrointest Endosc. 2021;93(1):77-85.e6. doi: 10.1016/j.gie.2020.06.059.
Kuiper T, Alderlieste YA, Tytgat KMAJ, et al. Automatic optical diagnosis of small colorectal lesions by laser-induced autofluorescence. Endoscopy 2015; 47: 56–62
Ichimasa K, Kudo SE, Mori Y, et al. Artificial intelligence may help in predicting the need for additional surgery after endoscopic resection of T1 colorectal cancer. Endoscopy. 2018;50(3):230-240. doi: 10.1055/s-0043-122385.
Kudo SE, Ichimasa K, Villard B, et al. Artificial Intelligence System to Determine Risk of T1 Colorectal Cancer Metastasis to Lymph Node. Gastroenterology. 2021;160(4):1075-1084.e2. doi: 10.1053/j.gastro.2020.09.027.
Sha S, Du W, Parkinson A, et al. Relative importance of clinical and sociodemographic factors in association with post-operative in-hospital deaths in colorectal cancer patients in New South Wales: An artificial neural network approach. J Eval Clin Pract. 2020;26(5):1389-1398. doi: 10.1111/jep.13318.
PelvEx Collaborative. Predicting outcomes of pelvic exenteration using machine learning. Colorectal Dis. 2020;22(12):1933-1940. doi: 10.1111/codi.15235.
Lorenzovici N, Dulf EH, Mocan T, et al. Artificial Intelligence in Colorectal Cancer Diagnosis Using Clinical Data: Non-Invasive Approach. Diagnostics (Basel). 2021;14;11(3):514. doi: 10.3390/diagnostics11030514
Daskalaki D, Aguilera F, Patton K, et al. Fluorescence in robotic surgery. J Surg Oncol 2015; 112: 250-256 [PMID: 25974861 DOI: 10.1002/jso.23910]
James DR, Ris F, Yeung TM, et al. Fluorescence angiography in laparoscopic low rectal and anorectal anastomoses with pinpoint perfusion imaging--a critical appraisal with specific focus on leak risk reduction. Colorectal Dis 2015; 17 Suppl 3: 16-21
Degett TH, Andersen HS, Gögenur I. Indocyanine green fluorescence angiography for intraoperative assessment of gastrointestinal anastomotic perfusion: a systematic review of clinical trials. Langenbecks Arch Surg 2016; 401: 767-775.
Park SH, Park HM, Baek KR, et al. Artificial intelligence based real-time microcirculation analysis system for laparoscopic colorectal surgery. World J Gastroenterol. 2020;28;26(44):6945-6962. doi: 10.3748/wjg.v26.i44.6945
Merath K, Hyer JM, Mehta R, et al. Use of Machine Learning for Prediction of Patient Risk of Postoperative Complications After Liver, Pancreatic, and Colorectal Surgery. J Gastrointest Surg. 2020 ;24(8):1843-1851. doi: 10.1007/s11605-019-04338-2.
Copeland GP, Jones D, Walters M. POSSUM: a scoring system for surgical audit. Br J Surg. 1991;78(3):355–60.
Gawande AA, Kwaan MR, Regenbogen SE, et al. An Apgar score for surgery. J Am Coll Surg. 2007;204(2): 201–208.
Bilimoria KY, Liu Y, Paruch JL, et al. Development and evaluation of the universal ACS NSQIP surgical risk calculator: a decision aid and informed consent tool for patients and surgeons. J Am Coll Surg. 2013;217(5):833–842.e1-3.
