Abstract
Artificial intelligence (AI) and machine learning (ML) have the potential to improve multiple facets of medical practice, including diagnosis of disease, surgical training, clinical outcomes, and access to healthcare. There have been various applications of this technology to surgical fields. AI and ML have been used to evaluate a surgeon’s technical skill. These technologies can detect instrument motion, recognize patterns in video recordings, and track the physical motion, eye movements, and cognitive function of the surgeon. These modalities also aid in the advancement of robotic surgical training. The da Vinci Standard Surgical System developed a recording and playback system to help trainees receive tactical feedback to acquire more precision when operating. ML has shown promise in recognizing and classifying complex patterns on diagnostic images and within pathologic tissue analysis. This allows for more accurate and efficient diagnosis and treatment. Artificial neural networks are able to analyze sets of symptoms in conjunction with labs, imaging, and exam findings to determine the likelihood of a diagnosis or outcome. Telemedicine is another use of ML and AI that uses technology such as voice recognition to deliver health care remotely. Limitations include the need for large data sets to program computers to create the algorithms. There is also the potential for misclassification of data points that do not follow the typical patterns learned by the machine. As more applications of AI and ML are developed for the surgical field, further studies are needed to determine feasibility, efficacy, and cost.
Similar content being viewed by others
References
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature. 521(7553):436–444. https://doi.org/10.1038/nature14539
Lundervold AS, Lundervold A (2018) An overview of deep learning in medical imaging focusing on MRI. Zeitschrift fur Medizinische Physik 29:102–127. https://doi.org/10.1016/j.zemedi.2018.11.002
Vaughn CJ, Kim E, O’Sullivan P, Huang E, Lin MYC, Wyles S et al (2016) Peer video review and feedback improve performance in basic surgical skills. Am J Surg 211(2):355–360. https://doi.org/10.1016/j.amjsurg.2015.08.034
Nakada SY, Hedican SP, Bishoff JT, Shichman SJ, Wolf JS Jr (2004) Expert videotape analysis and critiquing benefit laparoscopic skills training of urologists. JSLS. 8(2):183–186
Hashimoto DA, Rosman G, Witkowski ER, Stafford C, Navarette-Welton AJ, Rattner DW, Lillemoe KD, Rus DL, Meireles OR (2019) Computer vision analysis of intraoperative video: automated recognition of operative steps in laparoscopic sleeve gastrectomy. Annals of Surg 270:414–421. https://doi.org/10.1097/SLA.0000000000003460
Vedula SS, Ishii M, Hager GD (2017) Objective assessment of surgical technical skill and competency in the operating room. Annu Rev Biomed Eng 19(1):301–325. https://doi.org/10.1146/annurev-bioeng-071516-044435
Gomez ED, Aggarwal R, McMahan W, Bark K, Kuchenbecker KJ (2016) Objective assessment of robotic surgical skill using instrument contact vibrations. Surg Endosc 30:1419–1431. https://doi.org/10.1007/s00464-015-4346-z
Guru KA, Esfahani ET, Raza SJ, Bhat R, Wang K, Hammond Y, Wilding G, Peabody JO, Chowriappa AJ (2015) Cognitive skills assessment during robotic-assisted surgery: separating the wheat from the chaff. BJU Int 115(1):166–174. https://doi.org/10.1111/bju.12657
Hung AJ, Chen J, Gill IS (2018) Automated performance metrics and machine learning algorithms to measure surgeon performance and anticipate clinical outcomes in robotic surgery. JAMA Surg 153(8):770–771. https://doi.org/10.1001/jamasurg.2018.1512
French A, Lendvay TS, Sweet RM, Kowalewski TM (2017) Predicting surgical skill from the first N seconds of a task: value over task time using the isogony principle. Int Jour of Comp Assis Rad and Surg 12(7):1161–1170. https://doi.org/10.1007/s11548-017-1606-5
Carpenter BT, Sundaram CP (2017) Training the next generation of surgeons in robotic surgery. Robot Surg 4:39–44. https://doi.org/10.2147/RSRR/S70552
Jain M, Fry BT, Hess LW, Anger JT, Gewertz BL, Catchpole K (2016) Barriers to efficiency in robotic surgery: the resident effect. J Surg Res 205(2):296–304. https://doi.org/10.1016/j.jss.2016.06.092
Wiegmann DA, ElBardissi AW, Dearani JA, Daly RC, Sundt TM (2007) Disruptions in surgical flow and their relationship to surgical errors: an exploratory investigation. Surgery. 142(5):658–665. https://doi.org/10.1016/j.surg.2007.07.034
Sridhar AN, Briggs TP, Kelly JD, Nathan S (2017) Training in robotic surgery—an overview. Current Urology Reports.:18–18. https://doi.org/10.1007/s11934-017-0710-y
Okamura AM (2009) Haptic feedback in robot-assisted minimally invasive surgery. Curr Opin Urol 19(1):102–107. https://doi.org/10.1097/MOU.0b013e32831a478c
Pandya A, Eslamian S, Ying H, Nokleby M, Reisner LA (2019) A robotic recording and playback platform for training surgeons and learning autonomous behaviors using the da Vinci Surgical system. Robotics 8(9). https://doi.org/10.3390/robotics8010009
Wang S, Summers RM (2012) Machine learning and radiology. Med Image Anal 16(5):933–951. https://doi.org/10.1016/j.media.2012.02.005
Bi WL, Hosny A, Schabath MB, Giger ML, Birkbak NJ, Mehrtash A et al (2019) Artificial intelligence in cancer imaging: clinical challenges and applications. CA A Cancer J Clin 69:127–157. https://doi.org/10.3322/caac.21552
Park SY, Kim SM (2015) Acute appendicitis diagnosis using artificial neural networks. Technol Health Care 23(Suppl 2):S559–S565. https://doi.org/10.3233/THC-150994
Matsuki Y, Nakamura K, Watanabe H, Aoki T, Nakata H, Katsuragawa S, Doi K (2002) Usefulness of an artificial neural network for differentiating benign from malignant pulmonary nodules on high-resolution CT evaluation with receiver operating characteristic analysis. Am J Roentgenol 178:657–663. https://doi.org/10.2214/ajr.178.3.1780657
Litjens G, Sanchez CI, Timofeeva N, Hermsen M, Nagtegaal I, Kovacs I et al (2016) Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci Rep 6:26286. https://doi.org/10.1038/srep26286
Flodgren G, Rachas A, Farmer AJ, Inzitari M, Shepperd S (2015) Interactive telemedicine: effects on professional practice and health care outcomes. Cochrane Database Syst Rev 2015(9):CD002098. https://doi.org/10.1002/14651858.CD002098.pub2
Kahn JM, Rak KJ, Kuza CC, Ashcraft LE, Barnato AE, Fleck JC et al (2019) Determinants of intensive care unit telemedicine effectiveness. An ethnographic study. Am J Respir Crit Care Med 199(8):970–979. https://doi.org/10.1164/rccm.201802-0259OC
Lee H, Park JB, Choi SW, Yoon YE, Park HE, Lee SE, Lee SP, Kim HK, Cho HJ, Choi SY, Lee HY, Choi J, Lee YJ, Kim YJ, Cho GY, Choi J, Sohn DW (2017) Impact of a telehealth program with voice recognition technology in patients with chronic heart failure: feasibility study. JMIR Mhealth Uhealth 5(10):e127. https://doi.org/10.2196/mhealth.7058
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Conflict of Interest
The authors declare that they have no conflicts of interest.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Egert, M., Steward, J.E. & Sundaram, C.P. Machine Learning and Artificial Intelligence in Surgical Fields. Indian J Surg Oncol 11, 573–577 (2020). https://doi.org/10.1007/s13193-020-01166-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13193-020-01166-8