1
|
Moor J: The Dartmouth College artificial
intelligence conference: The next fifty years. AI Mag. 27:87–89.
2006.
|
2
|
Russell S and Norvig P: Artificial
intelligence: A modern approach. Prentice Hall, Upper Saddle River,
NJ, 1995.
|
3
|
Nilsson NJ: Artificial intelligence: A new
synthesis. Morgan Kaufmann, Burlington, MA, 1998.
|
4
|
Shinde PP and Shah S: A review of machine
learning and deep learning applications. In: Fourth International
Conference on Computing Communication Control and Automation
(ICCUBEA). IEEE, 2018.
|
5
|
Emmert-Streib F, Yang Z, Feng H, Tripathi
S and Dehmer M: An introductory review of deep learning for
prediction models with big data. Front Artif Intell.
3(4)2020.PubMed/NCBI View Article : Google Scholar
|
6
|
Hamamoto R, Suvarna K, Yamada M, Kobayashi
K, Shinkai N, Miyake M, Takahashi M, Jinnai S, Shimoyama R, Sakai
A, et al: Application of artificial intelligence technology in
oncology: Towards the establishment of precision medicine. Cancers
(Basel). 12(3532)2020.PubMed/NCBI View Article : Google Scholar
|
7
|
Sone K, Toyohara Y, Taguchi A, Miyamoto Y,
Tanikawa M, Uchino-Mori M, Iriyama T, Tsuruga T and Osuga Y:
Application of artificial intelligence in gynecologic malignancies:
A review. J Obstet Gynaecol Res. 47:2577–2585. 2021.PubMed/NCBI View Article : Google Scholar
|
8
|
Miyamoto Y, Tanikawa M, Sone K,
Mori-Uchino M, Tsuruga T and Osuga Y: Introduction of minimally
invasive surgery for the treatment of endometrial cancer in Japan:
A review. Eur J Gynaecol Oncol. 42:10–17. 2021.
|
9
|
Moglia A, Georgiou K, Georgiou E, Satava
RM and Cuschieri A: A systematic review on artificial intelligence
in robot-assisted surgery Int J. Surg. 95(106151)2021.PubMed/NCBI View Article : Google Scholar
|
10
|
Madad Zadeh S, Francois T, Calvet L,
Chauvet P, Canis M, Bartoli A and Bourdel N: SurgAI: Deep learning
for computerized laparoscopic image understanding in gynaecology.
Surg Endosc. 34:5377–5383. 2020.PubMed/NCBI View Article : Google Scholar
|
11
|
Gültekin IB, Karabük E and Köse MF: ‘Hey
Siri! Perform a type 3 hysterectomy. Please watch out for the
ureter!’ What is autonomous surgery and what are the latest
developments? J Turk Ger Gynecol Assoc. 22:58–70. 2021.PubMed/NCBI View Article : Google Scholar
|
12
|
Han J, Davids J, Ashrafian H, Darzi A,
Elson DS and Sodergren M: A systematic review of robotic surgery:
From supervised paradigms to fully autonomous robotic approaches.
Int J Med Robot. 18(e2358)2022.PubMed/NCBI View
Article : Google Scholar
|
13
|
Mascagni P, Vardazaryan A, Alapatt D,
Urade T, Emre T, Fiorillo C, Pessaux P, Mutter D, Marescaux J,
Costamagna G, et al: Artificial intelligence for surgical safety:
Automatic assessment of the critical view of safety in laparoscopic
cholecystectomy using deep learning. Ann Surg. 275:955–961.
2022.PubMed/NCBI View Article : Google Scholar
|
14
|
Padovan E, Marullo G, Tanzi L, Piazzolla
P, Moos S, Porpiglia F and Vezzetti E: A deep learning framework
for real-time 3D model registration in robot-assisted laparoscopic
surgery. Int J Med Robot. 18(e2387)2022.PubMed/NCBI View
Article : Google Scholar
|
15
|
Koo B, Robu MR, Allam M, Pfeiffer M,
Thompson S, Gurusamy K, Davidson B, Speidel S, Hawkes D, Stoyanov D
and Clarkson MJ: Automatic, global registration in laparoscopic
liver surgery. Int J Comput Assist Radiol Surg. 17:167–176.
2022.PubMed/NCBI View Article : Google Scholar
|
16
|
Namazi B, Sankaranarayanan G and Devarajan
V: A contextual detector of surgical tools in laparoscopic videos
using deep learning. Surg Endosc. 36:679–688. 2022.PubMed/NCBI View Article : Google Scholar
|
17
|
Yamazaki Y, Kanaji S, Kudo T, Takiguchi G,
Urakawa N, Hasegawa H, Yamamoto M, Matsuda Y, Yamashita K, Matsuda
T, et al: Quantitative comparison of surgical device usage in
laparoscopic gastrectomy between surgeons' skill levels: An
automated analysis using a neural network. J Gastrointest Surg.
26:1006–1014. 2022.PubMed/NCBI View Article : Google Scholar
|
18
|
Aspart F, Bolmgren JL, Lavanchy JL, Beldi
G, Woods MS, Padoy N and Hosgor E: ClipAssistNet: Bringing
real-time safety feedback to operating rooms. Int J Comput Assist
Radiol Surg. 17:5–13. 2022.PubMed/NCBI View Article : Google Scholar
|
19
|
Cheng K, You J, Wu S, Chen Z, Zhou Z, Guan
J, Peng B and Wang X: Artificial intelligence-based automated
laparoscopic cholecystectomy surgical phase recognition and
analysis. Surg Endosc. 36:3160–3168. 2022.PubMed/NCBI View Article : Google Scholar
|
20
|
Kitaguchi D, Takeshita N, Matsuzaki H,
Hasegawa H, Igaki T, Oda T and Ito M: Deep learning-based automatic
surgical step recognition in intraoperative videos for transanal
total mesorectal excision. Surg Endosc. 36:1143–1151.
2022.PubMed/NCBI View Article : Google Scholar
|
21
|
Kitaguchi D, Takeshita N, Matsuzaki H,
Takano H, Owada Y, Enomoto T, Oda T, Miura H, Yamanashi T, Watanabe
M, et al: Real-time automatic surgical phase recognition in
laparoscopic sigmoidectomy using the convolutional neural
network-based deep learning approach. Surg Endosc. 34:4924–4931.
2020.PubMed/NCBI View Article : Google Scholar
|
22
|
Hernández A, Robles de Zulueta P, Spagnolo
E, Soguero C, Cristobal I, Pascual I, López A and Ramiro-Cortijo D:
Deep learning to measure the intensity of indocyanine green in
endometriosis surgeries with intestinal resection. J Pers Med.
12(982)2022.PubMed/NCBI View Article : Google Scholar
|
23
|
Twinanda AP, Yengera G, Mutter D,
Marescaux J and Padoy N: RSDNet: Learning to predict remaining
surgery duration from laparoscopic videos without manual
annotations. IEEE Trans Med Imaging. 38:1069–1078. 2019.PubMed/NCBI View Article : Google Scholar
|
24
|
Bodenstedt S, Wagner M, Mündermann L,
Kenngott H, Müller-Stich B, Breucha M, Torge Mees S, Weitz J and
Speidel S: Prediction of laparoscopic procedure duration using
unlabeled, multimodal sensor data. Int J Comput Assist Radiol Surg.
14:1089–1095. 2019.PubMed/NCBI View Article : Google Scholar
|
25
|
Igaki T, Kitaguchi D, Kojima S, Hasegawa
H, Takeshita N, Mori K, Kinugasa Y and Ito M: Artificial
intelligence-based total mesorectal excision plane navigation in
laparoscopic colorectal surgery. Dis Colon Rectum. 65:e329–e333.
2022.PubMed/NCBI View Article : Google Scholar
|
26
|
Kumazu Y, Kobayashi N, Kitamura N, Rayan
E, Neculoiu P, Misumi T, Hojo Y, Nakamura T, Kumamoto T, Kurahashi
Y, et al: Automated segmentation by deep learning of loose
connective tissue fibers to define safe dissection planes in
robot-assisted gastrectomy. Sci Rep. 11(21198)2021.PubMed/NCBI View Article : Google Scholar
|
27
|
Moglia A, Morelli L, D'Ischia R, Fatucchi
LM, Pucci V, Berchiolli R, Ferrari M and Cuschieri A: Ensemble deep
learning for the prediction of proficiency at a virtual simulator
for robot-assisted surgery. Surg Endosc. 36:6473–6479.
2022.PubMed/NCBI View Article : Google Scholar
|
28
|
Zheng Y, Leonard G, Zeh H and Fey AM:
Frame-wise detection of surgeon stress levels during laparoscopic
training using kinematic data. Int J Comput Assist Radiol Surg.
17:785–794. 2022.PubMed/NCBI View Article : Google Scholar
|
29
|
Yang GZ, Cambias J, Cleary K, Daimler E,
Drake J, Dupont PE, Hata N, Kazanzides P, Martel S, Patel RV, et
al: Medical robotics-Regulatory, ethical, and legal considerations
for increasing levels of autonomy. Sci Robot.
2(eaam8638)2017.PubMed/NCBI View Article : Google Scholar
|
30
|
Nagy TD and Haidegger T: Performance and
capability assessment in surgical subtask automation. Sensors
(Basel). 22(2501)2022.PubMed/NCBI View Article : Google Scholar
|
31
|
Shademan A, Decker RS, Opfermann JD,
Leonard S, Krieger A and Kim PCW: Supervised autonomous robotic
soft tissue surgery. Sci. Transl. Med. 8(337ra64)2016.PubMed/NCBI View Article : Google Scholar
|
32
|
Saeidi H, Ge J, Kam M, Opfermann JD,
Leonard S, Joshi AS and Krieger A: Supervised autonomous
electrosurgery via biocompatible near-infrared tissue tracking
techniques. IEEE Trans Med Robot Bionics. 1:228–236.
2019.PubMed/NCBI View Article : Google Scholar
|
33
|
Saeidi H, Le HND, Opfermann JD, Leonard S,
Kim A, Hsieh MH, Kang JU and Krieger A: Autonomous laparoscopic
robotic suturing with a novel actuated suturing tool and 3D
endoscope. IEEE Int Conf Robot Autom. 2019:1541–1547.
2019.PubMed/NCBI View Article : Google Scholar
|
34
|
Kam M, Saeidi H, Wei W, Opfermann JD,
Leonard S, Hsieh MH, Kang JU and Krieger A: Semi-autonomous robotic
anastomoses of vaginal cuffs using marker enhanced 3D imaging and
path planning. Med Image Comput Comput Assist Interv. 11768:65–73.
2019.PubMed/NCBI View Article : Google Scholar
|
35
|
Saeidi H, Opfermann JD, Kam M, Raghunathan
S, Leonard S and Krieger A: A confidence-based shared control
strategy for the Smart Tissue Autonomous Robot (STAR). Rep US.
1268-1275:2018.PubMed/NCBI View Article : Google Scholar
|
36
|
Saeidi H, Opfermann JD, Kam M, Wei W,
Leonard S, Hsieh MH, Kang JU and Krieger A: Autonomous robotic
laparoscopic surgery for intestinal anastomosis. Sci Robot.
7(eabj2908)2022.PubMed/NCBI View Article : Google Scholar
|
37
|
Hannaford B, Rosen J, Friedman DW, King H,
Roan P, Cheng L, Glozman D, Ma J, Kosari SN and White L: Raven-II:
An open platform for surgical robotics research. IEEE Trans Biomed
Eng. 60:954–959. 2013.PubMed/NCBI View Article : Google Scholar
|
38
|
Hu D, Gong Y, Seibel EJ, Sekhar LN and
Hannaford B: Semi-autonomous image-guided brain tumour resection
using an integrated robotic system: A bench-top study. Int J Med
Robot. 14(e1872)2018.PubMed/NCBI View
Article : Google Scholar
|
39
|
Gao Q, Tan N and Sun Z: A hybrid
learning-based hysteresis compensation strategy for surgical
robots. Int J Med Robot. 17(e2275)2021.PubMed/NCBI View
Article : Google Scholar
|