Treffer: Development of an artificial intelligence based virtual tool for measuring distances during image-guided surgery.
Title:
Development of an artificial intelligence based virtual tool for measuring distances during image-guided surgery.
Authors:
Kwok R; Surgical Artificial Intelligence Research Academy, University Health Network, Toronto, ON, Canada., Yoshida T; Surgical Artificial Intelligence Research Academy, University Health Network, Toronto, ON, Canada.; Department of Gastroenterological Surgery, Hokkaido University Graduate School of Medicine, Hokkaido, Japan., Hunter J; UHN Data Team, University Health Network, Toronto, ON, Canada., Laplante S; Surgical Artificial Intelligence Research Academy, University Health Network, Toronto, ON, Canada.; Division of Metabolic and Abdominal Wall Reconstructive Surgery (MARS), Mayo Clinic, Rochester, MN, USA., Brudno M; UHN Data Team, University Health Network, Toronto, ON, Canada., Fecso A; Surgical Artificial Intelligence Research Academy, University Health Network, Toronto, ON, Canada.; Department of Surgery, University of Toronto, 399 Bathurst Street, Toronto, ON, M5T 258, Canada., Okrainec A; Surgical Artificial Intelligence Research Academy, University Health Network, Toronto, ON, Canada.; Department of Surgery, University of Toronto, 399 Bathurst Street, Toronto, ON, M5T 258, Canada., Madani A; Surgical Artificial Intelligence Research Academy, University Health Network, Toronto, ON, Canada. amin.madani@uhn.ca.; Department of Surgery, University of Toronto, 399 Bathurst Street, Toronto, ON, M5T 258, Canada. amin.madani@uhn.ca.
Source:
Surgical endoscopy [Surg Endosc] 2026 Jan; Vol. 40 (1), pp. 679-687. Date of Electronic Publication: 2025 Dec 09.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Springer Country of Publication: Germany NLM ID: 8806653 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1432-2218 (Electronic) Linking ISSN: 09302794 NLM ISO Abbreviation: Surg Endosc Subsets: MEDLINE
Imprint Name(s):
Publication: 1992- : New York : Springer
Original Publication: [Berlin] : Springer International, c1987-
Original Publication: [Berlin] : Springer International, c1987-
MeSH Terms:
References:
Banka G, Woodard G, Hernandez-Boussard T, Morton JM (2012) Laparoscopic vs open gastric bypass surgery: differences in patient demographics, safety, and outcomes. Arch Surg 147:550–556. https://doi.org/10.1001/archsurg.2012.195. (PMID: 10.1001/archsurg.2012.19522786543)
Luján JA, Frutos MD, Hernández Q, Liron R, Cuenca JR, Valero G, Parrilla P (2004) Laparoscopic versus open gastric bypass in the treatment of morbid obesity. Ann Surg 239:433–437. https://doi.org/10.1097/01.sla.0000120071.75691.1f. (PMID: 10.1097/01.sla.0000120071.75691.1f150243021356246)
Sørensen SMD, Savran MM, Konge L, Bjerrum F (2016) Three-dimensional versus two-dimensional vision in laparoscopy: a systematic review. Surg Endosc 30:11–23. https://doi.org/10.1007/s00464-015-4189-7. (PMID: 10.1007/s00464-015-4189-725840896)
Slagter N, van Wilsum M, de Heide LJM, Jutte EH, Kaijser MA, Damen SL, van Beek AP, Emous M (2022) Laparoscopic small bowel length measurement in bariatric surgery using a hand-over-hand technique with marked graspers: an ex vivo experiment. Obes Surg 32:1201–1208. https://doi.org/10.1007/s11695-022-05918-z. (PMID: 10.1007/s11695-022-05918-z352015718933352)
Hort A, Cheng Q, Morosin T, Yoon P, Talbot M (2023) Optimal common limb length in Roux-en-Y gastric bypass surgery: is it important for an ideal outcome? – a systematic review. ANZ J Surg 93:851–858. https://doi.org/10.1111/ans.18192. (PMID: 10.1111/ans.1819236480354)
Zorrilla-Nunez LF, Campbell A, Giambartolomei G, Lo Menzo E, Szomstein S, Rosenthal RJ (2019) The importance of the biliopancreatic limb length in gastric bypass: a systematic review. Surg Obes Relat Dis 15:43–49. https://doi.org/10.1016/j.soard.2018.10.013. (PMID: 10.1016/j.soard.2018.10.01330501957)
Choban PS, Flancbaum L (2002) The effect of Roux limb lengths on outcome after Roux-en-Y gastric bypass: a prospective, randomized clinical trial. Obes Surg 12:540–545. https://doi.org/10.1381/096089202762252316. (PMID: 10.1381/09608920276225231612194548)
Madan AK, Harper JL, Tichansky DS (2008) Techniques of laparoscopic gastric bypass: on-line survey of American Society for Bariatric Surgery practicing surgeons. Surg Obes Relat Dis 4:166–172. https://doi.org/10.1016/j.soard.2007.08.006. (PMID: 10.1016/j.soard.2007.08.00618069071)
Gazer B, Rosin D, Bar-Zakai B, Willenz U, Doron O, Gutman M, Nevler A (2017) Accuracy and inter-operator variability of small bowel length measurement at laparoscopy. Surg Endosc 31:4697–4704. https://doi.org/10.1007/s00464-017-5538-5. (PMID: 10.1007/s00464-017-5538-528409379)
Muise ED, Tackett JJ, Callender KA, Gandotra N, Bamdad MC, Cowles RA (2016) Accurate assessment of bowel length: the method of measurement matters. J Surg Res 206:146–150. https://doi.org/10.1016/j.jss.2016.07.022. (PMID: 10.1016/j.jss.2016.07.02227916354)
Wagner M, Mayer BFB, Bodenstedt S, Stemmer K, Fereydooni A, Speidel S, Dillmann R, Nickel F, Fischer L, Kenngott HG (2018) Computer-assisted 3D bowel length measurement for quantitative laparoscopy. Surg Endosc 32:4052–4061. https://doi.org/10.1007/s00464-018-6135-y. (PMID: 10.1007/s00464-018-6135-y29508142)
Mascagni P, Alapatt D, Sestini L, Altieri MS, Madani A, Watanabe Y, Alseidi A, Redan JA, Alfieri S, Costamagna G, Boškoski I, Padoy N, Hashimoto DA (2022) Computer vision in surgery: from potential to clinical value. Npj Digit Med 5:1–9. https://doi.org/10.1038/s41746-022-00707-5. (PMID: 10.1038/s41746-022-00707-5)
Madani A, Namazi B, Altieri MS, Hashimoto DA, Rivera AM, Pucher PH, Navarrete-Welton A, Sankaranarayanan G, Brunt LM, Okrainec A, Alseidi A (2022) Artificial intelligence for intraoperative guidance: using semantic segmentation to identify surgical anatomy during laparoscopic cholecystectomy. Ann Surg 276:363–369. https://doi.org/10.1097/SLA.0000000000004594. (PMID: 10.1097/SLA.000000000000459433196488)
Ping L, Wang Z, Yao J, Gao J, Yang S, Li J, Shi J, Wu W, Hua S, Wang H (2023) Application and evaluation of surgical tool and tool tip recognition based on convolutional neural network in multiple endoscopic surgical scenarios. Surg Endosc 37:7376–7384. https://doi.org/10.1007/s00464-023-10323-3. (PMID: 10.1007/s00464-023-10323-337580576)
Kitaguchi D, Takeshita N, Matsuzaki H, Hasegawa H, Igaki T, Oda T, Ito M (2022) Deep learning-based automatic surgical step recognition in intraoperative videos for transanal total mesorectal excision. Surg Endosc 36:1143–1151. https://doi.org/10.1007/s00464-021-08381-6. (PMID: 10.1007/s00464-021-08381-633825016)
Patrick D, Rizzo K, Grasso S, Schriver J (2023) Length of intraabdominal measurement of bowel (LIMB). Surg Open Sci 16:68–72. https://doi.org/10.1016/j.sopen.2023.09.018. (PMID: 10.1016/j.sopen.2023.09.0183781846010561113)
Guo Y, Liu Y, Georgiou T, Lew MS (2018) A review of semantic segmentation using deep neural networks. Int J Multimed Inf Retr 7:87–93. https://doi.org/10.1007/s13735-017-0141-z. (PMID: 10.1007/s13735-017-0141-z)
Davis J, Goadrich M (2006) The relationship between precision–recall and ROC curves. In: Cohen WW, Moore A (eds) Proceedings of the 23rd international conference on machine learning, association for computing machinery, New York, pp 233–240.
Sudre CH, Li W, Vercauteren T, Ourselin S, Jorge Cardoso M (2017) Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Cardoso MJ, Arbel T, Carneiro G, Syeda-Mahmood T, Tavares JMRS, Moradi M, Bradley A, Greenspan H, Papa JP, Madabhushi A, Nascimento JC, Cardoso JS, Belagiannis V, Lu Z (eds) Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer International Publishing, Cham, pp 240–248. (PMID: 10.1007/978-3-319-67558-9_28)
Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24:381–395. https://doi.org/10.1145/358669.358692. (PMID: 10.1145/358669.358692)
Guo Y (2012) A note on the number of solutions of the coplanar P4P problem. In: Sun D (ed) 2012 12th international conference on control automation robotics & vision (ICARCV). IEEE, Guangzhou, pp 1413–1418. (PMID: 10.1109/ICARCV.2012.6485396)
Zhang Z (1999) Flexible camera calibration by viewing a plane from unknown orientations. In: Tsosos JK (eds) Proceedings of the seventh IEEE international conference on computer vision, Kerkyra, pp 666–673.
Ravi N, Gabeur V, Hu Y-T, Hu R, Ryali C, Ma T, Khedr H, Rädle R, Rolland C, Gustafson L, Mintun E, Pan J, Alwala KV, Carion N, Wu C-Y, Girshick R, Dollár P, Feichtenhofer C (2024) SAM 2: segment anything in images and videos. arXiv preprint arXiv:2408.00714.
Eagleston J, Nimeri A (2023) Optimal small bowel limb lengths of Roux-en-Y gastric bypass. Curr Obes Rep 12:345–354. https://doi.org/10.1007/s13679-023-00513-4. (PMID: 10.1007/s13679-023-00513-437466789)
Altman DG, Bland JM (1983) Measurement in medicine: the analysis of method comparison studies. J R Stat Soc Ser Stat 32:307–317. https://doi.org/10.2307/2987937. (PMID: 10.2307/2987937)
Xie E, Wang W, Yu Z, Anandkumar A, Alvarez JM, Luo P (2021) SegFormer: simple and efficient design for semantic segmentation with transformers. arXiv preprint arXiv:2105.15203.
Mosqueira-Rey E, Hernández-Pereira E, Alonso-Ríos D, Bobes-Bascarán J, Fernández-Leal Á (2023) Human-in-the-loop machine learning: a state of the art. Artif Intell Rev 56:3005–3054. https://doi.org/10.1007/s10462-022-10246-w. (PMID: 10.1007/s10462-022-10246-w)
Wagner M, Mayer BFB, Bodenstedt S, Kowalewski K-F, Nickel F, Speidel S, Fischer L, Kenngott HG, Müller-Stich B-P (2021) Comparison of conventional methods for bowel length measurement in laparoscopic surgery to a novel computer-assisted 3D measurement system. Obes Surg 31:4692–4700. https://doi.org/10.1007/s11695-021-05620-6. (PMID: 10.1007/s11695-021-05620-6343311868490232)
von Bechtolsheim F, Schmidt S, Abel S, Schneider A, Wekenborg M, Bodenstedt S, Speidel S, Weitz J, Oehme F, Distler M (2022) Does speed equal quality? Time pressure impairs minimally invasive surgical skills in a prospective crossover trial. Int J Surg 104:106813. https://doi.org/10.1016/j.ijsu.2022.106813. (PMID: 10.1016/j.ijsu.2022.106813)
Shi R, Liu Z, Duan L, Jiang T (2025) Amodal segmentation for laparoscopic surgery video instruments. Sens Imaging 26:79. https://doi.org/10.1007/s11220-025-00609-2. (PMID: 10.1007/s11220-025-00609-2)
Lepetit V, Moreno-Noguer F, Fua P (2009) EPnP: an accurate O(n) solution to the PnP problem. Int J Comput Vis 81:155–166. https://doi.org/10.1007/s11263-008-0152-6. (PMID: 10.1007/s11263-008-0152-6)
Youyang F, Qing W, Yuan Y, Chao Y (2019) Robust improvement solution to perspective-n-point problem. Int J Adv Robot Syst 16:1729881419885700. https://doi.org/10.1177/1729881419885700. (PMID: 10.1177/1729881419885700)
M Sikaroudi S Rahnamayan HR Tizhoosh 2022 Hospital-agnostic image representation learning in digital pathology. arXiv preprint arXiv:2204.02404.
Kondrateva E, Pominova M, Popova E, Sharaev M, Bernstein A, Burnaev E (2020) Domain shift in computer vision models for MRI data analysis: an overview. arXiv preprint arXiv:2010.07222.
Guan H, Liu M (2022) Domain adaptation for medical image analysis: a survey. IEEE Trans Biomed Eng 69:1173–1185. https://doi.org/10.1109/TBME.2021.3117407. (PMID: 10.1109/TBME.2021.3117407346064459011180)
Ayuso SA, Robinson JN, Okorji LM, Thompson KJ, McKillop IH, Kuwada TS, Gersin KS, Barbat SD, Bauman RW, Nimeri A (2022) Why size matters: an evaluation of gastric pouch size in Roux-en-Y gastric bypass using CT volumetric analysis and its effect on marginal ulceration. Obes Surg 32:587–592. https://doi.org/10.1007/s11695-021-05850-8. (PMID: 10.1007/s11695-021-05850-834985616)
Cherla DV, Lew DF, Escamilla RJ, Holihan JL, Cherla AS, Flores-Gonzalez J, Ko TC, Kao LS, Liang MK (2018) Differences of alternative methods of measuring abdominal wall hernia defect size: a prospective observational study. Surg Endosc 32:1228–1233. https://doi.org/10.1007/s00464-017-5797-1. (PMID: 10.1007/s00464-017-5797-128917010)
Angst E, Hiatt JR, Gloor B, Reber HA, Hines OJ (2010) Laparoscopic surgery for cancer: a systematic review and a way forward. J Am Coll Surg 211:412–423. https://doi.org/10.1016/j.jamcollsurg.2010.05.019. (PMID: 10.1016/j.jamcollsurg.2010.05.019208001992930894)
Luján JA, Frutos MD, Hernández Q, Liron R, Cuenca JR, Valero G, Parrilla P (2004) Laparoscopic versus open gastric bypass in the treatment of morbid obesity. Ann Surg 239:433–437. https://doi.org/10.1097/01.sla.0000120071.75691.1f. (PMID: 10.1097/01.sla.0000120071.75691.1f150243021356246)
Sørensen SMD, Savran MM, Konge L, Bjerrum F (2016) Three-dimensional versus two-dimensional vision in laparoscopy: a systematic review. Surg Endosc 30:11–23. https://doi.org/10.1007/s00464-015-4189-7. (PMID: 10.1007/s00464-015-4189-725840896)
Slagter N, van Wilsum M, de Heide LJM, Jutte EH, Kaijser MA, Damen SL, van Beek AP, Emous M (2022) Laparoscopic small bowel length measurement in bariatric surgery using a hand-over-hand technique with marked graspers: an ex vivo experiment. Obes Surg 32:1201–1208. https://doi.org/10.1007/s11695-022-05918-z. (PMID: 10.1007/s11695-022-05918-z352015718933352)
Hort A, Cheng Q, Morosin T, Yoon P, Talbot M (2023) Optimal common limb length in Roux-en-Y gastric bypass surgery: is it important for an ideal outcome? – a systematic review. ANZ J Surg 93:851–858. https://doi.org/10.1111/ans.18192. (PMID: 10.1111/ans.1819236480354)
Zorrilla-Nunez LF, Campbell A, Giambartolomei G, Lo Menzo E, Szomstein S, Rosenthal RJ (2019) The importance of the biliopancreatic limb length in gastric bypass: a systematic review. Surg Obes Relat Dis 15:43–49. https://doi.org/10.1016/j.soard.2018.10.013. (PMID: 10.1016/j.soard.2018.10.01330501957)
Choban PS, Flancbaum L (2002) The effect of Roux limb lengths on outcome after Roux-en-Y gastric bypass: a prospective, randomized clinical trial. Obes Surg 12:540–545. https://doi.org/10.1381/096089202762252316. (PMID: 10.1381/09608920276225231612194548)
Madan AK, Harper JL, Tichansky DS (2008) Techniques of laparoscopic gastric bypass: on-line survey of American Society for Bariatric Surgery practicing surgeons. Surg Obes Relat Dis 4:166–172. https://doi.org/10.1016/j.soard.2007.08.006. (PMID: 10.1016/j.soard.2007.08.00618069071)
Gazer B, Rosin D, Bar-Zakai B, Willenz U, Doron O, Gutman M, Nevler A (2017) Accuracy and inter-operator variability of small bowel length measurement at laparoscopy. Surg Endosc 31:4697–4704. https://doi.org/10.1007/s00464-017-5538-5. (PMID: 10.1007/s00464-017-5538-528409379)
Muise ED, Tackett JJ, Callender KA, Gandotra N, Bamdad MC, Cowles RA (2016) Accurate assessment of bowel length: the method of measurement matters. J Surg Res 206:146–150. https://doi.org/10.1016/j.jss.2016.07.022. (PMID: 10.1016/j.jss.2016.07.02227916354)
Wagner M, Mayer BFB, Bodenstedt S, Stemmer K, Fereydooni A, Speidel S, Dillmann R, Nickel F, Fischer L, Kenngott HG (2018) Computer-assisted 3D bowel length measurement for quantitative laparoscopy. Surg Endosc 32:4052–4061. https://doi.org/10.1007/s00464-018-6135-y. (PMID: 10.1007/s00464-018-6135-y29508142)
Mascagni P, Alapatt D, Sestini L, Altieri MS, Madani A, Watanabe Y, Alseidi A, Redan JA, Alfieri S, Costamagna G, Boškoski I, Padoy N, Hashimoto DA (2022) Computer vision in surgery: from potential to clinical value. Npj Digit Med 5:1–9. https://doi.org/10.1038/s41746-022-00707-5. (PMID: 10.1038/s41746-022-00707-5)
Madani A, Namazi B, Altieri MS, Hashimoto DA, Rivera AM, Pucher PH, Navarrete-Welton A, Sankaranarayanan G, Brunt LM, Okrainec A, Alseidi A (2022) Artificial intelligence for intraoperative guidance: using semantic segmentation to identify surgical anatomy during laparoscopic cholecystectomy. Ann Surg 276:363–369. https://doi.org/10.1097/SLA.0000000000004594. (PMID: 10.1097/SLA.000000000000459433196488)
Ping L, Wang Z, Yao J, Gao J, Yang S, Li J, Shi J, Wu W, Hua S, Wang H (2023) Application and evaluation of surgical tool and tool tip recognition based on convolutional neural network in multiple endoscopic surgical scenarios. Surg Endosc 37:7376–7384. https://doi.org/10.1007/s00464-023-10323-3. (PMID: 10.1007/s00464-023-10323-337580576)
Kitaguchi D, Takeshita N, Matsuzaki H, Hasegawa H, Igaki T, Oda T, Ito M (2022) Deep learning-based automatic surgical step recognition in intraoperative videos for transanal total mesorectal excision. Surg Endosc 36:1143–1151. https://doi.org/10.1007/s00464-021-08381-6. (PMID: 10.1007/s00464-021-08381-633825016)
Patrick D, Rizzo K, Grasso S, Schriver J (2023) Length of intraabdominal measurement of bowel (LIMB). Surg Open Sci 16:68–72. https://doi.org/10.1016/j.sopen.2023.09.018. (PMID: 10.1016/j.sopen.2023.09.0183781846010561113)
Guo Y, Liu Y, Georgiou T, Lew MS (2018) A review of semantic segmentation using deep neural networks. Int J Multimed Inf Retr 7:87–93. https://doi.org/10.1007/s13735-017-0141-z. (PMID: 10.1007/s13735-017-0141-z)
Davis J, Goadrich M (2006) The relationship between precision–recall and ROC curves. In: Cohen WW, Moore A (eds) Proceedings of the 23rd international conference on machine learning, association for computing machinery, New York, pp 233–240.
Sudre CH, Li W, Vercauteren T, Ourselin S, Jorge Cardoso M (2017) Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Cardoso MJ, Arbel T, Carneiro G, Syeda-Mahmood T, Tavares JMRS, Moradi M, Bradley A, Greenspan H, Papa JP, Madabhushi A, Nascimento JC, Cardoso JS, Belagiannis V, Lu Z (eds) Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer International Publishing, Cham, pp 240–248. (PMID: 10.1007/978-3-319-67558-9_28)
Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24:381–395. https://doi.org/10.1145/358669.358692. (PMID: 10.1145/358669.358692)
Guo Y (2012) A note on the number of solutions of the coplanar P4P problem. In: Sun D (ed) 2012 12th international conference on control automation robotics & vision (ICARCV). IEEE, Guangzhou, pp 1413–1418. (PMID: 10.1109/ICARCV.2012.6485396)
Zhang Z (1999) Flexible camera calibration by viewing a plane from unknown orientations. In: Tsosos JK (eds) Proceedings of the seventh IEEE international conference on computer vision, Kerkyra, pp 666–673.
Ravi N, Gabeur V, Hu Y-T, Hu R, Ryali C, Ma T, Khedr H, Rädle R, Rolland C, Gustafson L, Mintun E, Pan J, Alwala KV, Carion N, Wu C-Y, Girshick R, Dollár P, Feichtenhofer C (2024) SAM 2: segment anything in images and videos. arXiv preprint arXiv:2408.00714.
Eagleston J, Nimeri A (2023) Optimal small bowel limb lengths of Roux-en-Y gastric bypass. Curr Obes Rep 12:345–354. https://doi.org/10.1007/s13679-023-00513-4. (PMID: 10.1007/s13679-023-00513-437466789)
Altman DG, Bland JM (1983) Measurement in medicine: the analysis of method comparison studies. J R Stat Soc Ser Stat 32:307–317. https://doi.org/10.2307/2987937. (PMID: 10.2307/2987937)
Xie E, Wang W, Yu Z, Anandkumar A, Alvarez JM, Luo P (2021) SegFormer: simple and efficient design for semantic segmentation with transformers. arXiv preprint arXiv:2105.15203.
Mosqueira-Rey E, Hernández-Pereira E, Alonso-Ríos D, Bobes-Bascarán J, Fernández-Leal Á (2023) Human-in-the-loop machine learning: a state of the art. Artif Intell Rev 56:3005–3054. https://doi.org/10.1007/s10462-022-10246-w. (PMID: 10.1007/s10462-022-10246-w)
Wagner M, Mayer BFB, Bodenstedt S, Kowalewski K-F, Nickel F, Speidel S, Fischer L, Kenngott HG, Müller-Stich B-P (2021) Comparison of conventional methods for bowel length measurement in laparoscopic surgery to a novel computer-assisted 3D measurement system. Obes Surg 31:4692–4700. https://doi.org/10.1007/s11695-021-05620-6. (PMID: 10.1007/s11695-021-05620-6343311868490232)
von Bechtolsheim F, Schmidt S, Abel S, Schneider A, Wekenborg M, Bodenstedt S, Speidel S, Weitz J, Oehme F, Distler M (2022) Does speed equal quality? Time pressure impairs minimally invasive surgical skills in a prospective crossover trial. Int J Surg 104:106813. https://doi.org/10.1016/j.ijsu.2022.106813. (PMID: 10.1016/j.ijsu.2022.106813)
Shi R, Liu Z, Duan L, Jiang T (2025) Amodal segmentation for laparoscopic surgery video instruments. Sens Imaging 26:79. https://doi.org/10.1007/s11220-025-00609-2. (PMID: 10.1007/s11220-025-00609-2)
Lepetit V, Moreno-Noguer F, Fua P (2009) EPnP: an accurate O(n) solution to the PnP problem. Int J Comput Vis 81:155–166. https://doi.org/10.1007/s11263-008-0152-6. (PMID: 10.1007/s11263-008-0152-6)
Youyang F, Qing W, Yuan Y, Chao Y (2019) Robust improvement solution to perspective-n-point problem. Int J Adv Robot Syst 16:1729881419885700. https://doi.org/10.1177/1729881419885700. (PMID: 10.1177/1729881419885700)
M Sikaroudi S Rahnamayan HR Tizhoosh 2022 Hospital-agnostic image representation learning in digital pathology. arXiv preprint arXiv:2204.02404.
Kondrateva E, Pominova M, Popova E, Sharaev M, Bernstein A, Burnaev E (2020) Domain shift in computer vision models for MRI data analysis: an overview. arXiv preprint arXiv:2010.07222.
Guan H, Liu M (2022) Domain adaptation for medical image analysis: a survey. IEEE Trans Biomed Eng 69:1173–1185. https://doi.org/10.1109/TBME.2021.3117407. (PMID: 10.1109/TBME.2021.3117407346064459011180)
Ayuso SA, Robinson JN, Okorji LM, Thompson KJ, McKillop IH, Kuwada TS, Gersin KS, Barbat SD, Bauman RW, Nimeri A (2022) Why size matters: an evaluation of gastric pouch size in Roux-en-Y gastric bypass using CT volumetric analysis and its effect on marginal ulceration. Obes Surg 32:587–592. https://doi.org/10.1007/s11695-021-05850-8. (PMID: 10.1007/s11695-021-05850-834985616)
Cherla DV, Lew DF, Escamilla RJ, Holihan JL, Cherla AS, Flores-Gonzalez J, Ko TC, Kao LS, Liang MK (2018) Differences of alternative methods of measuring abdominal wall hernia defect size: a prospective observational study. Surg Endosc 32:1228–1233. https://doi.org/10.1007/s00464-017-5797-1. (PMID: 10.1007/s00464-017-5797-128917010)
Angst E, Hiatt JR, Gloor B, Reber HA, Hines OJ (2010) Laparoscopic surgery for cancer: a systematic review and a way forward. J Am Coll Surg 211:412–423. https://doi.org/10.1016/j.jamcollsurg.2010.05.019. (PMID: 10.1016/j.jamcollsurg.2010.05.019208001992930894)
Contributed Indexing:
Keywords: Artificial intelligence; Computer vision; Measurement; Surgery
Entry Date(s):
Date Created: 20251209 Date Completed: 20260122 Latest Revision: 20260122
Update Code:
20260122
DOI:
10.1007/s00464-025-12461-2
PMID:
41366573
Database:
MEDLINE
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Introduction: Image-guided surgery has unique depth perception challenges. This complicates procedures requiring intracorporeal measurements, including gastric bypass, where conventional methods are subjective. Computer vision (CV) has been used for tool identification, which can locate key features for a mathematics-based prediction of 3D distance. This feasibility study aims to develop such a CV tool to objectively measure intraoperative distances.
Methods: Development of the proof-of-concept digital ruler involved developing a CV instrument detection algorithm, and a computer program to compute and display inter-grasper distance. These were then combined and validated. The CV algorithm was trained by annotating laparoscopic surgery videos to identify the jaw assembly. Model performance was tested against ground truth annotations. The computer program was then developed and tested with manual annotations in a bench-box simulator, using a ruler for ground truth. Both components were combined in a prototype for beta-testing and validation in simulation setting, using a bench box and surgery video recordings. Bench box validation compared pipeline and human predictions to actual measured lengths of simulated bowel. Video validation compared pipeline predictions to those shown by an intracorporeal ruler.
Results: A total of 1205 frames (64 cases) were annotated. The model was trained using a 60/20/20 training/testing/validation split. Compared to annotations, the model had a Precision Recall AUC, accuracy, and Dice Score of 0.89, 0.99, and 0.80, respectively. Forty-nine sample measurement frames were used to validate the computer program, with a mean error of estimation of 0.79 cm. Bench box testing compared to a test group showed the prototype's best performance at larger distances (150 cm), with a "human in the loop" system. In the video validation, the prototype demonstrated low measurement variability.
Conclusions: CV-based techniques can be effectively used to reduce subjectivity of intracorporeal measurement by delivering an objective measurement during image-guided surgery.
(© 2025. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)
Declarations. Disclosures: RK, TY, JH, SL, MB, and AF have no conflicts of interest to disclose. AM is a consultant for Johnson & Johnson. AO is a consultant for Medtronics and Medtech Syndicates, has equity interest in GT Metabolic Solutions and Qaelon Medical, and receives honoraria for speaking and teaching from Ethicon.