Artificial intelligence-enhanced fluorescence molecular imaging in colorectal cancer surgery: Advances and future directions

Abstract

Achieving surgical precision in colorectal cancer remains challenging due to subjective interpretation of visual and tactile cues, which frequently results in positive resection margins or overlooked microscopic lesions. Although intraoperative fluorescence molecular imaging improves real-time visualization, its broader clinical adoption is hindered by interobserver variability, false-positive signals, and ambiguous boundary delineation. The integration of artificial intelligence (AI) is shifting fluorescence imaging from purely visual assistance toward quantitative, data-driven surgical guidance. Here, we systematically examine the synergistic evolution of three core pillars—targeted molecular probes, advanced imaging devices, and AI algorithms—focusing on recent progress in automated tumor segmentation, discrimination between malignant and inflammatory tissues, vascular mapping, sentinel lymph node detection, and anastomotic perfusion assessment. We also discuss the clinical utility of AI-enhanced fluorescence imaging in resecting primary and metastatic tumors, alongside its emerging role in surgical education, and critically appraise current obstacles concerning data availability, regulatory approval, and clinical validation. Finally, we outline prospective directions aimed at fostering more predictive and personalized surgical strategies, while recognizing that most of these applications remain in early-stage validation.

Keywords

Artificial intelligence / Fluorescence molecular imaging / Indocyanine green / Colorectal cancer / Surgical navigation

References

[1] Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. May—Jun 2024; 74(3): 229-263. https://doi.org/10.3322/caac.21834

[2] Healy MA, Peacock O, Hu CY, et al. High rate of positive circumferential resection margin in colon cancer: a national appraisal and call for action. Ann Surg . Dec 1 2022; 276(6): 1023-1028. https://doi.org/10.1097/sla.0000000000004682

[3] Wei P, Ren M, Wang Q, et al. Analysis of specimen quality of intersphincteric resection for rectal cancer in the Chinese Transanal Total Mesorectal Excision Registry Collaborative database: a nationwide registered study. Chin J Dig Surg. 2024; 23(6): 819-825. https://doi.org/10.3760/cma.j.cn115610—20240410—00200

[4] Miao S, Sun M, Zhang B, et al. Multimodal deep learning: tumor and visceral fat impact on colorectal cancer occult peritoneal metastasis. Eur Radiol. Aug 2025; 35(8): 4522-4532. https://doi.org/10.1007/s00330—025—11450—2

[5] Zhang P, Ma C, Song F, et al. A review of advances in imaging methodology in fluorescence molecular tomography. Phys Med Biol . 2022; 67(10). https://doi.org/10.1088/1361—6560/ac5ce7

[6] Li X, Sun X, Lin H, Zhang P, Jiao M, Zhang N. In vivo tumor imaging and therapy based on near—infrared cadmium—free quantum dots. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi . Jun 25 2024; 41(3): 620-626. https://doi.org/10.7507/1001—5515.202404002

[7] Debie P, Hernot S. Emerging fluorescent molecular tracers to guide intra—operative surgical decision—making. Front Pharmacol . 2019; 10: 510. https://doi.org/10.3389/fphar.2019.00510

[8] Yan T, Weng F, Ming Y, et al. Luminescence probes in bio—applications: from principle to practice. Biosens (Basel) . Jul 8 2024; 14(7). https://doi.org/10.3390/bios14070333

[9] Mitsala A, Tsalikidis C, Pitiakoudis M, Simopoulos C, Tsaroucha AK. Artificial intelligence in colorectal cancer screening, diagnosis and treatment. A new era. Curr Oncol . Apr 23 2021; 28(3): 1581-1607. https://doi.org/10.3390/curroncol28030149

[10] Mansur A, Saleem Z, Elhakim T, Daye D. Role of artificial intelligence in risk prediction, prognostication, and therapy response assessment in colorectal cancer: current state and future directions. Front Oncol. 2023; 13: 1065402. https://doi.org/10.3389/fonc.2023.1065402

[11] Yu Z, Hong W, Green A. A new era of convergence: Molecular and Digital Medicine in the age of AI. Mol Digit Med. 2026; 1(1): 100003. https://doi.org/10.1016/j.mdmed.2026.100003

[12] Morales—Conde S, Licardie E, Alarcón I, Balla A. Indocyanine green (ICG) fluorescence guide for the use and indications in general surgery: recommendations based on the descriptive review of the literature and the analysis of experience. Cir Esp (Engl Ed). Sep 2022; 100(9): 534-554. https://doi.org/10.1016/j.cireng.2022.06.023

[13] Ankersmit M, Bonjer HJ, Hannink G, Schoonmade LJ, van der Pas M, Meijerink W. Near—infrared fluorescence imaging for sentinel lymph node identification in colon cancer: a prospective single—center study and systematic review with meta—analysis. Tech Coloproctol. Dec 2019; 23(12): 1113-1126. https://doi.org/10.1007/s10151—019—02107—6

[14] Blanco—Colino R, Espin—Basany E. Intraoperative use of ICG fluorescence imaging to reduce the risk of anastomotic leakage in colorectal surgery: a systematic review and meta—analysis. Tech Coloproctol. Jan 2018; 22(1): 15-23. https://doi.org/10.1007/s10151—017—1731—8

[15] Yasui M, Ohue M, Noura S, et al. Exploratory analysis of lateral pelvic sentinel lymph node status for optimal management of laparoscopic lateral lymph node dissection in advanced lower rectal cancer without suspected lateral lymph node metastasis. BMC Cancer. Aug 11 2021; 21(1): 911. https://doi.org/10.1186/s12885—021—08480—6

[16] Boland PA, Hardy NP, Moynihan A, et al. Intraoperative near infrared functional imaging of rectal cancer using artificial intelligence methods — now and near future state of the art. Eur J Nucl Med Mol Imaging . Aug 2024; 51(10): 3135-3148. https://doi.org/10.1007/s00259—024—06731—9

[17] Li Q, Zheng Z, Chen Y, Li Z, Feng S, Feng G. Biotinylated Viscosity Sensitive Cell Membrane Probe for Targeted Imaging and Precise Visualization of Tumor Cells and Tumors. Anal Chem. 2025; 97(3): 1627-1634. https://doi.org/10.1021/acs.analchem.4c04513

[18] Mahmood U, Weissleder R. Near—infrared optical imaging of proteases in cancer. Mol Cancer Ther. May 2003; 2(5): 489-496.

[19] Elahi SF, Wang TD. Future and advances in endoscopy. J Biophotonics. Aug 2011; 4(7—8): 471-481. https://doi.org/10.1002/jbio.201100048

[20] Mo S, Zhang X, Hameed S, Zhou Y, Dai Z. Glutathione—responsive disassembly of disulfide dicyanine for tumor imaging with reduction in background signal intensity. Theranostics. 2020; 10(5): 2130-2140. https://doi.org/10.7150/thno.39673

[21] Tkachenko M, Huber B, Hamotskyi S, et al. Exploring the role of preprocessing combinations in hyperspectral imaging for deep learning colorectal cancer detection. Sci Rep. Sep 23 2025; 15(1): 32685. https://doi.org/10.1038/s41598—025—20735—x

[22] Hardy NP, MacAonghusa P, Dalli J, et al. Clinical application of machine learning and computer vision to indocyanine green quantification for dynamic intraoperative tissue characterisation: how to do it. Surg Endosc. Aug 2023; 37(8): 6361-6370. https://doi.org/10.1007/s00464—023—09963—2

[23] Dalli J, Joosten JJ, Jindal A, et al. Impact of standardising indocyanine green fluorescence angiography technique for visual and quantitative interpretation on interuser variability in colorectal surgery. Surg Endosc. Mar 2024; 38(3): 1306-1315. https://doi.org/10.1007/s00464—023—10564—2

[24] McEntee PD, Singaravelu A, McCarrick CA, Murphy E, Boland PA, Cahill RA. Quantification of indocyanine green fluorescence angiography in colorectal surgery: a systematic review of the literature. Surg Endosc. Apr 2025; 39(4): 2677-2691. https://doi.org/10.1007/s00464—025—11621—8

[25] D'Urso A, Agnus V, Barberio M, et al. Computer—assisted quantification and visualization of bowel perfusion using fluorescence—based enhanced reality in left—sided colonic resections. Surg Endosc. Aug 2021; 35(8): 4321-4331. https://doi.org/10.1007/s00464—020—07922—9

[26] Son GM, Kwon MS, Kim Y, Kim J, Kim SH, Lee JW. Quantitative analysis of colon perfusion pattern using indocyanine green (ICG) angiography in laparoscopic colorectal surgery. Surg Endosc. May 2019; 33(5): 1640-1649. https://doi.org/10.1007/s00464—018—6439—y

[27] Achterberg FB, Bijlstra OD, Slooter MD, et al. ICG—fluorescence imaging for margin assessment during minimally invasive colorectal liver metastasis resection. JAMA Netw Open. Apr 1 2024; 7(4): e246548. https://doi.org/10.1001/jamanetworkopen.2024.6548

[28] Xu Z, Xie Y, Li Z, et al. AI—driven multimodal molecular imaging for precision oncology in breast cancer: Advances, challenges, and future directions. Mol Digit Med. 2026; 1(1): 100011. https://doi.org/10.1016/j.mdmed.2026.100011

[29] He W, Zhu H, Rao X, et al. Biophysical modeling and artificial intelligence for quantitative assessment of anastomotic blood supply in laparoscopic low anterior rectal resection. Surg Endosc. May 2025; 39(5): 3412-3421. https://doi.org/10.1007/s00464—025—11693—6

[30] Mahapatra D, Bozorgtabar B, Garnavi R. Image super—resolution using progressive generative adversarial networks for medical image analysis. Comput Med Imaging Graph. Jan 2019; 71: 30-39. https://doi.org/10.1016/j.compmedimag.2018.10.005

[31] Azad R, Aghdam EK, Rauland A, et al. Medical Image Segmentation Review: The Success of U—Net. IEEE Trans Pattern Anal Mach Intell. Dec 2024; 46(12): 10076-10095. https://doi.org/10.1109/tpami.2024.3435571

[32] Ryu K, Kitaguchi D, Nakajima K, et al. Deep learning—based vessel automatic recognition for laparoscopic right hemicolectomy. Surg Endosc. Jan 2024; 38(1): 171-178. https://doi.org/10.1007/s00464—023—10524—w

[33] Li B, Han J, Wang F, Yu B, Wang G, Yang F. Factors affecting survival prognosis of patients with rectal cancer after neoadjuvant chemoradiotherapy. Front Oncol. 2025; 15: 1562634. https://doi.org/10.3389/fonc.2025.1562634

[34] Moynihan A, Hardy N, Dalli J, et al. CLASSICA: Validating artificial intelligence in classifying cancer in real time during surgery. Colorectal Dis. Dec 2023; 25(12): 2392-2402. https://doi.org/10.1111/codi.16769

[35] Park SH, Park HM, Baek KR, Ahn HM, Lee IY, Son GM. Artificial intelligence based real—time microcirculation analysis system for laparoscopic colorectal surgery. World J Gastroenterol . Nov 28 2020; 26(44): 6945-6962. https://doi.org/10.3748/wjg.v26.i44.6945

[36] Qin J, Wu J. Realizing the Potential of Computer—Assisted Surgery by Embedding Digital Twin Technology. JMIR Med Inform. Nov 8 2022; 10(11): e35138. https://doi.org/10.2196/35138

[37] Ma Q, Meng R, Li R, et al. Multitask deep learning model based on multimodal data for predicting prognosis of rectal cancer: a multicenter retrospective study. BMC Med Inform Decis Mak. Jun 5 2025; 25(1): 209. https://doi.org/10.1186/s12911—025—03050—3

[38] Cahill RA, O'Shea DF, Khan MF, et al. Artificial intelligence indocyanine green (ICG) perfusion for colorectal cancer intra—operative tissue classification. Br J Surg. Jan 27 2021; 108(1): 5-9. https://doi.org/10.1093/bjs/znaa004

[39] Kojima S, Kitaguchi D, Igaki T, et al. Deep—learning—based semantic segmentation of autonomic nerves from laparoscopic images of colorectal surgery: an experimental pilot study. Int J Surg. 2023; 109(4): 813-820. https://doi.org/10.1097/js9.0000000000000317

[40] Kitaguchi D, Harai Y, Kosugi N, et al. Artificial intelligence for the recognition of key anatomical structures in laparoscopic colorectal surgery. Br J Surg . Sep 6 2023; 110(10): 1355-1358. https://doi.org/10.1093/bjs/znad249

[41] Narihiro S, Kitaguchi D, Hasegawa H, Takeshita N, Ito M. Deep Learning—Based Real—Time Ureter Identification in Laparoscopic Colorectal Surgery. Dis Colon Rectum. Oct 1 2024; 67(10): e1596-e1599. https://doi.org/10.1097/dcr.0000000000003335

[42] Schnelldorfer T, Castro J, Goldar—Najafi A, Liu L. Development of a Deep Learning System for Intraoperative Identification of Cancer Metastases. Ann Surg. 2024; 280(6): 1006-1013. https://doi.org/10.1097/SLA.0000000000006294

[43] Mc Entee PD, Boland PA, Cahill RA. AUGUR—AIM: Clinical validation of an artificial intelligence indocyanine green fluorescence angiography expert representer. Colorectal Dis. Apr 2025; 27(4): e70097. https://doi.org/10.1111/codi.70097

[44] Hardy NP, Epperlein JP, Dalli J, et al. Real—time administration of indocyanine green in combination with computer vision and artificial intelligence for the identification and delineation of colorectal liver metastases. Surg Open Sci. Mar 2023; 12: 48-54. https://doi.org/10.1016/j.sopen.2023.03.004

[45] Kimura T, Takiguchi K, Tsukita S, et al. Development of anatomically accurate digital organ models for surgical simulation and training. PLoS One . 2025; 20(4): e0320816. https://doi.org/10.1371/journal.pone.0320816

[46] Wang Y, Liu ZS, Wang ZB, Liu S, Sun FB. Efficacy of laparoscopic low anterior resection for colorectal cancer patients with 3D—vascular reconstruction for left coronary artery preservation. World J Gastrointest Surg. Jun 27 2024; 16(6): 1548-1557. https://doi.org/10.4240/wjgs.v16.i6.1548

[47] Cao J, Shen ZL, Ye YJ, Wang S. Application of indocyanine green fluorescence imaging in colorectal cancer surgery. Zhonghua Wei Chang Wai Ke Za Zhi. Oct 25 2019; 22(10): 997-1000. https://doi.org/10.3760/cma.j.issn.1671—0274.2019.10.017

[48] Liberale G, Bohlok A, Bormans A, et al. Indocyanine green fluorescence imaging for sentinel lymph node detection in colorectal cancer: a systematic review. Eur J Surg Oncol. Sep 2018; 44(9): 1301-1306. https://doi.org/10.1016/j.ejso.2018.05.034

[49] Vallance A, Wexner S, Berho M, et al. A collaborative review of the current concepts and challenges of anastomotic leaks in colorectal surgery. Colorectal Dis. Jan 2017; 19(1). https://doi.org/10.1111/codi.13534

[50] Jafari MD, Wexner SD, Martz JE, et al. Perfusion assessment in laparoscopic left—sided/anterior resection (PILLAR II): a multi—institutional study. J Am Coll Surg. Jan 2015; 220(1): 82-92.e1. https://doi.org/10.1016/j.jamcollsurg.2014.09.015

[51] Sucher R, Brunotte M, Seehofer D. Indocyanine green fluorescence staining in liver surgery. Chirurg. Jun 2020; 91(6): 466-473. https://doi.org/10.1007/s00104—020—01203—w

[52] Marino MV, Di Saverio S, Podda M, Gomez Ruiz M, Gomez Fleitas M. The application of indocyanine green fluorescence imaging during robotic liver resection: a case—matched study. World J Surg. Oct 2019; 43(10): 2595-2606. https://doi.org/10.1007/s00268—019—05055—2

[53] De Backer P, Eckhoff JA, Simoens J, et al. Multicentric exploration of tool annotation in robotic surgery: lessons learned when starting a surgical artificial intelligence project. Surg Endosc. Nov 2022; 36(11): 8533-8548. https://doi.org/10.1007/s00464—022—09487—1

[54] Xu J, Zhang X, Chang W. Application and prospect of artificial intelligence in minimally invasive diagnosis and treatment of colorectal cancer. Chin J Dig Surg. 2025; 24(6): 701-705. https://doi.org/10.3760/cma.j.cn115610—20250304—00082

[55] Shang Y, Chen T, Li X, Li J, Yang Q. Artificial intelligence revolution in breast cancer management: a comprehensive review. Mol Digit Med. 2026; 1(1): 100010. https://doi.org/10.1016/j.mdmed.2026.100010

[56] Sun X, Qiao T, Zhang Z, Wang X, Gao Z, Ding D. A near—infrared fluorescent probe with assembly/aggregation—induced retention effect for specific diagnosis of metastasis and image—guided surgery in breast cancer. Biosens Bioelectron. Jan 1 2025; 267: 116801. https://doi.org/10.1016/j.bios.2024.116801

[57] Lu J, Zhuang W, Dong X, Yang H. Recent advances in functional activatable probes for oncological surgery: illuminating precision or hindering clinical translation? Eur J Med Chem. Nov 5 2025; 297: 117930. https://doi.org/10.1016/j.ejmech.2025.117930

[58] Miao YR, Wang Y, Shi L, Lv JT, Yang XJ. Challenges in clinical translation of artificial intelligence and real—time imaging navigation in radical gastrectomy. World J Gastroenterol. Oct 28 2025; 31(40): 111389. https://doi.org/10.3748/wjg.v31.i40.111389

[59] Cao W, Zhou M, Zhao Z. Applications of digital twins in public health: a critical review of current capabilities and a roadmap for translational readiness. Mol Digit Med. 2026; 1(1): 100013. https://doi.org/10.1016/j.mdmed.2026.100013

[60] Varghese C, Harrison EM, O'Grady G, Topol EJ. Artificial intelligence in surgery. Nat Med. May 2024; 30(5): 1257-1268. https://doi.org/10.1038/s41591—024—02970—3

[61] Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial intelligence in surgery: promises and perils. Ann Surg. Jul 2018; 268(1): 70-76. https://doi.org/10.1097/sla.0000000000002693

[62] Zhang Y, Sun H, Qin L. Artificial intelligence in the surgical treatment of digestive system malignancies: applications and challenges. Chin J General Surg. 2025; 40(5): 338-346. https://doi.org/10.3760/cma.j.cn113855—20250311—00134

[63] Barbiero P, Viñas Torné R, Lió P. Graph representation forecasting of patient's medical conditions: toward a digital twin. Front Genet. 2021; 12: 652907. https://doi.org/10.3389/fgene.2021.652907

[64] Schmidgall S, Opfermann JD, Kim JW, Krieger A. Will your next surgeon be a robot? Autonomy and AI in robotic surgery. Sci Robot. 2025; 10(104): eadt0187. https://doi.org/10.1126/scirobotics.adt0187

[65] Dong Y, Zhang J, Xie C, Li Z. A survey of key issues in edge intelligent computing under cloud—edge—terminal architecture: computing optimization and computing offloading. J Electron & Inf Technol. 2024; 46(3): 765-776. https://doi.org/10.11999/JEIT230390

[66] Kinoshita K, Maruyama T, Kobayashi N, et al. Correction: an artificial intelligence—based nerve recognition model is useful as surgical support technology and as an educational tool in laparoscopic and robot—assisted rectal cancer surgery. Surg Endosc. Sep 2024; 38(9): 5481. https://doi.org/10.1007/s00464—024—11132—y

[67] Patrascu S, Cotofana—Graure GM, Surlin V, et al. Preoperative immunocite—derived ratios predict surgical complications better when artificial neural networks are used for analysis—a pilot comparative study. J Pers Med. Jan 1 2023; 13(1). https://doi.org/10.3390/jpm13010101

[68] Maheshwari K, Cywinski JB, Papay F, Khanna AK, Mathur P. Artificial intelligence for perioperative medicine: perioperative intelligence. Anesth Analg . Apr 1 2023; 136(4): 637-645. https://doi.org/10.1213/ane.0000000000005952