Spectral characterization of intraoperative renal perfusion using hyperspectral imaging and artificial intelligence.
Hyperspectral imaging
Machine learning
Porcine model
Renal malperfusion
Renal perfusion
Surgery
Surgical data science
Translational research
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
27 Jul 2024
27 Jul 2024
Historique:
received:
27
03
2024
accepted:
22
07
2024
medline:
28
7
2024
pubmed:
28
7
2024
entrez:
27
7
2024
Statut:
epublish
Résumé
Accurate intraoperative assessment of organ perfusion is a pivotal determinant in preserving organ function e.g. during kidney surgery including partial nephrectomy or kidney transplantation. Hyperspectral imaging (HSI) has great potential to objectively describe and quantify this perfusion as opposed to conventional surrogate techniques such as ultrasound flowmeter, indocyanine green or the subjective eye of the surgeon. An established live porcine model under general anesthesia received median laparotomy and renal mobilization. Different scenarios that were measured using HSI were (1) complete, (2) gradual and (3) partial malperfusion. The differences in spectral reflectance as well as HSI oxygenation (StO
Identifiants
pubmed: 39068299
doi: 10.1038/s41598-024-68280-3
pii: 10.1038/s41598-024-68280-3
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
17262Informations de copyright
© 2024. The Author(s).
Références
MacLennan, S. et al. Systematic review of perioperative and quality-of-life outcomes following surgical management of localised renal cancer. Eur. Urol. 62, 1097–1117. https://doi.org/10.1016/j.eururo.2012.07.028 (2012).
doi: 10.1016/j.eururo.2012.07.028
pubmed: 22841673
MacLennan, S. et al. Systematic review of oncological outcomes following surgical management of localised renal cancer. Eur. Urol. 61, 972–993. https://doi.org/10.1016/j.eururo.2012.02.039 (2012).
doi: 10.1016/j.eururo.2012.02.039
pubmed: 22405593
Simone, G. et al. On-clamp versus off-clamp partial nephrectomy: Propensity score-matched comparison of long-term functional outcomes. Int. J. Urol. 26, 985–991. https://doi.org/10.1111/iju.14079 (2019).
doi: 10.1111/iju.14079
pubmed: 31342589
El Zorkany, K., Bridson, J. M., Sharma, A. & Halawa, A. Transplant renal vein thrombosis. Exp. Clin. Transplant 15, 123–129. https://doi.org/10.6002/ect.2016.0060 (2017).
doi: 10.6002/ect.2016.0060
pubmed: 28338457
Beierwaltes, W. H., Harrison-Bernard, L. M., Sullivan, J. C. & Mattson, D. L. Assessment of renal function; clearance, the renal microcirculation, renal blood flow, and metabolic balance. Compr. Physiol. 3, 165–200. https://doi.org/10.1002/cphy.c120008 (2013).
doi: 10.1002/cphy.c120008
pubmed: 23720284
Hren, R., Sersa, G., Simoncic, U. & Milanic, M. Imaging perfusion changes in oncological clinical applications by hyperspectral imaging: A literature review. Radiol. Oncol. 56, 420–429. https://doi.org/10.2478/raon-2022-0051 (2022).
doi: 10.2478/raon-2022-0051
pubmed: 36503709
pmcid: 9784371
Sucher, R. et al. Hyperspectral imaging (HSI) of human kidney allografts. Ann. Surg. 276, e48–e55. https://doi.org/10.1097/sla.0000000000004429 (2022).
doi: 10.1097/sla.0000000000004429
pubmed: 33196483
Nickel, F. et al. Optimization of anastomotic technique and gastric conduit perfusion with hyperspectral imaging and machine learning in an experimental model for minimally invasive esophagectomy. Eur. J. Surg. Oncol. https://doi.org/10.1016/j.ejso.2023.04.007 (2023).
doi: 10.1016/j.ejso.2023.04.007
pubmed: 37105869
Samuel, A. L. Some studies in machine learning using the game of checkers. IBM J. Res. Dev. 3, 210–229. https://doi.org/10.1147/rd.33.0210 (1959).
doi: 10.1147/rd.33.0210
Chin, K., Hellebrekers, T. & Majidi, C. Machine learning for soft robotic sensing and control. Adv. Intell. Syst. 2, 1900171. https://doi.org/10.1002/aisy.201900171 (2020).
doi: 10.1002/aisy.201900171
Shi, Q. et al. Deep learning enabled smart mats as a scalable floor monitoring system. Nat. Commu. 11, 4609. https://doi.org/10.1038/s41467-020-18471-z (2020).
doi: 10.1038/s41467-020-18471-z
Saha, D. & Manickavasagan, A. Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review. Curr. Res. Food Sci. 4, 28–44. https://doi.org/10.1016/j.crfs.2021.01.002 (2021).
doi: 10.1016/j.crfs.2021.01.002
pubmed: 33659896
pmcid: 7890297
Medus, L. D., Saban, M., Francés-Víllora, J. V., Bataller-Mompeán, M. & Rosado-Muñoz, A. Hyperspectral image classification using CNN: Application to industrial food packaging. Food Control 125, 107962. https://doi.org/10.1016/j.foodcont.2021.107962 (2021).
doi: 10.1016/j.foodcont.2021.107962
Cui, R. et al. Deep learning in medical hyperspectral images: A review. Sensors https://doi.org/10.3390/s22249790 (2022).
doi: 10.3390/s22249790
pubmed: 36560346
pmcid: 9785616
Nickel, F., Studier-Fischer, A., Knödler, S. & Müller-Stich, B. P. SPACE trial—SPectrAl Characterization of organs and tissuEs during surgery, < https://www.researchregistry.com/browse-the-registry#home/registrationdetails/5fbbf2e463f2fd001b12cc30/ > (2020).
Zhou, L. et al. Selective versus hilar clamping during minimally invasive partial nephrectomy: A systematic review and meta-analysis. J. Endourol. 29, 855–863. https://doi.org/10.1089/end.2014.0878 (2015).
doi: 10.1089/end.2014.0878
pubmed: 25746718
Zhang, L. et al. Comparison of selective and main renal artery clamping in partial nephrectomy of renal cell cancer: A PRISMA-compliant systematic review and meta-analysis. Medicine 97, e11856. https://doi.org/10.1097/md.0000000000011856 (2018).
doi: 10.1097/md.0000000000011856
pubmed: 30142777
pmcid: 6112923
Klatte, T. et al. A literature review of renal surgical anatomy and surgical strategies for partial nephrectomy. Eur. Urol. 68, 980–992. https://doi.org/10.1016/j.eururo.2015.04.010 (2015).
doi: 10.1016/j.eururo.2015.04.010
pubmed: 25911061
pmcid: 4994971
Han, D. S., Johnson, J. P., Schulster, M. L. & Shah, O. Indications for and results of renal autotransplantation. Curr, Opin. Nephrol. Hypertens. 32, 183–192. https://doi.org/10.1097/mnh.0000000000000860 (2023).
doi: 10.1097/mnh.0000000000000860
pubmed: 36683544
Arrigoni, S., Turra, G. & Signoroni, A. Hyperspectral image analysis for rapid and accurate discrimination of bacterial infections: A benchmark study. Comput. Biol. Med. 88, 60–71. https://doi.org/10.1016/j.compbiomed.2017.06.018 (2017).
doi: 10.1016/j.compbiomed.2017.06.018
pubmed: 28700901
Youssef, D., Fekry, O., Badr, A., Afify, A. & Hamed, E. A new perspective on quantitative assessment of photodynamic therapy mediated hydrogel nanocomposite in wound healing using objective biospeckle and morphological local-gradient. Comput. Biol. Med. 163, 107196. https://doi.org/10.1016/j.compbiomed.2023.107196 (2023).
doi: 10.1016/j.compbiomed.2023.107196
pubmed: 37356291
Wang, S. & Sun, Z. Hydrogel and machine learning for soft robots’ sensing and signal processing: A review. J. Bionic Eng. 20, 845–857. https://doi.org/10.1007/s42235-022-00320-y (2023).
doi: 10.1007/s42235-022-00320-y
Lin, S.-B., Lei, Y. & Zhou, D.-X. Boosted kernel ridge regression: Optimal learning rates and early stopping. J. Mach. Learn. Res. 20, 1738–1773 (2019).
Bu, S. et al. An optimized machine learning model for predicting hospitalization for COVID-19 infection in the maintenance dialysis population. Comput. Biol. Med. 165, 107410. https://doi.org/10.1016/j.compbiomed.2023.107410 (2023).
doi: 10.1016/j.compbiomed.2023.107410
pubmed: 37672928
Chi, W. & Du, Y. Automatic and objective gradation of 114 183 terrorist attacks using a machine learning approach. ETRI J. 43, 694–701. https://doi.org/10.4218/etrij.2020-0138 (2021).
doi: 10.4218/etrij.2020-0138
Tetschke, F. et al. Hyperspectral imaging for monitoring oxygen saturation levels during normothermic kidney perfusion. J. Sens. Sens. Syst. 5, 313–318. https://doi.org/10.5194/jsss-5-313-2016 (2016).
doi: 10.5194/jsss-5-313-2016
Sommer, F. et al. Hyperspectral imaging during normothermic machine perfusion—A functional classification of ex vivo kidneys based on convolutional neural networks. Biomedicines 10, 397 (2022).
doi: 10.3390/biomedicines10020397
pubmed: 35203605
pmcid: 8962340
Ayala, L. et al. Spectral imaging enables contrast agent–free real-time ischemia monitoring in laparoscopic surgery. Sci. Adv. 9, eadd6778. https://doi.org/10.1126/sciadv.add6778 (2023).
doi: 10.1126/sciadv.add6778
pubmed: 36897951
pmcid: 10005169
Studier-Fischer, A. et al. HeiPorSPECTRAL: The Heidelberg porcine HyperSPECTRAL imaging dataset of 20 physiological organs. Sci. Data 10, 414. https://doi.org/10.1038/s41597-023-02315-8 (2023).
doi: 10.1038/s41597-023-02315-8
pubmed: 37355750
pmcid: 10290660
Kenngott, H. G. et al. Effects of laparoscopy, laparotomy, and respiratory phase on liver volume in a live porcine model for liver resection. Surg. Endosc. 35, 7049–7057. https://doi.org/10.1007/s00464-020-08220-0 (2021).
doi: 10.1007/s00464-020-08220-0
pubmed: 33398570
pmcid: 8599330
Dietrich, M. et al. Hyperspectral imaging for the evaluation of microcirculatory tissue oxygenation and perfusion quality in haemorrhagic shock: A porcine study. Biomedicines 9, 1829 (2021).
doi: 10.3390/biomedicines9121829
pubmed: 34944645
pmcid: 8698916
Nickel, F. et al. Computer tomographic analysis of organ motion caused by respiration and intraoperative pneumoperitoneum in a porcine model for navigated minimally invasive esophagectomy. Surg. Endosc. 32, 4216–4227. https://doi.org/10.1007/s00464-018-6168-2 (2018).
doi: 10.1007/s00464-018-6168-2
pubmed: 29603002
Nickel, F. et al. Navigation system for minimally invasive esophagectomy: Experimental study in a porcine model. Surg. Endosc. 27, 3663–3670. https://doi.org/10.1007/s00464-013-2941-4 (2013).
doi: 10.1007/s00464-013-2941-4
pubmed: 23549772
Gehrig, T. et al. Comparison of different surgical techniques in distal pancreatectomy: An experimental study in a porcine model. Surg. Innov. 18, 329–337. https://doi.org/10.1177/1553350610395032 (2011).
doi: 10.1177/1553350610395032
pubmed: 21307018
Kilkenny, C., Browne, W., Cuthill, I. C., Emerson, M. & Altman, D. G. Animal research: Reporting in vivo experiments: The ARRIVE guidelines. Br. J. Pharmacol. 160, 1577–1579. https://doi.org/10.1111/j.1476-5381.2010.00872.x (2010).
doi: 10.1111/j.1476-5381.2010.00872.x
pubmed: 20649561
pmcid: 2936830
Holmer, A., Marotz, J., Wahl, P., Dau, M. & Kammerer, P. W. Hyperspectral imaging in perfusion and wound diagnostics: Methods and algorithms for the determination of tissue parameters. Biomed. Tech. https://doi.org/10.1515/bmt-2017-0155 (2018).
doi: 10.1515/bmt-2017-0155
Studier-Fischer, A. et al. Spectral organ fingerprints for machine learning-based intraoperative tissue classification with hyperspectral imaging in a porcine model. Sci. Rep. 12, 11028. https://doi.org/10.1038/s41598-022-15040-w (2022).
doi: 10.1038/s41598-022-15040-w
pubmed: 35773276
pmcid: 9247052
Lee, D. K. Alternatives to P value: Confidence interval and effect size. Korean J. Anesthesiol. 69, 555–562. https://doi.org/10.4097/kjae.2016.69.6.555 (2016).
doi: 10.4097/kjae.2016.69.6.555
pubmed: 27924194
pmcid: 5133225
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444. https://doi.org/10.1038/nature14539 (2015).
doi: 10.1038/nature14539
pubmed: 26017442
Maier-Hein, L. et al. Metrics reloaded: Recommendations for image analysis validation. arXiv (2023). < https://arxiv.org/abs/2206.01653 >.
Wold, S., Esbensen, K. & Geladi, P. Principal component analysis. Chemometrics Intell. Lab. Syst. 2, 37–52. https://doi.org/10.1016/0169-7439(87)80084-9 (1987).
doi: 10.1016/0169-7439(87)80084-9