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
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

17262

Informations 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

Auteurs

A Studier-Fischer (A)

Department of General, Visceral, and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany. alexander@studier-fischer.com.
Department of Urology and Urosurgery, Medical Faculty of the University of Heidelberg, University Medical Center Mannheim, Mannheim, Germany. alexander@studier-fischer.com.
Division of Intelligent Systems and Robotics in Urology (ISRU), German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany. alexander@studier-fischer.com.
DKFZ Hector Cancer Institute at the University Medical Center Mannheim, Mannheim, Germany. alexander@studier-fischer.com.

M Bressan (M)

Department of General, Visceral, and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany.

A Bin Qasim (AB)

Division of Intelligent Medical Systems, German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany.
HIDSS4Health - Helmholtz Information and Data Science School for Health, Karlsruhe, Heidelberg, Germany.
National Center for Tumor Diseases (NCT) Heidelberg, a partnership between DKFZ and Heidelberg University Hospital, Heidelberg, Germany.

B Özdemir (B)

Department of General, Visceral, and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany.
Division of Intelligent Systems and Robotics in Urology (ISRU), German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany.
DKFZ Hector Cancer Institute at the University Medical Center Mannheim, Mannheim, Germany.

J Sellner (J)

Division of Intelligent Medical Systems, German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany.
HIDSS4Health - Helmholtz Information and Data Science School for Health, Karlsruhe, Heidelberg, Germany.
National Center for Tumor Diseases (NCT) Heidelberg, a partnership between DKFZ and Heidelberg University Hospital, Heidelberg, Germany.
Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany.

S Seidlitz (S)

Division of Intelligent Medical Systems, German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany.
HIDSS4Health - Helmholtz Information and Data Science School for Health, Karlsruhe, Heidelberg, Germany.
National Center for Tumor Diseases (NCT) Heidelberg, a partnership between DKFZ and Heidelberg University Hospital, Heidelberg, Germany.
Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany.

C M Haney (CM)

Department of Urology and Urosurgery, Medical Faculty of the University of Heidelberg, University Medical Center Mannheim, Mannheim, Germany.
Division of Intelligent Systems and Robotics in Urology (ISRU), German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany.
DKFZ Hector Cancer Institute at the University Medical Center Mannheim, Mannheim, Germany.

L Egen (L)

Department of Urology and Urosurgery, Medical Faculty of the University of Heidelberg, University Medical Center Mannheim, Mannheim, Germany.
Division of Intelligent Systems and Robotics in Urology (ISRU), German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany.
DKFZ Hector Cancer Institute at the University Medical Center Mannheim, Mannheim, Germany.

M Michel (M)

Department of Urology and Urosurgery, Medical Faculty of the University of Heidelberg, University Medical Center Mannheim, Mannheim, Germany.
Division of Intelligent Systems and Robotics in Urology (ISRU), German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany.
DKFZ Hector Cancer Institute at the University Medical Center Mannheim, Mannheim, Germany.

M Dietrich (M)

Department of Anesthesiology, Heidelberg University Hospital, Heidelberg, Germany.

G A Salg (GA)

Department of General, Visceral, and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany.

F Billmann (F)

Department of General, Visceral, and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany.

H Nienhüser (H)

Department of General, Visceral, and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany.

T Hackert (T)

Department of General, Visceral, and Thoracic Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.

B P Müller (BP)

Department of Digestive Surgery, University Digestive Healthcare Center, Basel, Switzerland.

L Maier-Hein (L)

Division of Intelligent Medical Systems, German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany.
HIDSS4Health - Helmholtz Information and Data Science School for Health, Karlsruhe, Heidelberg, Germany.
National Center for Tumor Diseases (NCT) Heidelberg, a partnership between DKFZ and Heidelberg University Hospital, Heidelberg, Germany.
Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany.

F Nickel (F)

Department of General, Visceral, and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany.
HIDSS4Health - Helmholtz Information and Data Science School for Health, Karlsruhe, Heidelberg, Germany.
Department of General, Visceral, and Thoracic Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.

K F Kowalewski (KF)

Department of Urology and Urosurgery, Medical Faculty of the University of Heidelberg, University Medical Center Mannheim, Mannheim, Germany.
Division of Intelligent Systems and Robotics in Urology (ISRU), German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany.
DKFZ Hector Cancer Institute at the University Medical Center Mannheim, Mannheim, Germany.

Articles similaires

[Redispensing of expensive oral anticancer medicines: a practical application].

Lisanne N van Merendonk, Kübra Akgöl, Bastiaan Nuijen
1.00
Humans Antineoplastic Agents Administration, Oral Drug Costs Counterfeit Drugs

Smoking Cessation and Incident Cardiovascular Disease.

Jun Hwan Cho, Seung Yong Shin, Hoseob Kim et al.
1.00
Humans Male Smoking Cessation Cardiovascular Diseases Female
Humans United States Aged Cross-Sectional Studies Medicare Part C
1.00
Humans Yoga Low Back Pain Female Male

Classifications MeSH