Hyperspectral imaging for perioperative monitoring of microcirculatory tissue oxygenation and tissue water content in pancreatic surgery — an observational clinical pilot study

Dietrich, Maximilian; Marx, Sebastian; Forst, Maik von der; Brückner, Thomas; Schmitt, Felix C. F.; Fiedler, Mascha O.; Nickel, Felix; Studier-Fischer, Alexander; Müller‐Stich, Beat P.; Hackert, Thilo; Brenner, Thorsten; Weigand, Markus A.; Uhle, Florian; Schmidt, Karsten

doi:10.1186/s13741-021-00211-6

Cited by 17 publications

(17 citation statements)

References 30 publications

(58 reference statements)

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“…Elevated levels of serum epinephrine and norepinephrine were found in patients with traumatic brain injury (17) and hemorrhagic shock after trauma(18). Even in patients undergoing general anesthesia, the immediate effects of induction agents, leading to a signi cant increase of tissue oxygenation, perfusion index and tissue water index have been demonstrated (9).…”

Section: Discussionmentioning

confidence: 99%

“…In septic patients it revealed reduced tissue oxygenation in combination with increased tissue water content, which suggests that the technology could also distinguish between different types of shocks based on speci c patterns(6-8). Furthermore, HSI immediately showed the effects of induction agents in patients undergoing general anesthesia for pancreatic surgery (9).…”

Section: Introduction Backgroundmentioning

confidence: 99%

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Implementation of hyperspectral imaging in a trauma resuscitation room – a randomized controlled trial

Katzenschlager

Dietrich

Peterstorfer

et al. 2022

Preprint

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Background: Hyperspectral imaging (HSI) is a novel imaging technology with the ability to assess microcirculatory impairment. We aimed to assess feasibility of performing HSI, a noninvasive, contactless method to assess microcirculatory alterations, during trauma resuscitation care.Methods: This randomized controlled clinical trial was conducted in a dedicated trauma resuscitation room of a level one trauma center. We included adult patients who were admitted to the trauma resuscitation room. Patients were allocated in a 1:1 ratio to the HSI group (intervention) or control group. In addition to the standard of care, patients in the intervention group had two hyperspectral recordings (HSR) of their hand palm taken. Primary outcomes were the treatment duration of the primary survey (until end of ABCDE-evaluation, ultrasound and evaluation by the trauma team) and the total resuscitation room care (until transport to definitive care) as well as the ability to perform measurements from all HSR. Secondary outcomes were analyses from the intervention group compared to HSI measurements of 25 healthy volunteers including an analysis based on the ISS (Injury severity score) (<16 vs. ≥16). Care givers, and those assessing the outcomes were blinded to group assignment.Results: Our final analysis included 51 patients, with 25 and 26 allocated to the control and intervention group, respectively. There was a statistically significant shorter median duration of the primary survey in the control group (03:22min [Q1 to Q3 03:00 to 03:51]) compared to the intervention group (03:59min [Q1 to Q3 03:29 to 04:35]) with a difference of -37 seconds (95% CI -66 to -12). Total resuscitation room care was longer in the control group, but without significance: 60 seconds (95% CI -60 to 180). From 52 HSI, we were able to perform hyperspectral measurements on all images, with significant differences between injured patients and healthy volunteers.Conclusion: HSI proved to be feasible during resuscitation room care and can provide valuable information on the microcirculatory state. Trial registration: DRKS DRKS00024047 – www.drks.de. Registered on 13th April 2021

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Section: Discussionmentioning

confidence: 99%

Section: Introduction Backgroundmentioning

confidence: 99%

Implementation of hyperspectral imaging in a trauma resuscitation room – a randomized controlled trial

Katzenschlager

Dietrich

Peterstorfer

et al. 2022

Preprint

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“…Despite the general success of MSI and HSI [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32] , applications in the operating room (OR) have been limited. Some of the main reasons why spectral imaging has not yet found its way into surgical practice are related to image acquisition time, processing time and size of the available devices 13 .…”

Section: Mainmentioning

confidence: 99%

Spectral imaging enables contrast agent-free real-time ischemia monitoring in laparoscopic surgery

Ayala

Adler

Seidlitz

et al. 2022

Preprint

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Laparoscopic surgery has evolved as a key technique for cancer diagnosis and therapy. While characterization of the tissue perfusion is crucial in various procedures, such as partial nephrectomy, doing so by means of visual inspection remains highly challenging. Spectral imaging takes advantage of the fact that different tissue components have unique optical properties to recover relevant information on tissue function such as ischemia. However, clinical success stories for advancing laparoscopic surgery with spectral imaging are lacking to date. To address this bottleneck, we developed the first laparoscopic real-time multispectral imaging (MSI) system featuring a compact and lightweight multispectral camera and the possibility to complement the conventional RGB (Red, Green, and Blue) surgical view of the patient with functional information at a video rate of 25 Hz. To account for the high inter-patient variability of human tissue, we phrase the problem of ischemia detection as an out-of-distribution (OoD) detection problem that does not rely on data from any other patient. Using an ensemble of invertible neural networks (INNs) as a core component, our algorithm computes the likelihood of ischemia based on a short (several seconds) video sequence acquired at the beginning of each surgery. A first-in-human trial performed on 10 patients undergoing partial nephrectomy demonstrates the feasibility of our approach for fully-automatic live ischemia monitoring during laparoscopic surgery. Compared to the clinical state-of-the-art approach based on indocyanine green (ICG) fluorescence, the proposed MSI-based method does not require the injection of a contrast agent and is repeatable if the wrong segment has been clamped. Spectral imaging combined with advanced deep learning-based analysis tools could thus evolve as an important tool for fast, efficient, reliable and safe functional imaging in minimally invasive surgery.

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“…HSI has demonstrated its potential in numerous medical applications, such as tumor detection [ 20 , 21 , 22 ], skin analysis [ 23 ], and hemorrhagic shock analysis [ 24 ]. In addition, various tissue parameters have been calculated from HSI data, such as tissue oxygen saturation [ 25 , 26 ] and tissue water content [ 27 , 28 ]. Knowledge about tissue-specific properties may provide additional information regarding organ quality in transplantation medicine.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Imaging during Normothermic Machine Perfusion—A Functional Classification of Ex Vivo Kidneys Based on Convolutional Neural Networks

et al. 2022

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Facing an ongoing organ shortage in transplant medicine, strategies to increase the use of organs from marginal donors by objective organ assessment are being fostered. In this context, normothermic machine perfusion provides a platform for ex vivo organ evaluation during preservation. Consequently, analytical tools are emerging to determine organ quality. In this study, hyperspectral imaging (HSI) in the wavelength range of 550 nm–995 nm was applied. Classification of 26 kidneys based on HSI was established using KidneyResNet, a convolutional neural network (CNN) based on the ResNet-18 architecture, to predict inulin clearance behavior. HSI preprocessing steps were implemented, including automated region of interest (ROI) selection, before executing the KidneyResNet algorithm. Training parameters and augmentation methods were investigated concerning their influence on the prediction. When classifying individual ROIs, the optimized KidneyResNet model achieved 84% and 62% accuracy in the validation and test set, respectively. With a majority decision on all ROIs of a kidney, the accuracy increased to 96% (validation set) and 100% (test set). These results demonstrate the feasibility of HSI in combination with KidneyResNet for non-invasive prediction of ex vivo kidney function. This knowledge of preoperative renal quality may support the organ acceptance decision.

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Hyperspectral imaging for perioperative monitoring of microcirculatory tissue oxygenation and tissue water content in pancreatic surgery — an observational clinical pilot study

Cited by 17 publications

References 30 publications

Implementation of hyperspectral imaging in a trauma resuscitation room – a randomized controlled trial

Implementation of hyperspectral imaging in a trauma resuscitation room – a randomized controlled trial

Spectral imaging enables contrast agent-free real-time ischemia monitoring in laparoscopic surgery

Hyperspectral Imaging during Normothermic Machine Perfusion—A Functional Classification of Ex Vivo Kidneys Based on Convolutional Neural Networks

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