Spectral organ fingerprints for machine learning-based intraoperative tissue classification with hyperspectral imaging in a porcine model

Studier-Fischer, Alexander; Seidlitz, Silvia; Sellner, Jan; Özdemir, Berkin; Wiesenfarth, Manuel; Ayala, Leonardo; Odenthal, Jan; Knoedler, Samuel; Kowalewski, Karl‐Friedrich; Haney, Caelán Max; Camplisson, Isabella; Dietrich, Maximilian; Schmidt, Karsten; Salg, Gabriel Alexander; Kenngott, Hannes; Adler, Tim; Schreck, Nicholas; Kopp‐Schneider, Annette; Maier‐Hein, Klaus H.; Maier-Hein, Lena; Müller‐Stich, Beat P.; Nickel, Felix

doi:10.1038/s41598-022-15040-w

Cited by 23 publications

(22 citation statements)

References 66 publications

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“…Hyperspectral Imaging (HSI) is a fast and noninvasive and nonionizing monitoring technique based on spectrometric tissue analysis (88)(89)(90). The target tissue is illuminated by halogen lamps and remitted light is detected in a wavelength spectrum from visual to near-infrared light (380-1000 nm) (91).…”

Section: Hyperspectral Imagingmentioning

confidence: 99%

Postoperative free flap monitoring in reconstructive surgery—man or machine?

Knoedler

Hoch²,

Huelsboemer³

et al. 2023

Front. Surg.

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Free tissue transfer is widely used for the reconstruction of complex tissue defects. The survival of free flaps depends on the patency and integrity of the microvascular anastomosis. Accordingly, the early detection of vascular comprise and prompt intervention are indispensable to increase flap survival rates. Such monitoring strategies are commonly integrated into the perioperative algorithm, with clinical examination still being considered the gold standard for routine free flap monitoring. Despite its widespread acceptance as state of the art, the clinical examination also has its pitfalls, such as the limited applicability in buried flaps and the risk of poor interrater agreement due to inconsistent flap (failure) appearances. To compensate for these shortcomings, a plethora of alternative monitoring tools have been proposed in recent years, each of them with inherent strengths and limitations. Given the ongoing demographic change, the number of older patients requiring free flap reconstruction, e.g., after cancer resection, is rising. Yet, age-related morphologic changes may complicate the free flap evaluation in elderly patients and delay the prompt detection of clinical signs of flap compromise. In this review, we provide an overview of currently available and employed methods for free flap monitoring, with a special focus on elderly patients and how senescence may impact standard free flap monitoring strategies.

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Section: Hyperspectral Imagingmentioning

confidence: 99%

Postoperative free flap monitoring in reconstructive surgery—man or machine?

Knoedler

Hoch²,

Huelsboemer³

et al. 2023

Front. Surg.

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“…DL is currently often utilized in image recognition, a process summarized by the term radiomics. [5][6][7][8] With the rise of minimally invasive pancreatic surgery (MIPS), intra-and postoperative patient data are becoming increasingly available for AI applications. The vast amount of generated data open a rapidly growing new field for AI supported decision-making, surgical training, image guidance, and personalized postoperative management.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence in pancreatic surgery: current applications

Kuemmerli¹,

Rössler²,

Berchtold³

et al. 2023

Journal of Pancreatology

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Surgery plays a central role in the treatment of benign and malignant pancreatic diseases. Artificial intelligence (AI) is an important upcoming technology to support surgeons in pre-, intra-, and postoperative diagnosis, decision-making and training toward an optimized patient care. Current AI applications show a promising role in the evaluation of preoperative images for prediction of malignancy and resectability, intraoperative decision support, surgical training as well as a postoperative risk stratification to personalize the management of complications. This scoping review summarizes the most up to date developments of AI in pancreatic surgery with the highest available level of evidence.

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“…Hyperspectral imaging (HSI) is a novel technique in medical imaging. In previous publications it has already been shown that HSI has the ability to record organ-speci c spectral re ectance (4) and that it has the ability to evaluate changes in microcircular capillary perfusion of tissue in porcine models (4). It has now been used to assess the quality of microcirculatory perfusion in critically ill patients (5).…”

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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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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Spectral organ fingerprints for machine learning-based intraoperative tissue classification with hyperspectral imaging in a porcine model

Cited by 23 publications

References 66 publications

Postoperative free flap monitoring in reconstructive surgery—man or machine?

Postoperative free flap monitoring in reconstructive surgery—man or machine?

Artificial intelligence in pancreatic surgery: current applications

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

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