Künstliche Intelligenz und hyperspektrale Bildgebung zur bildgestützten Assistenz in der minimal-invasiven Chirurgie

Chalopin, Claire; Nickel, Felix; Pfahl, Annekatrin; Köhler, Helmut; Maktabi, Marianne; Thieme, René; Sucher, Robert; Jansen-Winkeln, B; Studier-Fischer, Alexander; Seidlitz, Silvia; Maier-Hein, Lena; Neumuth, Thomas; Melzer, Andreas; Müller‐Stich, Beat P.; Gockel, Ines

doi:10.1007/s00104-022-01677-w

Cited by 5 publications

(7 citation statements)

References 28 publications

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“…[55] A promising novel application is intelligent hyperspectral imaging for intraoperative detection and discrimination of structures and tissues. [56]…”

Section: Intraoperative Managementmentioning

confidence: 99%

“…[55] A promising novel application is intelligent hyperspectral imaging for intraoperative detection and discrimination of structures and tissues. [56] Image guidance MIPS is associated with novel challenges for the surgeon such as limited tactile feedback, a reduced field of view and more difficult spatial orientation. Computer-assisted image-guided surgery has the potential to overcome the hurdles of MIPS.…”

Section: Decision Supportmentioning

confidence: 99%

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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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“…[55] A promising novel application is intelligent hyperspectral imaging for intraoperative detection and discrimination of structures and tissues. [56]…”

Section: Intraoperative Managementmentioning

confidence: 99%

Section: Decision Supportmentioning

confidence: 99%

Artificial intelligence in pancreatic surgery: current applications

Kuemmerli¹,

Rössler²,

Berchtold³

et al. 2023

Journal of Pancreatology

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“…The median intensive care unit and entire hospital stay were 5 (3-10) and 27 d (19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31). Seven patients (50%) developed early allograft dysfunction; the median Model of Early Allograft Function and Liver Graft Assessment Following Transplantation scores were 5.94 (4.64-7.03) and -0.50 (-1.38 to -0.13).…”

Section: Patient Characteristics and Nmp Parametersmentioning

confidence: 99%

“…[16][17][18][19][20][21][22][23][24][25][26] The capacity of HSI has been demonstrated in different medical applications, such as image-guided surgery and perfusion quality assessment during ex vivo organ MP. [17][18][19][20][21][22][23][24][25][27][28][29] However, very small anatomic regions as the extrahepatic BD area are difficult to assess with the current HSI technology as currently available because the generated indices 17,30 may not precisely assess all tissue spectra.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Biliary Complications After Human Liver Transplantation Using Hyperspectral Imaging and Convolutional Neural Networks: A Proof-of-concept Study

Fodor,

Zelger,

Pallua

et al. 2023

Transplantation

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Background. Biliary complications (BCs) negatively impact the outcome after liver transplantation. We herein tested whether hyperspectral imaging (HSI) generated data from bile ducts (BD) on reperfusion and machine learning techniques for data readout may serve as a novel approach for predicting BC. Methods. Tissue-specific data from 136 HSI liver images were integrated into a convolutional neural network (CNN). Fourteen patients undergoing liver transplantation after normothermic machine preservation served as a validation cohort. Assessment of oxygen saturation, organ hemoglobin, and tissue water levels through HSI was performed after completing the biliary anastomosis. Resected BD segments were analyzed by immunohistochemistry and real-time confocal microscopy. Results. Immunohistochemistry and real-time confocal microscopy revealed mild (grade I: 1%–40%) BD damage in 8 patients and moderate (grade II: 40%–80%) injury in 1 patient. Donor and recipient data alone had no predictive capacity toward BC. Deep learning-based analysis of HSI data resulted in >90% accuracy of automated detection of BD. The CNN-based analysis yielded a correct classification in 72% and 69% for BC/no BC. The combination of HSI with donor and recipient factors showed 94% accuracy in predicting BC. Conclusions. Deep learning-based modeling using CNN of HSI-based tissue property data represents a noninvasive technique for predicting postoperative BC.

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“…Furthermore, the technology can be implemented into flexible endoscopes in order to allow a so-called optical biopsy [ 5 ], rigid endoscopes to assist the surgeons during minimal invasive procedures [ 6 ] as well as microscopes to analyze resected tissues [ 7 ]. In several research areas, HSI has shown its great potential to discriminate between several tissue structures by analyzing the tissue–light interactions, measured as specific spectral signatures, allowing tissue perfusion assessment [ 8 , 9 ] and tissue differentiation [ 10 , 11 ]. HSI has been successfully evaluated to detect skin cancer [ 12 , 13 , 14 ], gastric cancer [ 15 ], oral cancer [ 16 ], breast cancer [ 17 ], brain cancer [ 18 , 19 , 20 , 21 , 22 ], head and neck cancer [ 23 ], as well as colorectal cancer [ 24 ] in humans.…”

Section: Introductionmentioning

confidence: 99%

Impact of Pre- and Post-Processing Steps for Supervised Classification of Colorectal Cancer in Hyperspectral Images

Tkachenko

Chalopin

Jansen-Winkeln

et al. 2023

Cancers

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Background: Recent studies have shown that hyperspectral imaging (HSI) combined with neural networks can detect colorectal cancer. Usually, different pre-processing techniques (e.g., wavelength selection and scaling, smoothing, denoising) are analyzed in detail to achieve a well-trained network. The impact of post-processing was studied less. Methods: We tested the following methods: (1) Two pre-processing techniques (Standardization and Normalization), with (2) Two 3D-CNN models: Inception-based and RemoteSensing (RS)-based, with (3) Two post-processing algorithms based on median filter: one applies a median filter to a raw predictions map, the other applies the filter to the predictions map after adopting a discrimination threshold. These approaches were evaluated on a dataset that contains ex vivo hyperspectral (HS) colorectal cancer records of 56 patients. Results: (1) Inception-based models perform better than RS-based, with the best results being 92% sensitivity and 94% specificity; (2) Inception-based models perform better with Normalization, RS-based with Standardization; (3) Our outcomes show that the post-processing step improves sensitivity and specificity by 6.6% in total. It was also found that both post-processing algorithms have the same effect, and this behavior was explained. Conclusion: HSI combined with tissue classification algorithms is a promising diagnostic approach whose performance can be additionally improved by the application of the right combination of pre- and post-processing.

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Künstliche Intelligenz und hyperspektrale Bildgebung zur bildgestützten Assistenz in der minimal-invasiven Chirurgie

Cited by 5 publications

References 28 publications

Artificial intelligence in pancreatic surgery: current applications

Artificial intelligence in pancreatic surgery: current applications

Prediction of Biliary Complications After Human Liver Transplantation Using Hyperspectral Imaging and Convolutional Neural Networks: A Proof-of-concept Study

Impact of Pre- and Post-Processing Steps for Supervised Classification of Colorectal Cancer in Hyperspectral Images

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