New intraoperative imaging technologies: Innovating the surgeon’s eye toward surgical precision

Mascagni, Pietro; Longo, Fabio; Barberio, Manuel; Seeliger, Barbara; Agnus, Vincent; Saccomandi, Paola; Hostettler, Alexandre; Marescaux, Jacques; Diana, Michele

doi:10.1002/jso.25148

Cited by 51 publications

(41 citation statements)

References 132 publications

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“…Minimally invasive surgery offers obvious advantages to patients, including a faster postoperative recovery and a shorter hospital stay. To improve surgical performances, intraoperative imaging modalities might enhance the surgical vision and assist the surgeon in the tasks of recognizing important anatomical structures that need to be safeguarded or selectively removed [1].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Analysis of In Vivo Hyperspectral Images for Automated Intraoperative Nerve Detection

et al. 2021

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Nerves are critical structures that may be difficult to recognize during surgery. Inadvertent nerve injuries can have catastrophic consequences for the patient and lead to life-long pain and a reduced quality of life. Hyperspectral imaging (HSI) is a non-invasive technique combining photography with spectroscopy, allowing non-invasive intraoperative biological tissue property quantification. We show, for the first time, that HSI combined with deep learning allows nerves and other tissue types to be automatically recognized in in vivo hyperspectral images. An animal model was used, and eight anesthetized pigs underwent neck midline incisions, exposing several structures (nerve, artery, vein, muscle, fat, skin). State-of-the-art machine learning models were trained to recognize these tissue types in HSI data. The best model was a convolutional neural network (CNN), achieving an overall average sensitivity of 0.91 and a specificity of 1.0, validated with leave-one-patient-out cross-validation. For the nerve, the CNN achieved an average sensitivity of 0.76 and a specificity of 0.99. In conclusion, HSI combined with a CNN model is suitable for in vivo nerve recognition.

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Section: Introductionmentioning

confidence: 99%

Deep Learning Analysis of In Vivo Hyperspectral Images for Automated Intraoperative Nerve Detection

et al. 2021

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“…FLER was complemented by further quantitative measurements from contrast-enhanced CT (contrast uptake in adrenal segments) and local capillary lactate levels, along with the binary interpretation of CLE (presence or absence of fluorescein uptake). Contrast-enhanced intraoperative CT is feasible where available, but entails exposure to ionizing radiation and has a significant impact on surgical workflow 9 . Fluorescence imaging is more practicable.…”

Section: Discussionmentioning

confidence: 99%

“…Optimal perfusion is key to the preservation of organ function. Advances in intraoperative imaging technologies have led to the potential for precise assessment of organ perfusion 9 . Not only does CT determine adrenal remnant volume 10 , but its intraoperative use with contrast enhancement can also provide perfusion assessment.…”

Section: Introductionmentioning

confidence: 99%

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Intraoperative imaging for remnant viability assessment in bilateral posterior retroperitoneoscopic partial adrenalectomy in an experimental model

Seeliger

Alesina

Walz

et al. 2020

British Journal of Surgery

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Background: A surgical approach preserving functional adrenal tissue allows biochemical cure while avoiding the need for lifelong steroid replacement. The aim of this experimental study was to evaluate the impact of intraoperative imaging during bilateral partial adrenalectomy on remnant perfusion and function. Methods: Five pigs underwent bilateral posterior retroperitoneoscopic central adrenal gland division (9 divided glands, 1 undivided). Intraoperative perfusion assessment included computer-assisted quantitative fluorescence imaging, contrast-enhanced CT, confocal laser endomicroscopy (CLE) and local lactate sampling. Specimen analysis after completion adrenalectomy (10 adrenal glands) comprised mitochondrial activity and electron microscopy. Results: Fluorescence signal intensity evolution over time was significantly lower in the cranial segment of each adrenal gland (mean(s.d.) 0⋅052(0⋅057) versus 0⋅133(0⋅057) change in intensity per s for cranial versus caudal parts respectively; P = 0⋅020). Concordantly, intraoperative CT in the portal phase demonstrated significantly lower contrast uptake in cranial segments (P = 0⋅031). In CLE, fluorescein contrast was observed in all caudal segments, but in only four of nine cranial segments (P = 0⋅035). Imaging findings favouring caudal perfusion were congruent, with significantly lower local capillary lactate levels caudally (mean(s.d.) 5⋅66(5⋅79) versus 11⋅58(6⋅53) mmol/l for caudal versus cranial parts respectively; P = 0⋅008). Electron microscopy showed more necrotic cells cranially (P = 0⋅031). There was no disparity in mitochondrial activity (respiratory rates, reactive oxygen species and hydrogen peroxide production) between the different segments. Conclusion: In a model of bilateral partial adrenalectomy, three intraoperative imaging modalities consistently discriminated between regular and reduced adrenal remnant perfusion. By avoiding circumferential dissection, mitochondrial function was preserved in each segment of the adrenal glands.

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“…Recent progress in medical imaging technology, surgical technique, and perioperative management has greatly contributed to the safety and efficacy of surgery. 10 This study aimed to provide an overview of established conventional techniques, new imaging technologies, and future perspectives, focusing on DL assessment in ALR (Table 1).…”

Section: Introductionmentioning

confidence: 99%

Demarcation Line Assessment in Anatomical Liver Resection: An Overview

et al. 2020

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Anatomical liver resection (ALR) is the preferred oncological approach for the treatment of primary liver malignancies, such as hepatocellular carcinoma and intrahepatic cholangiocarcinoma. The demarcation line (DL) is formed by means of selective vascular occlusion and is used by surgeons to guide ALR. Emerging intraoperative technologies are playing a major role to enhance the surgeon’s vision and ensure a precise oncologic surgery. In this article, a brief overview of modalities to assess the DL during ALRs is presented, from the established conventional techniques to future perspectives.

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New intraoperative imaging technologies: Innovating the surgeon’s eye toward surgical precision

Cited by 51 publications

References 132 publications

Deep Learning Analysis of In Vivo Hyperspectral Images for Automated Intraoperative Nerve Detection

Deep Learning Analysis of In Vivo Hyperspectral Images for Automated Intraoperative Nerve Detection

Intraoperative imaging for remnant viability assessment in bilateral posterior retroperitoneoscopic partial adrenalectomy in an experimental model

Demarcation Line Assessment in Anatomical Liver Resection: An Overview

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