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8 results on '"Nkusi, Richard"'

1. Surgical optomics: hyperspectral imaging and deep learning towards precision intraoperative automatic tissue recognition—results from the EX-MACHYNA trial

Author

Bannone, Elisa, Collins, Toby, Esposito, Alessandro, Cinelli, Lorenzo, De Pastena, Matteo, Pessaux, Patrick, Felli, Emanuele, Andreotti, Elena, Okamoto, Nariaki, Barberio, Manuel, Felli, Eric, Montorsi, Roberto Maria, Ingaglio, Naomi, Rodríguez-Luna, María Rita, Nkusi, Richard, Marescaux, Jacque, Hostettler, Alexandre, Salvia, Roberto, and Diana, Michele

Published
2024
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3. Partial Hepatic Vein Occlusion and Venous Congestion in Liver Exploration Using a Hyperspectral Camera: A Proposal for Monitoring Intraoperative Liver Perfusion

Author

Famularo, Simone, primary, Bannone, Elisa, additional, Collins, Toby, additional, Reitano, Elisa, additional, Okamoto, Nariaki, additional, Mishima, Kohei, additional, Riva, Pietro, additional, Tsai, Yu-Chieh, additional, Nkusi, Richard, additional, Hostettler, Alexandre, additional, Marescaux, Jacques, additional, Felli, Eric, additional, and Diana, Michele, additional

Published
2023
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4. Computer-Assisted Differentiation between Colon-Mesocolon and Retroperitoneum Using Hyperspectral Imaging (HSI) Technology

Author

Okamoto, Nariaki, primary, Rodríguez-Luna, María Rita, additional, Bencteux, Valentin, additional, Al-Taher, Mahdi, additional, Cinelli, Lorenzo, additional, Felli, Eric, additional, Urade, Takeshi, additional, Nkusi, Richard, additional, Mutter, Didier, additional, Marescaux, Jacques, additional, Hostettler, Alexandre, additional, Collins, Toby, additional, and Diana, Michele, additional

Published
2022
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6. Automatic Liver Viability Scoring with Deep Learning and Hyperspectral Imaging

Author

Felli, Eric, primary, Al-Taher, Mahdi, additional, Collins, Toby, additional, Nkusi, Richard, additional, Felli, Emanuele, additional, Baiocchini, Andrea, additional, Lindner, Veronique, additional, Vincent, Cindy, additional, Barberio, Manuel, additional, Geny, Bernard, additional, Ettorre, Giuseppe Maria, additional, Hostettler, Alexandre, additional, Mutter, Didier, additional, Gioux, Sylvain, additional, Schuster, Catherine, additional, Marescaux, Jacques, additional, Gracia-Sancho, Jordi, additional, and Diana, Michele, additional

Published
2021
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8. Prediction of In Vivo Laser-Induced Thermal Damage with Hyperspectral Imaging Using Deep Learning

Author

De Landro, Martina, Felli, Eric, Collins, Toby, Nkusi, Richard, Baiocchini, Andrea, Barberio, Manuel, Orrico, Annalisa, Pizzicannella, Margherita, Hostettler, Alexandre, Diana, Michele, and Saccomandi, Paola

Subjects
610 Medicine & health, 3. Good health
Abstract

Thermal ablation is an acceptable alternative treatment for primary liver cancer, of which laser ablation (LA) is one of the least invasive approaches, especially for tumors in high-risk locations. Precise control of the LA effect is required to safely destroy the tumor. Although temperature imaging techniques provide an indirect measurement of the thermal damage, a degree of uncertainty remains about the treatment effect. Optical techniques are currently emerging as tools to directly assess tissue thermal damage. Among them, hyperspectral imaging (HSI) has shown promising results in image-guided surgery and in the thermal ablation field. The highly informative data provided by HSI, associated with deep learning, enable the implementation of non-invasive prediction models to be used intraoperatively. Here we show a novel paradigm "peak temperature prediction model" (PTPM), convolutional neural network (CNN)-based, trained with HSI and infrared imaging to predict LA-induced damage in the liver. The PTPM demonstrated an optimal agreement with tissue damage classification providing a consistent threshold (50.6 �� 1.5 ��C) for the damage margins with high accuracy (~0.90). The high correlation with the histology score (r = 0.9085) and the comparison with the measured peak temperature confirmed that PTPM preserves temperature information accordingly with the histopathological assessment.

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