Claire Chalopin1, Felix Nickel2, Annekatrin Pfahl3, Hannes Köhler3, Marianne Maktabi3, René Thieme4, Robert Sucher4, Boris Jansen-Winkeln4,5, Alexander Studier-Fischer2, Silvia Seidlitz6, Lena Maier-Hein6, Thomas Neumuth3, Andreas Melzer3, Beat Peter Müller-Stich2, Ines Gockel4. 1. Innovation Center Computer Assisted Surgery, Universität Leipzig, Semmelweisstr. 14, 04103, Leipzig, Deutschland. claire.chalopin@medizin.uni-leipzig.de. 2. Klinik für Allgemein‑, Viszeral- und Transplantationschirurgie, Universitätsklinikum Heidelberg, Heidelberg, Deutschland. 3. Innovation Center Computer Assisted Surgery, Universität Leipzig, Semmelweisstr. 14, 04103, Leipzig, Deutschland. 4. Klinik und Poliklinik für Viszeral‑, Transplantations‑, Thorax- und Gefäßchirurgie, Universitätsklinikum Leipzig, Leipzig, Deutschland. 5. Abteilung für Allgemein‑, Viszeral- und Onkologische Chirurgie, Klinikum St. Georg Leipzig, Leipzig, Deutschland. 6. Division of Intelligent Medical Systems, Deutsches Krebsforschungszentrum, Heidelberg, Deutschland.
Abstract
BACKGROUND: Intraoperative imaging assists surgeons during minimally invasive procedures. Hyperspectral imaging (HSI) is a noninvasive and noncontact optical technique with great diagnostic potential in medicine. The combination with artificial intelligence (AI) approaches to analyze HSI data is called intelligent HSI in this article. OBJECTIVE: What are the medical applications and advantages of intelligent HSI for minimally invasive visceral surgery? MATERIAL AND METHODS: Within various clinical studies HSI data from multiple in vivo tissue types and oncological resections were acquired using an HSI camera system. Different AI algorithms were evaluated for detection and discrimination of organs, risk structures and tumors. RESULTS: In an experimental animal study 20 different organs could be differentiated with high precision (> 95%) using AI. In vivo, the parathyroid glands could be discriminated from surrounding tissue with an F1 score of 47% and sensitivity of 75%, and the bile duct with an F1 score of 79% and sensitivity of 90%. Furthermore, ex vivo tumor tissue could be successfully detected with an area under the receiver operating characteristic (ROC) curve (AUC) larger than 0.91. DISCUSSION: This study demonstrates that intelligent HSI can automatically and accurately detect different tissue types. Despite great progress in the last decade intelligent HSI still has limitations. Thus, accurate AI algorithms that are easier to understand for the user and an extensive standardized and continuously growing database are needed. Further clinical studies should support the various medical applications and lead to the adoption of intelligent HSI in the clinical routine practice.
BACKGROUND: Intraoperative imaging assists surgeons during minimally invasive procedures. Hyperspectral imaging (HSI) is a noninvasive and noncontact optical technique with great diagnostic potential in medicine. The combination with artificial intelligence (AI) approaches to analyze HSI data is called intelligent HSI in this article. OBJECTIVE: What are the medical applications and advantages of intelligent HSI for minimally invasive visceral surgery? MATERIAL AND METHODS: Within various clinical studies HSI data from multiple in vivo tissue types and oncological resections were acquired using an HSI camera system. Different AI algorithms were evaluated for detection and discrimination of organs, risk structures and tumors. RESULTS: In an experimental animal study 20 different organs could be differentiated with high precision (> 95%) using AI. In vivo, the parathyroid glands could be discriminated from surrounding tissue with an F1 score of 47% and sensitivity of 75%, and the bile duct with an F1 score of 79% and sensitivity of 90%. Furthermore, ex vivo tumor tissue could be successfully detected with an area under the receiver operating characteristic (ROC) curve (AUC) larger than 0.91. DISCUSSION: This study demonstrates that intelligent HSI can automatically and accurately detect different tissue types. Despite great progress in the last decade intelligent HSI still has limitations. Thus, accurate AI algorithms that are easier to understand for the user and an extensive standardized and continuously growing database are needed. Further clinical studies should support the various medical applications and lead to the adoption of intelligent HSI in the clinical routine practice.
Authors: Esther Kho; Behdad Dashtbozorg; Lisanne L de Boer; Koen K Van de Vijver; Henricus J C M Sterenborg; Theo J M Ruers Journal: Biomed Opt Express Date: 2019-08-07 Impact factor: 3.732
Authors: Marianne Maktabi; Hannes Köhler; Magarita Ivanova; Thomas Neumuth; Nada Rayes; Lena Seidemann; Robert Sucher; Boris Jansen-Winkeln; Ines Gockel; Manuel Barberio; Claire Chalopin Journal: Int J Med Robot Date: 2020-05-10 Impact factor: 2.547
Authors: Silvia Seidlitz; Jan Sellner; Jan Odenthal; Berkin Özdemir; Alexander Studier-Fischer; Samuel Knödler; Leonardo Ayala; Tim J Adler; Hannes G Kenngott; Minu Tizabi; Martin Wagner; Felix Nickel; Beat P Müller-Stich; Lena Maier-Hein Journal: Med Image Anal Date: 2022-05-27 Impact factor: 13.828
Authors: Boris Jansen-Winkeln; Manuel Barberio; Claire Chalopin; Katrin Schierle; Michele Diana; Hannes Köhler; Ines Gockel; Marianne Maktabi Journal: Cancers (Basel) Date: 2021-02-25 Impact factor: 6.575