Intraoperative EXamination Using MAChine-learning-based HYperspectral for diagNosis & Autonomous Anatomy Assessment

NCT04589884 | Terminated

• 1 other identifier: 20-005
• Study Type: observational
• Target: 112
• Locations: 1 country
• Sites: 1

Brief Summary

The intraoperative recognition of target structures, which need to be preserved or selectively removed, is of paramount importance during surgical procedures. This task relies mainly on the anatomical knowledge and experience of the operator. Misperception of the anatomy can have devastating consequences. Hyperspectral imaging (HSI) represents a promising technology that is able to perform a real-time optical scanning over a large area, providing both spatial and spectral information. HSI is an already established method of objectively classifying image information in a number of scientific fields (e.g. remote sensing). Our group recently employed HSI as intraoperative tool in the porcine model to quantify perfusion of the organs of the gastrointestinal tract against robust biological markers. Results showed that this technology is able to quantify bowel blood supply with a high degree of precision. Hyperspectral signatures have been successfully used, coupled to machine learning algorithms, to discriminate fine anatomical structures such as nerves or ureters intraoperatively (unpublished data). The i-EX-MACHYNA3 study aims at translating the HSI technology in combination with several deep learning algorithms to differentiate among different classes of human tissues (including key anatomical structures such as BD, nerves and ureters).

Conditions

Parathyroid Diseases; Thyroid Diseases; Liver Cancer; Liver Metastases; Digestive Cancer; Digestive Perfusion

Keywords

Hyperspectral Imaging; Deep learning; Autonomous tissue recognition; Tissue spectral signature

Outcome Measures

Primary Outcomes (1)

• To collect human tissue spectral features to build a spectral tissue library and build successively machine learning algorithm to enable real-time automated tissue recognition

  To collect clean and consistent datasets and the evaluation of the accuracy based on ground truth evaluations, such as clinical evaluation and pathology reports.

  1 day

Secondary Outcomes (2)

• To correlate HSI values with biological data obtained as standard of care

  1 day

• To correlate HSI values with pathological data obtained as standard of care

  1 day

Study Arms (5)

Parathyroid disease

Other: Hyperspectral Imaging

Thyroid disease

Other: Hyperspectral Imaging

Liver tumors and metastases

Other: Hyperspectral Imaging

Digestive tumors

Other: Hyperspectral Imaging

Digestive perfusion

Other: Hyperspectral Imaging

Interventions

Hyperspectral Imaging OTHER

Hyperspectral images of the operative field will be collected at several time points during the surgical procedure. The device used is the TIVITA® compact Hyperspectral imaging system (Diaspective Vision GmbH, Germany). It is a CE (European Economic Area) mark approved device. The acquisition takes roughly 10 seconds, is contrast-free and contact-free.

Digestive perfusion; Digestive tumors; Liver tumors and metastases; Parathyroid disease; Thyroid disease

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients undergoing open surgical elective or emergency procedures. Patients undergoing laparoscopic procedure will also be informed about the study and in case of conversion to open surgery, will be enrolled in the study.

You may qualify if:

• Man or woman over 18 years old.
• Scheduled for elective or emergency surgery
• Patient able to receive and understand information related to the study.
• Patient affiliated to the French social security system.

You may not qualify if:

• Contra-indication for anesthesia
• Pregnant or lactating patient.
• Patient under guardianship or trusteeship.
• Patient under the protection of justice.

Sponsors & Collaborators

• IHU Strasbourg: lead
• ARC Foundation for Cancer Research: collaborator

Study Sites (1)

Service de Chirurgie Digestive et Endocrinienne, NHC

Strasbourg, France

Location

Related Publications (22)

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MeSH Terms

Conditions

Parathyroid Diseases; Thyroid Diseases; Liver Neoplasms; Gastrointestinal Neoplasms

Interventions

Hyperspectral Imaging

Condition Hierarchy (Ancestors)

Endocrine System Diseases; Digestive System Neoplasms; Neoplasms by Site; Neoplasms; Digestive System Diseases; Liver Diseases; Gastrointestinal Diseases

Intervention Hierarchy (Ancestors)

Spectrum Analysis; Chemistry Techniques, Analytical; Investigative Techniques

Study Officials

• Michele DIANA, MD, PhD

  Service de Chirurgie Digestive et Endocrinienne, NHC, Strasbourg

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: October 9, 2020
• First Posted: October 19, 2020
• Study Start: September 22, 2020
• Primary Completion: October 15, 2021
• Study Completion: October 15, 2021
• Last Updated: January 9, 2024

Record last verified: 2024-01

Data Sharing

• IPD Sharing: Will not share

Locations

FR (1)