Validation and Feasibility in Clinical Practice and Concordance of an Automated System Coupling an RGB-D Camera and a Software Based on Artificial Intelligence for the Measurement of Shoulder Range of Motion for Patients Operated on a Total Reversed Shoulder Prosthesis.

Arthroplasty Complications Clinical Trial

Official title: Validation and Feasibility in Clinical Practice and Concordance of an Automated System Coupling an RGB-D Camera and the ShoulderROM, an Artificial Intelligence -Based Software, for the Measurement of Shoulder Range of Motion for Patients Operated on a Total Reversed Shoulder Prosthesis.

Status Recruiting

Clinical Trial Summary

The functional evaluation of the shoulder, which is the most mobile joint in the human body, is a complex clinical examination to perform. The mobility of the shoulder is based on a three-dimensional mobility cone, which is difficult to represent and measure. However, an accurate and reliable measurement of the shoulder's articular amplitude is fundamental for its functional evaluation. Indeed, these measurements contribute to determine the global management strategy of the patient and the follow-up of its evolution.

The conventional method of measuring shoulder joint amplitudes involves the use of a goniometer. Nevertheless, visual estimation is the most used in consultation but is limited by its very examiner-dependent character.

Technological advances have allowed the development and deployment of additional tools in the clinical setting, with the goal of simplifying, reducing measurement bias, and standardizing joint range of motion (ROM) measurement techniques.

Our team has recently published a study to validate the use of a joint ROM measurement system, coupling a RGB-D (Red Green Blue - Depth) sensor and an artificial intelligence (AI) algorithm, on volunteer subjects with no shoulder history. The RGB-D camera is a technological tool in high development and low cost. It consists of two sensors, an infrared projector and an RGB module. The camera simultaneously provides a two-dimensional (2D) image and its environment by creating a color flow using infrared technology combined with a depth map characterizing the distance of objects seen in the image. The AI algorithm then automatically detects a 2D skeleton that identifies the main joints of the upper limb (shoulder, elbow, wrist) and the trunk axis. Then, the angle of interest is measured and each mobility is automatically measured in 3D by the algorithm.

The main objective of the study is to validate and demonstrate the feasibility in clinical practice and the concordance of an automated RGB-D + AI system for the measurement of shoulder joint ROMs of patients having undergone reverse total shoulder replacement surgery. These measurements will be compared with the visual method and the goniometer, that are measurements made in normal care routine. The ROM measures obtained by means of the RGB-D + AI system will be compared to those obtained in clinical practice during the annual follow-up visit in normal care routine. The main evaluation criterion is the measurement of joint amplitude measured in degrees [°]. The ROMs that will be measured are those normally assessed in clinical practice: abduction-adduction, flexion-extension and external-internal rotation elbow to body or at 90°.

This study aims also at observing and comparing the postoperative joint ROM measurements estimated in the preoperative planning phase by the Blue-Print software with the actual postoperative ROM measured with the RGB-D + AI system.

The study is observational. The processing of the collected data does not foresee any intervention on the patient or modification of the surgeon's choice concerning the management of the patient. It is indeed a RNIPH (Recherche non impliquant la personne humaine).

Clinical Trial Description

n/a

Related Conditions & MeSH terms

• Arthroplasty Complications

• NCT number: NCT05292157
• Study type: Observational
• Source: Centre Hospitalier Universitaire de Nice
• Contact: Marc-Olivier GAUCI, MD
• Phone: +334 92 03 70 27
• Email: gauci.mo@chu-nice.fr
• Status: Recruiting
• Start date: January 1, 2022
• Completion date: May 31, 2022