Intelligent Operating Room (inOr)
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Details and patient eligibility
About
The widely varied practice of surgery, alongside rapidly expanding specialised knowledge and evolving technology as well as the fast turnover of operating theatre staff means they often face unfamiliar operations, techniques and equipment. To the investigator's knowledge, there is no formal induction for the work undertaken specifically within the operating theatre.
Many studies have shown that standardised practices, formal training and mental rehearsal improve surgical performance.
In this context, Artificial Intelligence (AI) is expected to have vast applications in surgery, particularly through standardisation, clinical decision and training support as well as patient-centred care optimisation.
Digital SurgeryTM developed GoSurgeryTM software to consolidate induction processes, support training and achieve standardised surgical practices, ultimately improving surgical performances and patient outcomes.
GoSurgeryTM allows surgeons to prepare step-by-step standardised workflows of procedures, including equipment, tips and warnings.
In preparation for surgery, workflows can used by operating team staff as a form of induction and mental rehearsal. During the surgery, using pedal-controlled tablets, relevant information for each step of the procedure is presented. GoSurgeryTM has developed AI computer vision to recognise the steps and automatically present the workflows without user-intervention.
After the surgery, the AI will allow surgeons to review their performances uploaded onto a personal virtual Hub and compare timing of steps to their previous repository of cases, as well as giving them the ability to share any interesting or difficult cases, supporting learning opportunities and monitoring of progression.
This feasibility study sets the bases to test the ability of GoSurgeryTM to improve induction processes, team performance, surgical training and patient outcomes.
The research will compare preparedness and performance of operating staff with/without the use of GoSurgeryTM, through questionnaires, observational team assessments, technical measures and patient outcomes.
Data will be collected at Imperial College Trust, Chelsea and Westminster Hospital and University College Hospital on patients undergoing general surgery. Anonymised images of keyhole surgery shall be analysed in collaboration with Digital SurgeryTM to develop the AI computer vision software.
Full description
Primary emergency and elective general surgery procedures at St Mary's Hospital, Imperial College Healthcare Trust, Chelsea and Westminster University Hospital Trusts and University College London University Hospital, under the care of participating surgical teams shall be considered.
The study will be set up as a sequential cohort study, comparing the performance of surgical teams, before and after the introduction of GoSurgeryTM software.
We will include three Bariatric teams from St Mary's, University Hospital and Chelsea and Westminster Hospitals.
In the three cohorts, videocameras and microphones will be positioned in the operating theatre in order to capture all events and conversations taking place from the beginning to the end of each case.
As is routine surgical practice, once the patient has been prepared for surgery, the keyhole (laparoscopic) camera will be connected to the laparoscopic stack to be projected onto the operating room screens. Recording will be started at the time of inserting the camera into the patient's abdomen, as is protocol.
in the intervention phase, GoSurgeryTM workflows will be made available to the intervention group for preparation before the cases and will be displayed within the operating theatre during the operation.
They will be controlled though pedals that allow to move backwards and forwards through the workflow.
When using GoSurgeryTM with visual recognition software, the keyhole video footage shall be directly extracted using local transfer over a closed wired network.
This video shall be fed into the visual recognition software algorithm. The software shall then recognise the specific part of the procedure being performed and display the relevant information on dedicated screens showing different views for different members of the team, eg surgeon view or scrub nurse view. Videos will be used to train the machine learning software to recognise the different steps of different operations so that it may then replace the pedal controls.
Members of the surgical team shall be asked to complete questionnaires before and/or after the cases.
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Inclusion and exclusion criteria
Patient Inclusion Criteria:
- patients undergoing any elective general surgery procedures;
- patients undergoing emergency general surgery procedures where the urgency of the procedure does not preclude gaining informed consent and setting up the necessary equipment;
- all patients must have capacity and be able to give consent either in English or through the use of an interpreter.
Operating theatre staff inclusion criteria:
- any staff whom have been allocated to the case
Patient Exclusion criteria shall include:
- patients undergoing immediate or urgent interventions whereby clinical urgency impedes the appropriate consent process to take place or the equipment to be set up;
- patients whom do not have capacity;
- patients whose level of English is not sufficient to give consent and an interpreter is not available.
Operating theatre staff exclusion criteria:
- there are no exclusion criteria for the operating theatre staff.
Trial design
Primary purpose
Allocation
Interventional model
Masking
150 participants in 3 patient groups
Trial contacts and locations
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Central trial contact
Ruth Nicholson; Jasmine Winter Beatty, MBBS MSc
Data sourced from clinicaltrials.gov
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