Clinical trial NCT04011189: Pain Detection Through Automated Video Analysis
Official title: Pain Detection Through Automated Video Analysis
Study type: INTERVENTIONAL
Overall status: ACTIVE_NOT_RECRUITING
Phase: NA
Conditions: Pain, Postoperative
Keywords: Postoperative pain
Automated video analysis
Pain score
Face recognition
Lead sponsor: KK Women's and Children's Hospital
Sponsor class: OTHER_GOV
First posted: 2019-07-08
Enrollment: 370
Sex: ALL
Minimum age (years): 6
Maximum age (years): 70
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: SUPPORTIVE_CARE

Brief summary: The study team has developed an algorithm for pain assessment based on automated video facial and body pose analysis. The investigators aim to assess the sensitivity of this algorithm in detecting pain in post-surgical patients and refine the algorithm to increase the sensitivity of pain detection in patients.

Detailed description: Post-surgical pain, if inadequately controlled, has deleterious short and long term consequences for the patient. Although most patients are able to report their pain scores, a minority are unable to do so and assessing their pain can prove to be a challenge for healthcare professionals. In recent years, facial recognition tools have been developed based on the premise that subtle facial variations signifies pain. However, changes in body, and head posture can also represent pain. As such, these tools are with their limitations and are only validated on certain groups of patients, thus may not be sensitive enough to detect pain in post-surgical patients.

The first stage of the study will be conducted on 40 patients presenting for major gynaecological surgery, with the obtained data used to fine tune the algorithm. The patients will be video-taped pre-surgically in the pre-evaluation anaesthetic clinic and post-surgically in the ward. They will be asked to rate their pain scores on the numerical rating scale and fill in questionnaires on their psychological and quality of health status. The pain scores will be correlated with the results obtained from the pain assessment algorithm.

The second phase will improve and enhance the model by (1) analysing body pose to improve the model performance; (2) validating the improved model by recruiting 200 patients undergoing surgical and pain procedures, inpatient and outpatient consultations to collect their videos before and after surgery and inpatient and outpatient pain consultations; (3) integrate the model into a standalone electronic application to improve its usability in both inpatient and outpatient settings.

The third phase will recruit 130 male paediatric patients presenting for circumcision surgery to improve algorithm by i) Adding body posture analysis and other physiological measurement to further improve the performance of our model; ii) Developing our model for use in the pediatric population; and iii) Improving its usability in both clinical and non-clinical settings. Deidentified keypoints will be extracted from the videos to further validate the model.

Eligibility criteria: Inclusion Criteria

Phases 1, 2:

* Patients undergoing surgical and pain procedures, inpatient and outpatient consultations
* American Society of Anaesthesiologists (ASA) physical status 1, 2 or 3 (ASA 1, 2 or 3) patients

Phase 3: Male children undergoing laser circumcision in day surgery

Exclusion Criteria

Phases 1, 2:

* Pregnant patients;
* Medical problems/ medications:

  1. Psychiatric disorders (e.g. anxiety, depression)
  2. Neurological disorders (e.g. Cerebrovascular accident, Parkinson's Disease)
  3. Musculoskeletal limitations that result in gait abnormalities/limitations

Phase 3:

* Developmental delay/ cognitive impairment
* Autism Spectrum disorder
* Attention-deficit/hyperactivity disorder
* Excessive anxiety or requires sedative premed
* Chronic pain conditions and/or medication
* Previous traumatic pain experience

Primary outcomes:
measure: Change in Pain score
timeFrame: Before surgery/procedure/consultation (1 day) and after surgery/procedure/consultation(1-3 days)
measure: Extracted key points from video
timeFrame: Before surgery/procedure/consultation (1 day) and after surgery/procedure/consultation(1-3 days)
Secondary outcomes:
measure: Change in Hospital Anxiety and Depression Scale (HADS) score
timeFrame: Before surgery/procedure/consultation (1 day) and after surgery/procedure/consultation(1-3 days)
measure: EQ-5D-3L score
timeFrame: Before surgery/procedure/consultation (1 day)
measure: Child Pain Anxiety Symptoms Scale (CPASS) score
timeFrame: Before surgery/procedure/consultation (1 day)
Other outcomes:
Locations:
city: Singapore
country: Singapore
facility: KK Women's and Children's Hospital
city: Singapore
country: Singapore
facility: Singapore General Hospital
Study officials:
affiliation: KK Women's and Children's Hospital
name: Ban Leong Sng, FANZCA
role: PRINCIPAL_INVESTIGATOR
References:
citation: Lee M, Kennedy L, Girgensohn A, Wilcox L, Lee JS, Tan CW, Sng BL. Pain Intensity Estimation from Mobile Video Using 2D and 3D Facial Keypoints. arXiv preprint arXiv:2006.12246. 2020 Jun 17.