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Thermal imaging (Heat Signature) to predict wound infection of paediatric surgical wounds

Infrared signature of paediatric surgical wound: thermographic profiles and early-stage test- accuracy study to predict the surgical site infection and development of deep learning based artificial intelligence technique for automatic image segmentation

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CTRI
Registry ID
CTRI/2022/03/041512
Enrollment
400
Registered
2022-03-31
Start date
Unknown
Completion date
Unknown
Last updated
2024-04-29

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Health Condition 1: K659- Peritonitis, unspecified

Interventions

Intervention1: NIl (observational study): NIl (observational study) Intervention2: No intervention is planned Thermographic images and Digital still images of laparotomy wounds will be captured in pre

Sponsors

ICMR Adhoc proposal call
Lead Sponsor

Eligibility

Inclusion criteria

Inclusion criteria: All children beyond neonatal period and till 14 years of age undergoing surgery of the abdomen (elective or emergency procedure) in which the GI tract (midgut and hind gut) is opened during the operation.

Exclusion criteria

Exclusion criteria: Re-do cases 2. GI tract (midgut and hind gut) not opened during the operation. 3. Patients undergoing minimally invasive surgery 4. Primary skin closure not done. 5. Stoma sited within the wound. 6. Those who require negative pressure wound therapy (NPWT) dressings. 7. Patients having organ/space SSI only.

Design outcomes

Primary

MeasureTime frame
Assess the early-stage performance and test-accuracy of the parameters obtained through the pilot study of the infrared thermography signatures as possible predictor of later SSI. 2) To explore concordance in visual wound assessment between surgeons in a tertiary medical setting 3) To characterize the profile of the abdomen and surgical wound during the SSI surveillance period as defined by CDC (till 30 days after surgery), by the technique of temporal infrared imaging. 4) Development of deep learning-based technique for automatic Region of Interest (SSI Profile) Segmentation. 5) Development of deep learning-based technique for detection of automatic signs from inflamed region of interests (as mentioned in objective 4) and prediction of its severity.Timepoint: 30 days—

Secondary

MeasureTime frame
4) Development of deep learning-based technique for automatic Region of Interest (SSI Profile) Segmentation. 5) Development of deep learning-based technique for detection of automatic signs from inflamed region of interests (as mentioned in objective 4) and prediction of its severity.Timepoint: 30 to 36 months after beginning of trial—

Countries

India

Contacts

Public ContactDr Anjan Kumar Dhua

AIIMS Delhi

dhuaanjan@gmail.com01126593309

Outcome results

None listed

Source: CTRI (via WHO ICTRP) · Data processed: Feb 4, 2026