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| Name | Class |
|---|---|
| KK Women's and Children's Hospital | OTHER_GOV |
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An exploratory study to explore the possibility of using computer vision algorithms to estimate a child's length using images taken by a healthcare professional or parents.
This is an exploratory, observational, pilot study that aims to evaluate the performance of a Length Artificial Intelligence (LAI) algorithm in a real world setting. Images will be collected by parents or healthcare professionals, together with physical length measurements. This data will be used to evaluate the accuracy of the algorithm and to explore potential improvements. Data on the acceptance and experience of the using the algorithm will be collected for improvements.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Children aged 0-18 months of age | Children aged 0-18 months of age with no structural abnormalities of the lower limbs or orthopedic conditions |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Physical length measurement | Other | Physical length will be measured and images will be collected for AI to estimate the length |
|
| Measure | Description | Time Frame |
|---|---|---|
| Accuracy of the length AI | Accuracy of the length AI in a clinic and in a home setting, derived from:
| 2 days |
| Measure | Description | Time Frame |
|---|---|---|
| Investigator's assessment on collection of images | Investigator's assessment on the ease of collecting the images [Very Easy, Easy, Normal, Difficult, Very Difficult] | 2 days |
| Parental acceptability of the length AI |
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Inclusion Criteria:
Exclusion Criteria:
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Children between 0-18 months of age with no physical deformities.
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| KK Women's and Children's Hospital | Singapore | 229899 | Singapore |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 41365661 | Derived | Chan D, Chua MC, Hadimaja M, Mukherjee S, Wong J, Yap F. Artificial intelligence-driven anthropometric assessment for young children: evaluating the accuracy and practicality of a digital image-based length and weight prediction tool. BMJ Health Care Inform. 2025 Dec 9;32(1):e101540. doi: 10.1136/bmjhci-2025-101540. | |
| 39576977 | Derived |
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| ID | Term |
|---|---|
| D006130 | Growth Disorders |
| ID | Term |
|---|---|
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
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Parental acceptability of length AI assessed via the study questionnaire [Very useful, useful, neutral, not useful, very not useful]
| 2 days |
| Investigators' (or delegates) acceptability of length AI | Investigators' (or delegates) likelihood of using the length AI assessed via the study questionnaire [Very Likely, Likely, Neutral, Unlikely, Very Unlikely] | 2 days |
| Chua MC, Hadimaja M, Wong J, Mukherjee SS, Foussat A, Chan D, Nandal U, Yap F. Exploring the Use of a Length AI Algorithm to Estimate Children's Length from Smartphone Images in a Real-World Setting: Algorithm Development and Usability Study. JMIR Pediatr Parent. 2024 Nov 22;7:e59564. doi: 10.2196/59564. |