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Smartphone Integration of Artificial Intelligence for Automated Plagiocephaly Diagnosis
Ayden Watt1, James Lee2, Matthew Toews3
1From the Department of Experimental Surgery, McGill University, Montreal, Canada.
Insights
A smartphone AI tool accurately diagnosed positional plagiocephaly in infants, showing 85.39% accuracy. This technology aids in early detection and monitoring of the common pediatric condition.
Area of Science:
- Pediatric medicine
- Medical technology
- Artificial intelligence in healthcare
Background:
- Positional plagiocephaly affects approximately 40% of infants under 12 months.
- Early diagnosis and treatment are crucial for satisfactory cosmetic outcomes.
- Improved diagnostic tools are needed for timely intervention.
Purpose of the Study:
- To evaluate the diagnostic capability of a smartphone-based artificial intelligence (AI) tool for positional plagiocephaly.
- To determine the accuracy of AI in identifying the presence and severity of positional plagiocephaly in infants.
Main Methods:
- A prospective validation study was conducted involving 89 infants aged 0-12 months.
- Participants were recruited from a tertiary care center's newborn nursery and craniofacial surgery clinic.
- AI diagnosis was validated against standard clinical examination, assessing presence and severity.
Main Results:
- The AI tool achieved a diagnostic accuracy of 85.39% compared to clinical examination.
- Sensitivity was 87.50% and specificity was 83.67%.
- The F1-score was 84.34%, indicating robust performance.
Conclusions:
- Smartphone-based AI accurately diagnoses positional plagiocephaly in a clinical setting.
- This technology can assist in guiding specialist consultations.
- It offers potential for longitudinal, quantitative monitoring of infant cranial shape.
Abstract:
Positional plagiocephaly is a pediatric condition with important cosmetic implications affecting ∼40% of infants under 12 months of age. Early diagnosis and treatment initiation is imperative in achieving satisfactory outcomes; improved diagnostic modalities are needed to support this goal. This study aimed to determine whether a smartphone-based artificial intelligence tool could diagnose positional plagiocephaly.
Methods:
A prospective validation study was conducted at a large tertiary care center with two recruitment sites: (1) newborn nursery, (2) pediatric craniofacial surgery clinic. Eligible children were aged 0-12 months with no history of hydrocephalus, intracranial tumors, intracranial hemorrhage, intracranial hardware, or prior craniofacial surgery. Successful artificial intelligence diagnosis required identification of the presence and severity of positional plagiocephaly.
Results:
A total of 89 infants were prospectively enrolled from the craniofacial surgery clinic (n = 25, 17 male infants [68%], eight female infants [32%], mean age 8.44 months) and newborn nursery (n = 64, 29 male infants [45%], 25 female infants [39%], mean age 0 months). The model obtained a diagnostic accuracy of 85.39% compared with a standard clinical examination with a disease prevalence of 48%. Sensitivity was 87.50% [95% CI, 75.94-98.42] with a specificity of 83.67% [95% CI, 72.35-94.99]. Precision was 81.40%, while likelihood ratios (positive and negative) were 5.36 and 0.15, respectively. The F1-score was 84.34%.
Conclusions:
The smartphone-based artificial intelligence algorithm accurately diagnosed positional plagiocephaly in a clinical environment. This technology may provide value by helping guide specialist consultation and enabling longitudinal quantitative monitoring of cranial shape.
