Smartphone Integration of Artificial Intelligence for Automated Plagiocephaly Diagnosis
Background:. 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 g...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wolters Kluwer
2023-05-01
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| Series: | Plastic and Reconstructive Surgery, Global Open |
| Online Access: | http://journals.lww.com/prsgo/fulltext/10.1097/GOX.0000000000004985 |
| _version_ | 1797817483507269632 |
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| author | Ayden Watt, MSc James Lee, MD, MSc Matthew Toews, PhD, MEng Mirko S. Gilardino, MD, MSc, FRCSC, FACS |
| author_facet | Ayden Watt, MSc James Lee, MD, MSc Matthew Toews, PhD, MEng Mirko S. Gilardino, MD, MSc, FRCSC, FACS |
| author_sort | Ayden Watt, MSc |
| collection | DOAJ |
| description | Background:. 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. |
| first_indexed | 2024-03-13T08:55:09Z |
| format | Article |
| id | doaj.art-2f47bb0ccb3b4d36b8c5224109501be3 |
| institution | Directory Open Access Journal |
| issn | 2169-7574 |
| language | English |
| last_indexed | 2024-03-13T08:55:09Z |
| publishDate | 2023-05-01 |
| publisher | Wolters Kluwer |
| record_format | Article |
| series | Plastic and Reconstructive Surgery, Global Open |
| spelling | doaj.art-2f47bb0ccb3b4d36b8c5224109501be32023-05-29T03:33:14ZengWolters KluwerPlastic and Reconstructive Surgery, Global Open2169-75742023-05-01115e498510.1097/GOX.0000000000004985202305000-00012Smartphone Integration of Artificial Intelligence for Automated Plagiocephaly DiagnosisAyden Watt, MSc0James Lee, MD, MSc1Matthew Toews, PhD, MEng2Mirko S. Gilardino, MD, MSc, FRCSC, FACS3From the * Department of Experimental Surgery, McGill University, Montreal, Canada† Division of Plastic and Reconstructive Surgery, McGill University Health Center, Montreal, Canada‡ École de Technologie Supérieure, Department of Systems Engineering, Montréal, Canada.† Division of Plastic and Reconstructive Surgery, McGill University Health Center, Montreal, CanadaBackground:. 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.http://journals.lww.com/prsgo/fulltext/10.1097/GOX.0000000000004985 |
| spellingShingle | Ayden Watt, MSc James Lee, MD, MSc Matthew Toews, PhD, MEng Mirko S. Gilardino, MD, MSc, FRCSC, FACS Smartphone Integration of Artificial Intelligence for Automated Plagiocephaly Diagnosis Plastic and Reconstructive Surgery, Global Open |
| title | Smartphone Integration of Artificial Intelligence for Automated Plagiocephaly Diagnosis |
| title_full | Smartphone Integration of Artificial Intelligence for Automated Plagiocephaly Diagnosis |
| title_fullStr | Smartphone Integration of Artificial Intelligence for Automated Plagiocephaly Diagnosis |
| title_full_unstemmed | Smartphone Integration of Artificial Intelligence for Automated Plagiocephaly Diagnosis |
| title_short | Smartphone Integration of Artificial Intelligence for Automated Plagiocephaly Diagnosis |
| title_sort | smartphone integration of artificial intelligence for automated plagiocephaly diagnosis |
| url | http://journals.lww.com/prsgo/fulltext/10.1097/GOX.0000000000004985 |
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