Artificial Intelligence Automation of Proptosis Measurement: An Indicator for Pediatric Orbital Abscess Surgery
Introduction To evaluate the ability of artificial intelligence (AI) software to quantify proptosis for identifying patients who need surgical drainage. Methods We pursued a retrospective study including 56 subjects with a clinical diagnosis of subperiosteal orbital abscess (SPOA) secondary to sinusitis at a tertiary pediatric hospital from 2002 to 2016. AI computer software was developed to perform 3D visualization and quantitative assessment of proptosis from computed tomography (CT) images acquired at the time of hospital admission. The AI software automatically computed linear and volume metrics of proptosis to provide more practice-consistent and informative measures. Two experienced physicians independently measured proptosis using the interzygomatic line method on axial CT images. The AI software and physician proptosis assessments were evaluated for association with eventual treatment procedures as standalone markers and in combination with the standard predictors. Results To treat the SPOA, 31 of 56 (55%) children underwent surgical intervention, including 18 early surgeries (performed within 24 h of admission), and 25 (45%) were managed medically. The physician measurements of proptosis were strongly correlated (Spearman r = 0.89, 95% CI 0.82–0.93) with 95% limits of agreement of ± 1.8 mm. The AI linear measurement was on average 1.2 mm larger (p = 0.007) and only moderately correlated with the average physicians’ measurements (r = 0.53, 95% CI 0.31–0.69). Increased proptosis of both AI volumetric and linear measurements were moderately predictive of surgery (AUCs of 0.79, 95% CI 0.68–0.91, and 0.78, 95% CI 0.65–0.90, respectively) with the average physician measurement being poorly to fairly predictive (AUC of 0.70, 95% CI 0.56–0.84). The AI proptosis measures were also significantly greater in the early as compared to the late surgery groups (p = 0.02, and p = 0.04, respectively). The surgical and medical groups showed a substantial difference in the abscess volume (p < 0.001). Conclusion AI proptosis measures significantly differed from physician assessments and showed a good overall ability to predict the eventual treatment. The volumetric AI proptosis measurement significantly improved the ability to predict the likelihood of surgery compared to abscess volume alone. Further studies are needed to better characterize and incorporate the AI proptosis measurements for assisting in clinical decision-making..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:12 |
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| Enthalten in: |
Ophthalmology and therapy - 12(2023), 5 vom: 23. Juni, Seite 2479-2491 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Fu, Roxana [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Themen: |
Automation |
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| Anmerkungen: |
© The Author(s) 2023 |
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| doi: |
10.1007/s40123-023-00754-5 |
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SPR052820017 |
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| 520 | |a Introduction To evaluate the ability of artificial intelligence (AI) software to quantify proptosis for identifying patients who need surgical drainage. Methods We pursued a retrospective study including 56 subjects with a clinical diagnosis of subperiosteal orbital abscess (SPOA) secondary to sinusitis at a tertiary pediatric hospital from 2002 to 2016. AI computer software was developed to perform 3D visualization and quantitative assessment of proptosis from computed tomography (CT) images acquired at the time of hospital admission. The AI software automatically computed linear and volume metrics of proptosis to provide more practice-consistent and informative measures. Two experienced physicians independently measured proptosis using the interzygomatic line method on axial CT images. The AI software and physician proptosis assessments were evaluated for association with eventual treatment procedures as standalone markers and in combination with the standard predictors. Results To treat the SPOA, 31 of 56 (55%) children underwent surgical intervention, including 18 early surgeries (performed within 24 h of admission), and 25 (45%) were managed medically. The physician measurements of proptosis were strongly correlated (Spearman r = 0.89, 95% CI 0.82–0.93) with 95% limits of agreement of ± 1.8 mm. The AI linear measurement was on average 1.2 mm larger (p = 0.007) and only moderately correlated with the average physicians’ measurements (r = 0.53, 95% CI 0.31–0.69). Increased proptosis of both AI volumetric and linear measurements were moderately predictive of surgery (AUCs of 0.79, 95% CI 0.68–0.91, and 0.78, 95% CI 0.65–0.90, respectively) with the average physician measurement being poorly to fairly predictive (AUC of 0.70, 95% CI 0.56–0.84). The AI proptosis measures were also significantly greater in the early as compared to the late surgery groups (p = 0.02, and p = 0.04, respectively). The surgical and medical groups showed a substantial difference in the abscess volume (p < 0.001). Conclusion AI proptosis measures significantly differed from physician assessments and showed a good overall ability to predict the eventual treatment. The volumetric AI proptosis measurement significantly improved the ability to predict the likelihood of surgery compared to abscess volume alone. Further studies are needed to better characterize and incorporate the AI proptosis measurements for assisting in clinical decision-making. | ||
| 650 | 4 | |a Orbital cellulitis |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Subperiosteal orbital abscess |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Proptosis |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Computed tomography (CT) |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Automation |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Bandos, Andriy |4 aut | |
| 700 | 1 | |a Leader, Joseph K. |4 aut | |
| 700 | 1 | |a Melachuri, Samyuktha |0 (orcid)0000-0002-0200-7746 |4 aut | |
| 700 | 1 | |a Pradeep, Tejus |4 aut | |
| 700 | 1 | |a Bhatia, Aashim |4 aut | |
| 700 | 1 | |a Narayanan, Srikala |4 aut | |
| 700 | 1 | |a Campbell, Ashley A. |4 aut | |
| 700 | 1 | |a Zhang, Matthew |4 aut | |
| 700 | 1 | |a Sahel, José-Alain |4 aut | |
| 700 | 1 | |a Pu, Jiantao |4 aut | |
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