| Autor: |
Joshua J, Levy, Christopher M, Navas, Joan A, Chandra, Brock C, Christensen, Louis J, Vaickus, Michael, Curley, William D, Chey, Jason R, Baker, Eric D, Shah |
| Rok vydání: |
2022 |
| Zdroj: |
Digestive diseases and sciences. |
| ISSN: |
1573-2568 |
| Popis: |
We developed a deep learning algorithm to evaluate defecatory patterns to identify dyssynergic defecation using 3-dimensional high definition anal manometry (3D-HDAM).We developed a 3D-HDAM deep learning algorithm to evaluate for dyssynergia.Spatial-temporal data were extracted from consecutive 3D-HDAM studies performed between 2018 and 2020 at Dartmouth-Hitchcock Health. The technical procedure and gold standard definition of dyssynergia were based on the London consensus, adapted to the needs of 3D-HDAM technology. Three machine learning models were generated: (1) traditional machine learning informed by conventional anorectal function metrics, (2) deep learning, and (3) a hybrid approach. Diagnostic accuracy was evaluated using bootstrap sampling to calculate area-under-the-curve (AUC). To evaluate overfitting, models were validated by adding 502 simulated defecation maneuvers with diagnostic ambiguity.302 3D-HDAM studies representing 1208 simulated defecation maneuvers were included (average age 55.2 years; 80.5% women). The deep learning model had comparable diagnostic accuracy [AUC 0.91 (95% confidence interval 0.89-0.93)] to traditional [AUC 0.93(0.92-0.95)] and hybrid [AUC 0.96(0.94-0.97)] predictive models in training cohorts. However, the deep learning model handled ambiguous tests more cautiously than other models; the deep learning model was more likely to designate an ambiguous test as inconclusive [odds ratio 4.21(2.78-6.38)] versus traditional/hybrid approaches.Deep learning is capable of considering complex spatial-temporal information on 3D-HDAM technology. Future studies are needed to evaluate the clinical context of these preliminary findings. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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