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Background The diagnosis of functional constipation (FC) is based on the Rome criteria. The last edition of the criteria (Rome IV) for infants and toddlers modified the criteria to differentiate toilet‐trained (TT) and non‐toilet‐trained (NTT) children. These changes have not been validated. We aimed to understand the impact of adding toilet training to the diagnostic criteria and to assess the prevalence of FC. Methods Parents of infants and toddlers from six outpatient clinics (four public, two private) located in three geographically dispersed cities in Colombia completed validated questionnaires to diagnose functional gastrointestinal disorders according to Spanish version of Rome IV criteria (QPGS‐IV). Results A total of 1334 children (24.4 months ±15.0) participated: 482 (36%) TT and 852 (64%) NTT. The prevalence of FC was 21.1%. The prevalence increased with age, 0‐1 years 7.7%; 2 years 18.2%; 3 years 23.7%; and 4 years 37.2%. TT vs NTT for FC 41.9% vs 9.3%, respectively (OR 7.06, 95% CI 5.26‐9.47, P < .0001). TT more likely to report ≥ 3 criteria (OR = 2.43, 95% CI 1.41‐4.21, P = .0015). 18.3% of TT had episodes of fecal incontinence that met the frequency required by Rome for FC (≤1 episode/week). However, 87.1% had fecal incontinence less often. 7.4% of them characterized as large quantity. Conclusion We found no changes in the prevalence of FC using the Rome IV criteria vs Rome III. TT children are more likely to have FC. Study suggests that changes in Rome IV criteria were potentially clinically relevant and to have adequate face validity. Future studies should confirm our findings.

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Background We have previously reported the use of Artificial Intelligence (AI) guided EKG analysis for detection of ST-Elevation Myocardial Infarction (STEMI). To demonstrate the diagnostic value of our algorithm, we compared AI predictions with reports that were confirmed as STEMI. Purpose To demonstrate the absolute proficiency of AI for detecting STEMI in a standard12-lead EKG. Methods An observational, retrospective, case-control study. Sample: 5,087 EKG records, including 2,543 confirmed STEMI cases obtained via feedback from health centers following appropriate patient management (thrombolysis, primary Percutaneous Coronary Intervention (PCI), pharmacoinvasive therapy or coronary artery bypass surgery). Records excluded patient and medical information. The sample was derived from the International Telemedical Systems (ITMS) database. LUMENGT-AI Algorithm was employed. Preprocessing: detection of QRS complexes by wavelet system, segmentation of each EKG into individual heartbeats (53,667 total beats) with fixed window of 0.4s to the left and 0.9s to the right of main QRS; Classification: A 1-D convolutional neural network was implemented, “STEMI” and “Not-STEMI” classes were considered for each heartbeat, individual probabilities were aggregated to generate the final label for each record. Training & Testing: 90% and 10% of the sample were used, respectively. Experiments: Intel PC i7 8750H processor at 2.21GHz, 16GB RAM, Windows 10 OS with NVIDIA GTX 1070 GPU, 8GB RAM. Results The model yielded an accuracy of 97.2%, a sensitivity of 95.8%, and a specificity of 98.5%. Conclusion(s) Our AI-based algorithm can reliably diagnose STEMI and will preclude the role of a cardiologist for screening and diagnosis, especially in the pre-hospital setting.