OBJECTIVES: We investigated whether transesophageal echocardiography (TEE) assisted with a computer-aided diagnostic (CAD) algorithm was superior to TEE in diagnosing left atrial (LA)/left atrial appendage (LAA) thrombi in patients with atrial fibrillation (AF) in a single prospective study. METHODS: Transesophageal echocardiography was performed in patients with AF, and images were reconstructed. Gray level co-occurrence matrix-based features were calculated and then classified using an artificial neural network. The original data and processed images by the CAD system were studied by 5 radiologists independently in a blind manner. The diagnostic performance of each radiologist was evaluated. RESULTS: One hundred thirty patients with AF were investigated. Thirty-one patients (23.9%) had a diagnosis of LA/LAA thrombi. The mean sensitivity ± SD of TEE for LA/LAA thrombi was 0.933 ± 0.027, which was noticeably improved by CAD (0.955 ± 0.021; P < .05). The specificity of TEE was 0.811 ± 0.055, which was markedly lower than that by TEE plus CAD (0.970 ± 0.009; P < .05). The positive predictive value of TEE was low (0.613 ± 0.073) compared to that of TEE plus CAD (0.908 ± 0.027; P < .001), whereas the negative predictive values were comparable for TEE, CAD, and TEE plus CAD. Diagnosis of an LA/LAA thrombus by TEE plus CAD had a higher accuracy rate (0.966 ± 0.011) than that by TEE (0.840 ± 0.047; P < .01). The mean area under the receiver operating characteristic curve (Az) for TEE was 0.834 ± 0.009 (95% confidence interval [CI], 0.815-0.852), which was markedly lower than the Az for TEE plus CAD (0.932 ± 0.005; 95% CI, 0.921-0.943). The use of CAD significantly improved the Az values for all 5 radiologists (P < .001). CONCLUSIONS: The CAD algorithm significantly improves the diagnostic accuracy of TEE for LA/LAA thrombi in patients with AF.
OBJECTIVES: We investigated whether transesophageal echocardiography (TEE) assisted with a computer-aided diagnostic (CAD) algorithm was superior to TEE in diagnosing left atrial (LA)/left atrial appendage (LAA) thrombi in patients with atrial fibrillation (AF) in a single prospective study. METHODS: Transesophageal echocardiography was performed in patients with AF, and images were reconstructed. Gray level co-occurrence matrix-based features were calculated and then classified using an artificial neural network. The original data and processed images by the CAD system were studied by 5 radiologists independently in a blind manner. The diagnostic performance of each radiologist was evaluated. RESULTS: One hundred thirty patients with AF were investigated. Thirty-one patients (23.9%) had a diagnosis of LA/LAA thrombi. The mean sensitivity ± SD of TEE for LA/LAA thrombi was 0.933 ± 0.027, which was noticeably improved by CAD (0.955 ± 0.021; P < .05). The specificity of TEE was 0.811 ± 0.055, which was markedly lower than that by TEE plus CAD (0.970 ± 0.009; P < .05). The positive predictive value of TEE was low (0.613 ± 0.073) compared to that of TEE plus CAD (0.908 ± 0.027; P < .001), whereas the negative predictive values were comparable for TEE, CAD, and TEE plus CAD. Diagnosis of an LA/LAA thrombus by TEE plus CAD had a higher accuracy rate (0.966 ± 0.011) than that by TEE (0.840 ± 0.047; P < .01). The mean area under the receiver operating characteristic curve (Az) for TEE was 0.834 ± 0.009 (95% confidence interval [CI], 0.815-0.852), which was markedly lower than the Az for TEE plus CAD (0.932 ± 0.005; 95% CI, 0.921-0.943). The use of CAD significantly improved the Az values for all 5 radiologists (P < .001). CONCLUSIONS: The CAD algorithm significantly improves the diagnostic accuracy of TEE for LA/LAA thrombi in patients with AF.
Authors: Uzoma N Ibebuogu; Joseph H Schafer; Mark J Schwade; Jennifer L Waller; Gyanendra K Sharma; Vincent J B Robinson Journal: Echocardiography Date: 2019-12-19 Impact factor: 1.724
Authors: Baptiste Vasey; Stephan Ursprung; Benjamin Beddoe; Elliott H Taylor; Neale Marlow; Nicole Bilbro; Peter Watkinson; Peter McCulloch Journal: JAMA Netw Open Date: 2021-03-01
Authors: Shane M Summers; Eric J Chin; Brit J Long; Ronald D Grisell; John G Knight; Kurt W Grathwohl; John L Ritter; Jeffrey D Morgan; Jose Salinas; Lorne H Blackbourne Journal: West J Emerg Med Date: 2016-03-02