RESUMO
OBJECTIVE: To develop an alert/verbal/painful/unresponsive (AVPU) scale assessment system based on automated video and speech recognition technology (AVPU-AVSR) that can automatically assess a patient's level of consciousness and evaluate its performance through clinical simulation. METHODS: We developed an AVPU-AVSR system with a whole-body camera, face camera, and microphone. The AVPU-AVSR system automatically extracted essential audiovisual features to assess the AVPU score from the recorded video files. Arm movement, pain stimulus, and eyes-open state were extracted using a rule-based approach using landmarks estimated from pre-trained pose and face estimation models. Verbal stimuli were extracted using a pre-trained speech-recognition model. Simulations of a physician examining the consciousness of 12 simulated patients for 16 simulation scenarios (4 for each of "Alert", "Verbal", "Painful", and "Unresponsive") were conducted under the AVPU-AVSR system. The accuracy, sensitivity, and specificity of the AVPU-AVSR system were assessed. RESULTS: A total of 192 cases with 12 simulated patients were assessed using the AVPU-AVSR system with a multi-class accuracy of 0.95 (95% confidence interval [CI] (0.92-0.98). The sensitivity and specificity (95% CIs) for detecting impaired consciousness were 1.00 (0.97-1.00) and 0.88 (0.75-0.95), respectively. The sensitivity and specificity of each extracted feature ranged from 0.88 to 1.00 and 0.98 to 1.00. CONCLUSIONS: The AVPU-AVSR system showed good accuracy in assessing consciousness levels in a clinical simulation and has the potential to be implemented in clinical practice to automatically assess mental status.
