Objective Auditory brainstem response(ABR)is an objective electrophysiological test that evalu-ates a patient's hearing,and the ABR is interpreted primarily by recognizing the latency of waves Ⅰ through Ⅴ.The latency of wave Ⅰ to wave Ⅴ has important reference value for clinicians to judge the status of patients' auditory neu-ral pathways,so accurate interpretation of the results of this examination is particularly important.However,the a-nalysis of the result of the ABR entirely depends on the auditory teacher's ability to recognize and interpret the hu-man eye,which makes it difficult for inexperienced auditory teachers to interpret ABR waveforms.In order to solve this problem,many scholars proposed to use machine learning(ML)model to objectively interpret ABR results.ML has strong learning ability and can independently complete an objective and accurate interpretation of ABR re-sults,which brings the dawn for the objective and accurate interpretation of ABR results.

Objective Auditory brainstem response(ABR)is an objective electrophysiological test that evalu-ates a patient's hearing,and the ABR is interpreted primarily by recognizing the latency of waves Ⅰ through Ⅴ.The latency of wave Ⅰ to wave Ⅴ has important reference value for clinicians to judge the status of patients' auditory neu-ral pathways,so accurate interpretation of the results of this examination is particularly important.However,the a-nalysis of the result of the ABR entirely depends on the auditory teacher's ability to recognize and interpret the hu-man eye,which makes it difficult for inexperienced auditory teachers to interpret ABR waveforms.In order to solve this problem,many scholars proposed to use machine learning(ML)model to objectively interpret ABR results.ML has strong learning ability and can independently complete an objective and accurate interpretation of ABR re-sults,which brings the dawn for the objective and accurate interpretation of ABR results.