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Diagnosis using voice is non-invasive and can be implemented through various voice recording devices; therefore, it can be used as a screening or diagnostic assistant tool for laryngeal voice disease to help clinicians. The development of artificial intelligence algorithms, such as machine learning, led by the latest deep learning technology, began with a binary classification that distinguishes normal and pathological voices; consequently, it has contributed in improving the accuracy of multi-classification to classify various types of pathological voices. However, no conclusions that can be applied in the clinical field have yet been achieved. Most studies on pathological speech classification using speech have used the continuous short vowel /ah/, which is relatively easier than using continuous or running speech. However, continuous speech has the potential to derive more accurate results as additional information can be obtained from the change in the voice signal over time. In this review, explanations of terms related to artificial intelligence research, and the latest trends in machine learning and deep learning algorithms are reviewed; furthermore, the latest research results and limitations are introduced to provide future directions for researchers.

Background and Objectives The quality of life after thyroidectomy, such as voice change, is considered to be as important as control of the disease. In this study, we aimed to evaluate changes in both subjective and objective voice parameters after thyroidectomy resulting in normal morbidity of the vocal cords.Materials and Method In this prospective cohort study, 204 patients who underwent thyroidectomy with or without central neck dissection at a single referral center from Feb 2015 to Aug 2016 were enrolled. All patients underwent prospective voice evaluations including both subjective and objective assessments preoperatively and then at 2 weeks, 3, 6, and 12 months postoperatively. Temporal changes of the voice parameters were analyzed.Results Values of the subjective assessment tool worsened during the early postoperative follow-up period and did not recover to the preoperative values at 12 months postoperatively. The maximal phonation time gradually decreased, whereas most objective parameters, including maximal vocal pitch (MVP), reached preoperative values at 3–6 months postoperatively. The initial decrease in MVP was significantly greater in patients undergoing total thyroidectomy, and their MVP recovery time was faster than that of patients undergoing lobectomy (p=0.001). Patients whose external branch of the superior laryngeal nerve was confirmed intact by electroidentification showed no difference in recovery speed compared with patients without electroindentification (p=0.102), although the initial decrease in MVP was lower with electroidentification.Conclusion Subjective assessment in voice quality and maximal phonation time after thyroidectomy did not show recovery to preoperative values. Aggravation of MVP was associated with surgical extent and electroidentification.