The accuracy of an Online Sequential Extreme Learning Machine in detecting voice pathology using the Malaysian Voice Pathology Database
Abstract Background A multidimensional voice quality assessment is recommended for all patients with dysphonia, which requires a patient visit to the otolaryngology clinic. The aim of this study was to determine the accuracy of an online artificial intelligence classifier, the Online Sequential Extr...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2023-09-01
|
| Series: | Journal of Otolaryngology - Head and Neck Surgery |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40463-023-00661-6 |
| _version_ | 1826840455072972800 |
|---|---|
| author | Nur Ain Nabila Za’im Fahad Taha AL-Dhief Mawaddah Azman Majid Razaq Mohamed Alsemawi Nurul Mu′azzah Abdul Latiff Marina Mat Baki |
| author_facet | Nur Ain Nabila Za’im Fahad Taha AL-Dhief Mawaddah Azman Majid Razaq Mohamed Alsemawi Nurul Mu′azzah Abdul Latiff Marina Mat Baki |
| author_sort | Nur Ain Nabila Za’im |
| collection | DOAJ |
| description | Abstract Background A multidimensional voice quality assessment is recommended for all patients with dysphonia, which requires a patient visit to the otolaryngology clinic. The aim of this study was to determine the accuracy of an online artificial intelligence classifier, the Online Sequential Extreme Learning Machine (OSELM), in detecting voice pathology. In this study, a Malaysian Voice Pathology Database (MVPD), which is the first Malaysian voice database, was created and tested. Methods The study included 382 participants (252 normal voices and 130 dysphonic voices) in the proposed database MVPD. Complete data were obtained for both groups, including voice samples, laryngostroboscopy videos, and acoustic analysis. The diagnoses of patients with dysphonia were obtained. Each voice sample was anonymized using a code that was specific to each individual and stored in the MVPD. These voice samples were used to train and test the proposed OSELM algorithm. The performance of OSELM was evaluated and compared with other classifiers in terms of the accuracy, sensitivity, and specificity of detecting and differentiating dysphonic voices. Results The accuracy, sensitivity, and specificity of OSELM in detecting normal and dysphonic voices were 90%, 98%, and 73%, respectively. The classifier differentiated between structural and non-structural vocal fold pathology with accuracy, sensitivity, and specificity of 84%, 89%, and 88%, respectively, while it differentiated between malignant and benign lesions with an accuracy, sensitivity, and specificity of 92%, 100%, and 58%, respectively. Compared to other classifiers, OSELM showed superior accuracy and sensitivity in detecting dysphonic voices, differentiating structural versus non-structural vocal fold pathology, and between malignant and benign voice pathology. Conclusion The OSELM algorithm exhibited the highest accuracy and sensitivity compared to other classifiers in detecting voice pathology, classifying between malignant and benign lesions, and differentiating between structural and non-structural vocal pathology. Hence, it is a promising artificial intelligence that supports an online application to be used as a screening tool to encourage people to seek medical consultation early for a definitive diagnosis of voice pathology. |
| first_indexed | 2024-03-11T22:10:14Z |
| format | Article |
| id | doaj.art-c6d4bb6a31d248969694f064d714baf5 |
| institution | Directory Open Access Journal |
| issn | 1916-0216 |
| language | English |
| last_indexed | 2025-02-16T12:41:56Z |
| publishDate | 2023-09-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Journal of Otolaryngology - Head and Neck Surgery |
| spelling | doaj.art-c6d4bb6a31d248969694f064d714baf52025-02-02T23:16:50ZengSAGE PublishingJournal of Otolaryngology - Head and Neck Surgery1916-02162023-09-0152111110.1186/s40463-023-00661-6The accuracy of an Online Sequential Extreme Learning Machine in detecting voice pathology using the Malaysian Voice Pathology DatabaseNur Ain Nabila Za’im0Fahad Taha AL-Dhief1Mawaddah Azman2Majid Razaq Mohamed Alsemawi3Nurul Mu′azzah Abdul Latiff4Marina Mat Baki5Department of Otorhinolaryngology-Head and Neck Surgery, Faculty of Medicine, Universiti Kebangsaan Malaysia (UKM), Hospital Canselor Tuanku MuhrizFaculty of Electrical Engineering, Universiti Teknologi Malaysia (UTM)Department of Otorhinolaryngology-Head and Neck Surgery, Faculty of Medicine, Universiti Kebangsaan Malaysia (UKM), Hospital Canselor Tuanku MuhrizCollege of Information Technology, Imam Ja’afar Al‐Sadiq UniversityFaculty of Electrical Engineering, Universiti Teknologi Malaysia (UTM)Department of Otorhinolaryngology-Head and Neck Surgery, Faculty of Medicine, Universiti Kebangsaan Malaysia (UKM), Hospital Canselor Tuanku MuhrizAbstract Background A multidimensional voice quality assessment is recommended for all patients with dysphonia, which requires a patient visit to the otolaryngology clinic. The aim of this study was to determine the accuracy of an online artificial intelligence classifier, the Online Sequential Extreme Learning Machine (OSELM), in detecting voice pathology. In this study, a Malaysian Voice Pathology Database (MVPD), which is the first Malaysian voice database, was created and tested. Methods The study included 382 participants (252 normal voices and 130 dysphonic voices) in the proposed database MVPD. Complete data were obtained for both groups, including voice samples, laryngostroboscopy videos, and acoustic analysis. The diagnoses of patients with dysphonia were obtained. Each voice sample was anonymized using a code that was specific to each individual and stored in the MVPD. These voice samples were used to train and test the proposed OSELM algorithm. The performance of OSELM was evaluated and compared with other classifiers in terms of the accuracy, sensitivity, and specificity of detecting and differentiating dysphonic voices. Results The accuracy, sensitivity, and specificity of OSELM in detecting normal and dysphonic voices were 90%, 98%, and 73%, respectively. The classifier differentiated between structural and non-structural vocal fold pathology with accuracy, sensitivity, and specificity of 84%, 89%, and 88%, respectively, while it differentiated between malignant and benign lesions with an accuracy, sensitivity, and specificity of 92%, 100%, and 58%, respectively. Compared to other classifiers, OSELM showed superior accuracy and sensitivity in detecting dysphonic voices, differentiating structural versus non-structural vocal fold pathology, and between malignant and benign voice pathology. Conclusion The OSELM algorithm exhibited the highest accuracy and sensitivity compared to other classifiers in detecting voice pathology, classifying between malignant and benign lesions, and differentiating between structural and non-structural vocal pathology. Hence, it is a promising artificial intelligence that supports an online application to be used as a screening tool to encourage people to seek medical consultation early for a definitive diagnosis of voice pathology.https://doi.org/10.1186/s40463-023-00661-6Online Sequential Extreme Learning MachineAccuracySensitivitySpecificityDysphoniaVoice database |
| spellingShingle | Nur Ain Nabila Za’im Fahad Taha AL-Dhief Mawaddah Azman Majid Razaq Mohamed Alsemawi Nurul Mu′azzah Abdul Latiff Marina Mat Baki The accuracy of an Online Sequential Extreme Learning Machine in detecting voice pathology using the Malaysian Voice Pathology Database Journal of Otolaryngology - Head and Neck Surgery Online Sequential Extreme Learning Machine Accuracy Sensitivity Specificity Dysphonia Voice database |
| title | The accuracy of an Online Sequential Extreme Learning Machine in detecting voice pathology using the Malaysian Voice Pathology Database |
| title_full | The accuracy of an Online Sequential Extreme Learning Machine in detecting voice pathology using the Malaysian Voice Pathology Database |
| title_fullStr | The accuracy of an Online Sequential Extreme Learning Machine in detecting voice pathology using the Malaysian Voice Pathology Database |
| title_full_unstemmed | The accuracy of an Online Sequential Extreme Learning Machine in detecting voice pathology using the Malaysian Voice Pathology Database |
| title_short | The accuracy of an Online Sequential Extreme Learning Machine in detecting voice pathology using the Malaysian Voice Pathology Database |
| title_sort | accuracy of an online sequential extreme learning machine in detecting voice pathology using the malaysian voice pathology database |
| topic | Online Sequential Extreme Learning Machine Accuracy Sensitivity Specificity Dysphonia Voice database |
| url | https://doi.org/10.1186/s40463-023-00661-6 |
| work_keys_str_mv | AT nurainnabilazaim theaccuracyofanonlinesequentialextremelearningmachineindetectingvoicepathologyusingthemalaysianvoicepathologydatabase AT fahadtahaaldhief theaccuracyofanonlinesequentialextremelearningmachineindetectingvoicepathologyusingthemalaysianvoicepathologydatabase AT mawaddahazman theaccuracyofanonlinesequentialextremelearningmachineindetectingvoicepathologyusingthemalaysianvoicepathologydatabase AT majidrazaqmohamedalsemawi theaccuracyofanonlinesequentialextremelearningmachineindetectingvoicepathologyusingthemalaysianvoicepathologydatabase AT nurulmuazzahabdullatiff theaccuracyofanonlinesequentialextremelearningmachineindetectingvoicepathologyusingthemalaysianvoicepathologydatabase AT marinamatbaki theaccuracyofanonlinesequentialextremelearningmachineindetectingvoicepathologyusingthemalaysianvoicepathologydatabase AT nurainnabilazaim accuracyofanonlinesequentialextremelearningmachineindetectingvoicepathologyusingthemalaysianvoicepathologydatabase AT fahadtahaaldhief accuracyofanonlinesequentialextremelearningmachineindetectingvoicepathologyusingthemalaysianvoicepathologydatabase AT mawaddahazman accuracyofanonlinesequentialextremelearningmachineindetectingvoicepathologyusingthemalaysianvoicepathologydatabase AT majidrazaqmohamedalsemawi accuracyofanonlinesequentialextremelearningmachineindetectingvoicepathologyusingthemalaysianvoicepathologydatabase AT nurulmuazzahabdullatiff accuracyofanonlinesequentialextremelearningmachineindetectingvoicepathologyusingthemalaysianvoicepathologydatabase AT marinamatbaki accuracyofanonlinesequentialextremelearningmachineindetectingvoicepathologyusingthemalaysianvoicepathologydatabase |