Checking for voice disorders without clinical intervention: The Greek and global VHI thresholds for voice disordered patients

Tafiadis, Dionysios; Chronopoulos, Spyridon K.; Helidoni, Meropi E.; Kosma, Evangelia I.; Voniati, Louiza; Papadopoulos, Periklis; Murry, Thomas; Ziavra, Nafsika; Velegrakis, George A.

doi:10.1038/s41598-019-45758-z

Cited by 17 publications

(12 citation statements)

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“…In a review paper of several translations of VHI in different languages, the values between 12 and 20 were found as cut-off points to distinguish between subjects with normal and pathological voices. The authors suggest that any VHI value above 18 is a sign of strong possibility of a clinical voice problem 13. According to this recommendation, we decided to choose 19 points as the cut-off point.…”

Section: Discussionmentioning

confidence: 99%

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Factors affecting voice quality in early glottic cancer before and after radiotherapy

Mekis

Strojan

Boltežar

2019

Radiology and Oncology

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BackgroundRadiotherapy (RT) is a successful mode of treatment for early glottic cancer. The aim of the study was to assess voice quality both before and 3 months after successful RT using multimodal methods while also identifying the factors affecting it.Patients and methodsIn 50 patients with T1 glottic carcinoma, the subjective (patients’ assessment of voice quality [VAS], Voice Handicap Index [VHI] questionnaire, phoniatricians’ assessment using the grade/roughness/ breathiness [GRB] scale), and objective assessments (fundamental laryngeal frequency [F0], jitter, shimmer, maximum phonation time [MPT]) of voice quality were performed before RT and 3 months post-RT. The data on gender, age, extent of the tumors, biopsy types, smoking, local findings, and RT were obtained from the medical documentation.ResultsThree months after the treatment, VAS, VHI, G and R scores, F0, and MPT significantly improved in comparison with their assessment prior to treatment. Before the treatment, the involvement of the anterior commissure significantly deteriorated jitter (p = 0.044) and the involvement of both vocal folds deteriorated jitter (p = 0.003) and shimmer (p = 0.007). After the RT, F0 was significantly higher in the patients with repeated biopsy than in the others (p = 0.047). In patients with post-RT changes, the B score was significantly higher than in those without post-RT changes (p = 0.029).ConclusionsVoice quality already significantly improved three months after the treatment of glottic cancer. The main reason for the decreased voice quality prior to treatment is the tumor’s extent. Post-RT laryngeal changes and repeated biopsies caused more scarring on vocal folds adversely influencing voice quality after the treatment.

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Section: Discussionmentioning

confidence: 99%

“…They also completed the Voice Handicap Index questionnaire (VHI), which is showing the influence of the patients’ voice on their lives 12. A score above 18 was considered characteristic for a clinical voice disorder 13…”

Section: Evaluation Of Laryngeal Function and Voice Qualitymentioning

confidence: 99%

Factors affecting voice quality in early glottic cancer before and after radiotherapy

Mekis

Strojan

Boltežar

2019

Radiology and Oncology

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“…Typical signals derived from the glottis segmentation are the glottal area waveform (GAW) 36 , the vocal fold trajectories 37 and the phonovibrogram 38 . Parameters computed from these signals bear the promise of a higher objectivity than many of the purely subjective metrics still being employed in the clinical routine 36,[39][40][41] . Figure 1 shows an exemplary HSV frame and the corresponding segmentation.…”

Section: Videoendoscopy and Glottis Segmentation Several Imaging Tecmentioning

confidence: 99%

BAGLS, a multihospital Benchmark for Automatic Glottis Segmentation

et al. 2020

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Laryngeal videoendoscopy is one of the main tools in clinical examinations for voice disorders and voice research. Using high-speed videoendoscopy, it is possible to fully capture the vocal fold oscillations, however, processing the recordings typically involves a time-consuming segmentation of the glottal area by trained experts. Even though automatic methods have been proposed and the task is particularly suited for deep learning methods, there are no public datasets and benchmarks available to compare methods and to allow training of generalizing deep learning models. In an international collaboration of researchers from seven institutions from the EU and USa, we have created BaGLS, a large, multihospital dataset of 59,250 high-speed videoendoscopy frames with individually annotated segmentation masks. The frames are based on 640 recordings of healthy and disordered subjects that were recorded with varying technical equipment by numerous clinicians. the BaGLS dataset will allow an objective comparison of glottis segmentation methods and will enable interested researchers to train their own models and compare their methods.

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“…Many studies already used it for model selection. In Dionysios et al [49], they used a precision-recall curve for determining the threshold for classifying potential voice disorders in the Greek population, which showed imbalanced labels. We compared precision recall-curve for 90 s of time window, the random forest showed the best performance as the same as the above experiment results.…”

Section: Evaluation Resultsmentioning

confidence: 99%

Improved Mitigation of Cyber Threats in IIoT for Smart Cities: A New-Era Approach and Scheme

Park

Lee

2021

Sensors

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Cybersecurity in Industrial Internet of Things (IIoT) has become critical as smart cities are becoming increasingly linked to industrial control systems (ICSs) used in critical infrastructure. Consequently, data-driven security systems for analyzing massive amounts of data generated by smart cities have become essential. A representative method for analyzing large-scale data is the game bot detection approach used in massively multiplayer online role-playing games. We reviewed the literature on bot detection methods to extend the anomaly detection approaches used in bot detection schemes to IIoT fields. Finally, we proposed a process wherein the data envelopment analysis (DEA) model was applied to identify features for efficiently detecting anomalous behavior in smart cities. Experimental results using random forest show that our extracted features based on a game bot can achieve an average F1-score of 0.99903 using 10-fold validation. We confirmed the applicability of the analyzed game-industry methodology to other fields and trained a random forest on the high-efficiency features identified by applying a DEA, obtaining an F1-score of 0.997 using the validation set approach. In this study, an anomaly detection method for analyzing massive smart city data based on a game industry methodology was presented and applied to the ICS dataset.

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Checking for voice disorders without clinical intervention: The Greek and global VHI thresholds for voice disordered patients

Cited by 17 publications

References 48 publications

Factors affecting voice quality in early glottic cancer before and after radiotherapy

Factors affecting voice quality in early glottic cancer before and after radiotherapy

BAGLS, a multihospital Benchmark for Automatic Glottis Segmentation

Improved Mitigation of Cyber Threats in IIoT for Smart Cities: A New-Era Approach and Scheme

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