10.14639/0392-100X-N2309
Objective. The diagnosis of benign lesions of the vocal fold (BLVF) is still challenging. The analysis of the acoustic signals through the implementation of machine learning models can be a viable solution aimed at offering support for clinical diagnosis. Materials and methods. In this study, a support vector machine was trained and cross-validated (10-fold cross-validation) using 138 features extracted from the acoustic signals of 418 patients with polyps, nodules, oedema, and cysts. The model’s performance was presented as accuracy and average F1-score. The results were also analysed in male (M) and female (F) subgroups. Results. The validation accuracy was 55%, 80%, and 54% on the overall cohort, and in M and F, respectively. Better performances were observed in the detection of cysts and nodules (58% and 62%, respectively) vs polyps and oedema (47% and 53%, respectively). The results on each lesion and the different patterns of the model on M and F are in line with clinical observations, obtaining better results on F and more accurate detection of polyps in M. Conclusions. This study showed moderately accurate detection of four types of BLVF using acoustic signals. The analysis of the diagnostic results on gender subgroups highlights different behaviours of the diagnostic model.
text/xml
1827-675X
2309
en
Andrea Mannini
http://creativecommons.org/licenses/by-nc-nd/4.0
ACTA Otorhinolaryngologica Italica
dysphonia
Artificial intelligence for the recognition of benign lesions of vocal folds from audio recordings
Text.Serial.Journal
https://www.actaitalica.it/
43
Laryngology
2023-07-28
2022-09-20
2023-09-29
2023-10-02
317-323
1
Andrea Mannini
2023/07/28
10.14639/0392-100X-N2309
317
Institute of Biorobotics, Scuola Superiore Sant’Anna, Pontedera, Italy; IRCCS Fondazione Don Carlo Gnocchi, Firenze, Italy
1827-675X
dysphonia
en
323
Artificial intelligence for the recognition of benign lesions of vocal folds from audio recordings
https://www.actaitalica.it/article/download/2309/1027
43
ACTA OTORHINOLARYNGOLOGICA ITALICA 2023;43:317-323; doi: 10.14639/0392-100X-N2309




Laryngology


Artificial intelligence for the recognition of benign
lesions of vocal folds from audio recordings
Il ruolo del machine learning nel riconoscimento delle lesioni cordali benigne
dal segnale vocale
Maria Raffaella Marchese1, Federico Sensoli2, Silvia Campagnini2,3, Matteo Cianchetti2, Andrea Nacci4,
Francesco Ursino5, Lucia D’Alatri1,6, Jacopo Galli1,6, Maria Chiara Carrozza2, Gaetano Paludetti1,6, Andrea Mannini2,3
1
  Unità Operativa Complessa di Otorinolaringoiatria, Dipartimento di Neuroscienze, Organi di Senso e Torace, Fondazione
Policlinico Universitario A. Gemelli IRCCS, Rome, Italy; 2 Institute of Biorobotics, Scuola Superiore Sant’Anna, Pontedera, Italy;
3
  IRCCS Fondazione Don Carlo Gnocchi, Firenze, Italy; 4 U.O. Otorinolaringoiatria Audiologia e Foniatria, Azienda Ospedaliero
Universitaria Pisana, Pisa, Italy; 5 Istituto Nazionale di Ricerche in Foniatria “G. Bartalena”, Pisa, Italy; 6 Sezione di Otorinolaringoiatria,
Dipartimento Universitario Testa-Collo e Organi di Senso, Università Cattolica del Sacro Cuore, Rome, Italy


SUMMARY
Objective. The diagnosis of benign lesions of the vocal fold (BLVF) is still challenging. The
analysis of the acoustic signals through the implementation of machine learning models can
be a viable solution aimed at offering support for clinical diagnosis.
Materials and methods. In this study, a support vector machine was trained and cross-validated
(10-fold cross-validation) using 138 features extracted from the acoustic signals of 418 patients
with polyps, nodules, oedema, and cysts. The model’s performance was presented as accuracy
and average F1-score. The results were also analysed in male (M) and female (F) subgroups.
Results. The validation accuracy was 55%, 80%, and 54% on the overall cohort, and in M
and F, respectively. Better performances were observed in the detection of cysts and nodules
(58% and 62%, respectively) vs polyps and oedema (47% and 53%, respectively). The re-               Received: September 20, 2022
sults on each lesion and the different patterns of the model on M and F are in line with clini-     Accepted: March 22, 2023
cal observations, obtaining better results on F and more accurate detection of polyps in M.         Published online: July 28, 2023
Conclusions. This study showed moderately accurate detection of four types of BLVF us-
ing acoustic signals. The analysis of the diagnostic results on gender subgroups highlights         Correspondence
different behaviours of the diagnostic model.                                                       Maria Raffaella Marchese
                                                                                                    Unità Operativa Complessa di Otorinolaringoia-
KEY WORDS: artificial intelligence, machine learning, benign lesions of vocal folds,                tria, Dipartimento di Scienze dell’Invecchiamento,
dysphonia                                                                                           Neurologiche, Ortopediche e della Testa-Collo,
                                                                                                    Fondazione Policlinico Universitario A. Gemelli
                                                                                                    IRCCS, l.go “A. Gemelli” 8, 00168 Rome, Italy
RIASSUNTO                                                                                           Tel. +39 06 30154439. Fax +39 06 3051194
Obiettivo. La diagnosi delle lesioni cordali benigne è ancora una sfida. L’analisi dei segna-       E-mail: mariaraffaella.marchese@policlinicogemelli.it
li vocali attraverso l’applicazione di modelli di Machine Learning potrebbe rappresentare
una valida soluzione nell’offrire un supporto alla diagnosi clinica.
                                                                                                      How to cite this article: Marchese MR, Sen-
Materiali e metodi. In questo studio una Support Vector Machine è stata addestrata e
                                                                                                      soli F, Campagnini S, et al. Artificial intelli-
sottoposta a validazione incrociata 10 volte usando 138 caratteristiche estratte dai segnali          gence for the recognition of benign lesions of
acustici di 418 pazienti affetti da polipi, noduli, edema di Reinke e cisti. Le prestazioni del       vocal folds from audio recordings. Acta Otorhi-
modello sono state espresse in termini di accuratezza e punteggio F1 medio. I risultati sono          nolaryngol Ital 2023;43:317-323. https://doi.
stati inoltre analizzati nei sottogruppi maschi (M) e femmine (F).                                    org/10.14639/0392-100X-N2309
Risultati. L’accuratezza era del 55%, 80% e 54% rispettivamente nel campione totale, nei
maschi e nelle femmine. Le performances migliori sono state ottenute nel riconoscimento             © Società Italiana di Otorinolaringoiatria
delle cisti e dei noduli (58% e 62% rispettivamente), rispetto ai polipi e agli edemi (47% e        e Chirurgia Cervico-Facciale
53% rispettivamente). I risultati per ciascuna lesione e i differenti pattern del modello sono
in linea con le caratteristiche cliniche nei sottogruppi maschi e femmine per i migliori ri-                       OPEN ACCESS
sultati ottenuti nel gruppo femminile e una sensibile discriminazione dei polipi nei maschi.        This is an open access article distributed in accordance with
Conclusioni. Questa ricerca ha dimostrato una capacità di riconoscimento dei quattro tipi           the CC-BY-NC-ND (Creative Commons Attribution-Non-
di lesioni cordali benigne in base ai segnali acustici moderatamente accurata. L’analisi dei        Commercial-NoDerivatives 4.0 International) license. The
                                                                                                    article can be used by giving appropriate credit and mentio-
risultati diagnostici nei sottogruppi divisi per genere evidenzia i diversi comportamenti del       ning the license, but only for non-commercial purposes and
modello diagnostico.                                                                                only in the original version. For further information: https://
                                                                                                    creativecommons.org/licenses/by-nc-nd/4.0/deed.en
PAROLE CHIAVE: intelligenza artificiale, machine learning, lesioni cordali benigne, disfonia



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Introduction                                                         ment follow-up, but it does not provide information for dif-
                                                                     ferential diagnosis or inter-individual comparison. In recent
The development of artificial intelligence (AI), the evolution       years, research has been oriented towards two principal aims,
of voice technology, progress in audio signal analysis, and nat-     namely nonlinear dynamic acoustic analysis and the individu-
ural language processing/understanding methods have opened           ation of quantitative instrumental tools for voice assessment 14.
the way to numerous potential applications of voice, such as         Novel ML algorithms have recently improved the clas-
the identification of vocal biomarkers for diagnosis, classifica-    sification accuracy of selected features in target variables
tion, patient remote monitoring, or to enhance clinical prac-        compared to more conventional procedures thanks to the
tice 1. Especially at the beginning of the applications of AI,       ability to combine and analyse large data-sets of voice
most studies used automatic systems based on machine learn-          features 15,16. Even if the majority of studies focus on the
ing (ML) on voice recordings to classify healthy subjects ac-        diagnosis of a disorder where they differentiate between
cording to age and gender 2,3. More recently, research focused       healthy and non-healthy subjects, we believe that a more
on the role of the audio signal of the voice as a signature of the   important task is frequently differential diagnosis, where
pathogenic process. Dysphonia indicates that some changes            one needs to choose between two or more different dis-
have occurred in voice production 4. The overall prevalence of       eases. Even though this is a challenging task, it is of crucial
dysphonia is approximately 1% even if the actual rates may be        importance to move decisional support to this level. To our
higher depending on the population studied and the definition        knowledge, the differential discrimination of BLVF using
of the specific voice disorder 5. The impact of a voice disorder     automatic audio data processing methods has been poorly
has been increasingly recognised as a public health concern          studied to date. Moreover, there is no dataset of off-the-
because it influences the quality of physical, social, and oc-       shelf audio recordings from dysphonic patients affected by
cupational aspects of life by interfering with communication 6.      BLVF available online. The main aim of this work is the
Voice health may be assessed by several acoustic parameters.         study, development, and validation of ML algorithms to
The relationship between voice pathology and acoustic voice          recognise the different BLVF from digital voice recordings.
features has been clinically established and confirmed both          As a side result, the analysis of features’ importance for the
quantitatively and subjectively by speech experts 7. The auto-       diagnostic models and their pathophysiological relevance
matic systems are designed to determine whether the sample           was obtained.
belongs to a healthy or non-healthy subject. The exactness of
acoustic parameters is linked to the features used to estimate
them for speech noise identification. Current voice searches         Materials and methods
are mostly restricted to basic questions even if with broad per-     We collected the audio recordings of dysphonic patients af-
spectives. Dankovicova et al. 8 demonstrated that ML analy-          fected by BLVF who referred to the Phoniatric Unit of the
sis can recognise pathological speech with high classification       Fondazione Policlinico Universitario A. Gemelli - IRCCS
accuracy. In the literature, the studies on vocal biomarkers         of Rome from June 2015 to December 2019. All voice sam-
have mainly been performed in the fields of neurodegenera-           ples were divided into the following groups based on the
tive disorders (Parkison’s and Alzheimer’s diseases) 6. On the       endoscopic diagnosis: vocal fold cysts, Reinke’s oedema,
contrary, the literature on vocal biomarkers of specific vocal       nodules and polyps. We excluded patients younger than 18
fold diseases is anecdotal and related to functional vocal fold      years or older than 65 years, with previous laryngeal or thy-
disorders or rare movement disorders of the larynx 9 (Tab. I).       roid surgery, speech therapy, pulmonary diseases, gastro-
The most common causes of dysphonia are benign lesions of            oesophageal reflux, laryngeal movement disorder or recur-
the vocal fold (BLVF). The prevalence has been reported to           rent laryngeal nerve paralysis. We also excluded non-native
be 2.5%-7.7% in national epidemiologic studies in the United         Italian speakers. The audio tracks were obtained by asking
States and South Korea 10,11. Currently, videolaryngostrobos-        to pronounce with usual voice intensity, pitch and quality
copy is the gold standard for the diagnosis of BLVF 12. How-         the word /aiuole/ three times in a row. Voices were acquired
ever, laryngoscopy is an invasive and expensive procedure.           using a Shure model SM48 microphone (Evanston IL) po-
Moreover, it is not generally available in primary care units        sitioned at an angle of 45° at a distance of 20 cm from the
increasing the risk of delayed diagnosis and treatment. BLVF         patient’s mouth. The microphone saturation input was fixed
can interfere with vocal folds closure, introducing asymme-          at 6/9 of CH1 and the environmental noise was < 30 dB
try to the vocal folds, and producing severely rough voices          sound pressure level (SPL). The signals were recorded in
and aperiodic acoustic waveforms 13. The traditional acoustic        “.nvi” format with a high-definition audio-recorder Com-
analysis is suitable only for nearly periodic signals and allows     puterized Speech Lab, model 4300B, from Kay Elemetrics
the clinician to have a quantifiable preliminary status for treat-   (Lincoln Park, NJ, USA) with a sampling rate of 50 kHz



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Table I. The list of researches that used ML analysis of voice recordings.
 Author and reference                   Size of sample                     Study aim                      Method of analysis              Author’s key findings
 Li et al. 2                      Training data set: 472 speakers;      To present an automatic          Baseline subsystems:    Minimum 3.1% and maximum
                                     development data set: 300          speaker age and gender           GMM based on mel-       5.6% improvement of accuracy
                                              speakers                   identification approach           frequency cepstral    compared to the SVM baseline
                                                                        which combines different      coefficient features, SVM               system
                                                                        methods at both acoustic         based on GMM mean
                                                                          and prosodic levels to        supervectors and SVM
                                                                          improve the baseline      based on 450-dimensional
                                                                              performance              utterance level features;
                                                                                                            four subsystems
 Berardi et al. 3                       From the archive of voice             To investigate the    The acoustic features were The ML model estimated the
                                      recordings given at Brigham       progressive degeneration      used to train two random          age of the talker as
                                       Young University of a single      of a single talker’s voice  Forest regression models.     well as the human listeners.
                                     individual spanning about 50       quality by comparing the       One model used actual     The acoustic feature related to
                                             years were used               results of a computer     (chronological) age as the the perception of aging voice is
                                                                         model trained on actual     response variable with the    the fundamental frequency
                                                                      age with a model trained on other using estimated age
                                                                      perceived age with the goal
                                                                         of determining acoustic
                                                                          features related to the
                                                                        perception of aging voice
 Dankovicova et al. 7              1560 speech features extracted       To utilise ML methods to         The classifiers, used:   91.3% classification accuracy
                                 and used to train the classification       recognise dysphonia          K-nearest neighbors,
                                                  model                                               random Forest plots, and
                                                                                                                  SVM
 Zhan et al. 8                          6148 smartphone activity          Ability of the mPDS to          ML based approach           Effective and objective
                                 assessments from 129 individuals detect intraday symptom                 to generate a mPDS       Parkinson disease severity
                                                                      fluctuations, the correlation     that objectively weighs  score derived from smartphone
                                                                         between the mPDS and       features derived from each             assessments
                                                                        standard measures, and            smartphone activity
                                                                       the ability of the mPDS to
                                                                        respond to dopaminergic
                                                                                  medication
 Suppa et al. 9                     60 patients with adductor-type      To evaluate with cepstral     Cepstral analysis and ML      ML and cepstral analysis
                                 spasmodic dysphonia pre-BoNT-A analysis and ML adductor               algorithm classification   differentiate healthy subjects
                                  therapy and 60 healthy subjects;      spasmodic voices and to                techniques         and adductor-type spasmodic
                                  35 patients were evaluated after      compare the results with                                      dysphonic voices. ML
                                              BoNT-A therapy           those of healthy subjects;                                 measures correlated with the
                                                                       to investigate the effect of                               severity of dysphonia pre and
                                                                                   BoNT-A                                              post-BoNT-A therapy
 Asci et al. 15                     Voice samples of 138 younger            To examine the age-     ML analysis through a SVM ML analysis demonstrates the
                                      adults and 123 older adults     related changes in voice in                                   effect of ageing on voice
                                         collected at home using              ecological setting
                                               smartphones
ML: Machine Learning; mPDS: mobile Parkinson disease score; BoNT-A: botulinum neurotoxin A; SVM: support vector machine; GMM: Gaussian mixture model.




frequency and converted to “.wav” format. Each audio file                             Signal pre-processing
was anonymously labelled with gender and type of BLVF.                                The unvoiced segments of signals were removed through
                                                                                      a threshold-based algorithm, which accounts for the signal
Analysis pipeline                                                                     loudness and computed using the MathWorks built-in func-
All the following analyses were performed using MatLab                                tion acousticLoudness. This function implements two differ-
R2019b, the MathWorks, Natick MA, USA. The analysis                                   ent methods accounted in the ISO 532 standard which allow
pipeline included signal pre-processing, features extrac-                             estimation of loudness as perceived by persons with onto-
tion, screening of the features, and model implementation                             logically normal hearing under specific listening conditions.
(Fig. 1).                                                                             More specifically, three different features characterising the



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M.R. Marchese et al.




Figure 1. Analysis pipeline.

                                                                    Figure 2. Overview of statistically significant features after the screening
                                                                    process.
human features are implemented, i.e. the dependency on
frequency, the concept of critical band and spectral masking
(the hearing feature for which frequencies can be swamped           models, 10-fold cross-validation was used both for param-
by a louder tone with close frequency). Further details on the      eter optimisation and further feature selection. Specifically,
implementation of these methods can be found in the fol-            two hyper-parameters of the model were set using a grid-
lowing references 17,18. Afterwards, a 1024-point Hamming           search and in a range 1-1000, the box-constraint C, which
window was used to segment the signals, with half-window            is a regularisation parameter, and the kernel parameter γ,
overlap, to reduce the spectral leakage.                            which controls the radius of influence of the kernel. Addi-
                                                                    tionally, a sequential feature selection (SFS) algorithm was
Feature extraction                                                  implemented to find an optimal feature set. Lastly, a class
On the segmented signal, 66 different features in the time,         balancing method was employed to overcome the unbal-
frequency, and cepstral domain were extracted. Next, seven          anced frequencies of the pathological classes. The augmen-
statistical measures were computed on the extracted fea-            tation algorithm of the Synthetic Minority Oversampling
tures, namely: mean, standard deviation, skewness, kurto-           Technique 19 was utilised for this purpose.
sis, 25th, 50th, and 75th percentiles. In addition, jitter, shim-   The classification performance was measured through the
mer, and tilt of the power spectrum were obtained from the          accuracy and the average F1-score. Both metrics were pro-
whole unsegmented signal (see supplementary material).              vided for the description of the overall classification perfor-
                                                                    mances and those obtained on gender subgroups.
Feature screening
Feature screening was applied using biostatistical analyses
on the whole dataset to reduce the extended number of fea-
                                                                    Results
tures to give as input to the classifier. Two statistical tests     Data were collected on 418 patients with a mean age of 48
were used to screen relevant features for the classification        years (range 19-65 years), 349 of whom (83.5%) were fe-
task: the one-way analysis of variance (ANOVA), when                males. The classification domain included four pathologi-
all the groups were normally distributed, and the Kruskal-          cal groups: among recruited patients, 69 (16.5% - 12 males
Wallis test, otherwise. The groups’ normality was veri-             and 57 females) were diagnosed with vocal fold cysts, 112
fied with the Kolmogorov-Smirnov test. For all the tests,           (26.8% - 12 males and 100 females) with Reinke’s oedema,
a p‑value < 0.05 was considered significant. An overview            141 (33.7% - 12 males and 129 females) with nodules and 96
of the screened features entering the classification model is       (23.0% - 33 males and 63 females) with polyps (Tab. II).
presented in Figure 2.                                              The seven statistical descriptors calculated for each of the
                                                                    66 extracted features led to a total number of 462 features.
Model implementation                                                Three additional features (jitter, shimmer, and tilt of the pow-
A non-linear Support Vector Machine (SVM) with a Gauss-             er spectrum) were computed on the whole signal, for a total
ian kernel was the algorithm chosen in this work. The clas-         of 466 features. Given the extensive number of features ex-
sifier was trained on the whole dataset (Mtot) and the male         tracted (overcoming the number of patients), a first statistical
(Mm) and female (Mf) subsamples separately. On the three            screening was performed. From the statistical tests, 138 of



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Table II. Sample numerosities on the whole dataset and pathology and gender-specific.
                                                                       Overall sample                    Pathology-specific samples (N)
                                                                                             Cysts        Oedemas             Nodules          Polyps
 Overall sample                                                            418                 69            112               141               96
 Gender-specific               Males                                        69                 12             12                12               33
 numerosity                    Females                                     349                 57            100               129               63



466 features were significantly associated with the outcome                 Thus, supporting the clinical experience with a tool based
and were thus fed into the SVM model. In Figure 1, a sum-                   on objective data can be crucial, and acoustic signals are
mary of the analysis pipeline and the number of patients and                promising non-invasive solutions for such a purpose.
features in each step is provided.                                          Going into detail about the specific types of BLVF, the model
On the overall model Mtot, the optimised 4 classes classi-                  had better performance on cysts and nodules (58% and 62%
fier showed an accuracy of 55.0% and an average F1-score                    accuracy, respectively), and less so for recognition of polyps
of 0.54. When splitting the sample by gender, an accuracy                   and oedema definition of identity (47% and 53% accuracy,
and average F1-score of 80% and 0.78 were obtained for                      respectively). While more investigation should be dedicated
Mm, and 54% and 0.54 for Mf, respectively. These results                    to these results, a possible explanation of these differences
are summarised in Table III, along with the single pathol-                  may be due to the diverse effects of the above-mentioned le-
ogy accuracies. In Figure 3, the confusion matrices for the                 sions on phonation. In fact, while polyps and oedema tend to
developed models are presented.                                             be very mobile during phonation, cysts and nodules are usu-
                                                                            ally less mobile 23 and this may have an effect on the features
                                                                            obtained from the acoustic signals.
Discussion
                                                                            Lower accuracy could also be due to different ages of pa-
In this work, an SVM was trained and cross-validated with                   tients, to fact that the pathological samples were at different
the aim to obtain differential diagnosis of BLVF types giv-                 stages of disease evolution, or because the morphological
en the acoustic signals of 418 patients, obtaining a global                 features varied and impacted differently on acoustic param-
accuracy of 55.0%.                                                          eters (e.g. size, the ratio of vocal lesion base to the width of
Compared to the majority of studies in the literature, our re-              the lesion, the implantation site etc.). In future studies, the
sults showed reduced performance, although it is worth notic-               system with a larger dataset and new classification model
ing that only a few have focused on pathological groups only                could be used to classify the stage of the disease.
for differential diagnosis 20,21. Even the comparison with the              Another aspect we considered in our analyses was the dif-
latter authors’ works should be done in light of the differences            ference in results obtained on gender split. Differences
among the studies. Namely, non-pathological cases were also                 between male and female voices and acoustic are related
included, with the exception of Pham et al. 19 in which deep                to many factors, such as physiology, anatomy, and even
learning algorithms were applied, different pathological cases              sociology and psychology, in terms of identity definition
were tested and different types of the acoustic signal were pro-            and behavioural characteristics 24. In this regard, as a result,
cessed (sustained vowels or combinations of them).                          differences in frequency content between genders could
The taxonomy of the types of BLVF is still in evolution, as                 emphasise the effects in the voice of different pathologies.
well as the tools and technologies available for diagnosis.                 In this work, where only features extracted from the spectral
For these reasons, and due to overlapping characteristics                   analysis entered the model, it is reasonable to obtain very dif-
of the groups, differential diagnosis can be challenging 22.                ferent behaviours of the model on the two subgroups. Specifi-


Table III. Classification results.
 Models                                       Overall sample                                 Pathology-specific accuracy
                                     Accuracy                F1-score    Cysts               Oedema                 Nodules                Polyps
 Mtot                                  0.55                    0.54       0.58                 0.53                   0.62                   0.47
 Mm                                    0.80                    0.78       0.75                 0.67                   0.83                   0.85
 Mf                                    0.54                    0.54       0.63                 0.54                   0.53                   0.49
Mtot: Whole dataset; Mm: male dataset; Mf: female dataset.




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                                                                          generically the role of gender in differential diagnosis, should
                                                                          be deepened further. Indeed, while reading these last findings,
                                                                          the sample size of the male subgroup should be taken into ac-
                                                                          count as a limitation reducing the generalisability of the results.
                                                                          To optimise the overall accuracy, other improvements could
                                                                          involve the training of other classification algorithms and the
                                                                          application of nested cross-validation for a better generalisa-
                                                                          bility of the results 26. Additionally, a larger prospective data-
                                                                          set could allow for the exploration of deep learning solutions
                                                                          and a complete selection of the features through automatic
                                                                          methods, avoiding the need for biostatistical screening. In
                                                                          future works, introducing novel features for classifiers or
                                                                          identifying the best performing ones can provide new infor-
                                                                          mation. More sophisticated features capturing hidden pat-
                                                                          terns or nonlinear relationships can significantly boost pre-
                                                                          diction accuracy. We believe that the clinical usefulness of
                                                                          the classification accuracy achieved by our model could be
                                                                          understood by comparing with further studies how our algo-
                                                                          rithm performs to those of human experts and non-experts.
                                                                          Above all, given the promising results in diagnostic prob-
                                                                          lems obtained from clinical and endoscopic high-speed
                                                                          videos 27, we believe that a bimodal analysis system that
                                                                          integrates both audio recording and video-endoscopic im-
                                                                          aging data would be helpful to improve accuracy and to
                                                                          explain the correlations of the SVM.
                                                                          Finally, our preliminary work overall suggests that machine
                                                                          learning, in combination with telemedicine, could provide
                                                                          a strategy to support screening between BLVF and malig-
                                                                          nant glottic lesions. Undoubtedly, a larger data set is need-
                                                                          ed before reaching such a target but this could be a concrete
                                                                          development.

                                                                          Conclusions
                                                                          This work focused on the development and cross-validation
                                                                          of a diagnostic model for the identification of four different
Figure 3. Confusion matrices for the developed model of male and female   BLVFs: polyps, cysts, nodules, and oedema. Although further
subgroups.                                                                efforts could be deployed on the technical implementation,
                                                                          when larger datasets will be available, the results appear prom-
                                                                          ising, with an overall accuracy in the automatic differential di-
cally, the results showed an accuracy of 79.7% in the model               agnosis of 55.3%. Moreover, the behavioural patterns of the
trained on males and 54.4% in the models trained on females.              models developed on the gender subgroups were particularly
The overall F1-score and accuracy and the pathology-specific              interesting. Specifically, a good sensitivity was found in polyp
accuracies suggest how better performances can be obtained                detection for males (85%), and in general better performance
in the male group, with respect to females. In fact, particularly         in the detection of each BLVF type than in females. Given
high accuracies of 83% and 85% were obtained on detection                 these results and the analysis of the literature, future research
of nodules and polyps, respectively (Tab. III). The promising             should focus on the combined use of clinical and instrumental
results on polyp detection, in opposition to the poor accuracy            data for the development of diagnostic models of BLVF.
obtained on the Mf (49%), may be explained by the fact that
BLVF, and especially vocal polyps, present a higher incidence             Conflict of interest statement
in the male population 25. Nevertheless, these results, and more          The authors declare no conflict of interest.



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Funding                                                                             the Korea National health and Nutrition Examination Surveys
This research did not receive any specific grant from funding                       2010‑2112. Ann Otol Rhinol Laryngol 2015;124:770-776. https://doi.
                                                                                    org/10.1177/0003489415583684
agencies in the public, commercial, or not-for-profit sectors.                 11
                                                                                    Hah JH, Sim S, An SY, et al. Evaluation of the prevalence of and
                                                                                    factors associated with laryngeal diseases among the general popu-
Author contributions                                                                lation. Laryngoscope 2015;125:2536-2542. https://doi.org/10.1002/
MRM: conceptualisation, data curation, writing; FS: for-                            lary.25424
mal analysis, software; SC: preparation and creation of                        12
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