An electroglottographical analysis-based discriminant function model differentiating multiple sclerosis patients from healthy controls

Utilising Discriminant Function Analysis (DFA) for Classifying Osteoarthritis (OA) Patients and Volunteers Based on Biomarker Concentration

Osteoarthritis (OA) is a degenerative joint disease characterised by the breakdown of cartilage, causing pain, stiffness, and limited movement. Early diagnosis is crucial for effective management but remains challenging due to non-specific early symptoms. This study explores the application of Discriminant Function Analysis (DFA) to classify OA patients and healthy volunteers based on biomarker concentrations of Interleukin-6 (IL-6), Tumour necrosis factor-alpha (TNF-α), and Myeloperoxidase (MPO). DFA was employed to analyse biomarker data from 86 participants (58 patients, 28 volunteers) to evaluate the discriminatory power of these biomarkers in predicting OA. Significant differences were observed in MPO and TNF-α levels between groups, while IL-6 did not show a significant distinction. The iterative classification process improved model assumptions and classification accuracy, achieving a pre-classification accuracy of 71.8%, which adjusted to 57.1% post-classification. The results highlight DFA's potential in OA diagnosis, suggesting its utility in managing complex data and aiding personalised treatment strategies. The study underscores the need for larger sample sizes and additional biomarkers to enhance diagnostic robustness and provides a foundation for integrating DFA into clinical practice for early OA detection.

Maximum Phonation Time as a Predictor of Lactate Threshold during Intermittent Incremental Endurance Test

The aim of the present study was to examine whether the exercise intensity corresponding to the lactate threshold may be predicted by the Maximum Phonation Time task (MPT). Ten Greek amateur football players (age: 18.4 ± 1.0 years), performed a graded cycling exercise test to exhaustion in order to determine lactate threshold. A number of physiological variables were measured including perceived exertion, cardiopulmonary values and blood lactate. The MPT variable was correlated with all of the physiological variables. Also, a binary logistic regression analysis was used to investigate whether MPT could predict lactate threshold. The ROC analysis showed specificity to be 0.90 and sensitivity to be 0.70 (optimal screening cutoff point for MPT 9.5 seconds). The results showed an odds ratio of 1.45 indicating a 45% increase in the probability of passing the threshold for every second there was a reduction in voice duration. MPT may be used as a simple, non-invasive, inexpensive method for monitoring exercise intensity during physical exercise. Further research is needed to measure its efficacy in bigger samples and in different sports.

Assessing clinical utility of machine learning and artificial intelligence approaches to analyze speech recordings in multiple sclerosis: A pilot study

BACKGROUND

An early diagnosis together with an accurate disease progression monitoring of multiple sclerosis is an important component of successful disease management. Prior studies have established that multiple sclerosis is correlated with speech discrepancies. Early research using objective acoustic measurements has discovered measurable dysarthria.

METHOD

The objective was to determine the potential clinical utility of machine learning and deep learning/AI approaches for the aiding of diagnosis, biomarker extraction and progression monitoring of multiple sclerosis using speech recordings. A corpus of 65 MS-positive and 66 healthy individuals reading the same text aloud was used for targeted acoustic feature extraction utilizing automatic phoneme segmentation. A series of binary classification models was trained, tuned, and evaluated regarding their Accuracy and area-under-the-curve.

RESULTS

The Random Forest model performed best, achieving an Accuracy of 0.82 on the validation dataset and an area-under-the-curve of 0.76 across 5 k-fold cycles on the training dataset. 5 out of 7 acoustic features were statistically significant.

CONCLUSION

Machine learning and artificial intelligence in automatic analyses of voice recordings for aiding multiple sclerosis diagnosis and progression tracking seems promising. Further clinical validation of these methods and their mapping onto multiple sclerosis progression is needed, as well as a validating utility for English-speaking populations.

Speech metrics, general disability, brain imaging and quality of life in multiple sclerosis

BACKGROUND AND PURPOSE

Objective measurement of speech has shown promising results to monitor disease state in multiple sclerosis. In this study, we characterize the relationship between disease severity and speech metrics through perceptual (listener based) and objective acoustic analysis. We further look at deviations of acoustic metrics in people with no perceivable dysarthria.

METHODS

Correlations and regression were calculated between speech measurements and disability scores, brain volume, lesion load and quality of life. Speech measurements were further compared between three subgroups of increasing overall neurological disability: mild (as rated by the Expanded Disability Status Scale ≤2.5), moderate (≥3 and ≤5.5) and severe (≥6).

RESULTS

Clinical speech impairment occurred majorly in people with severe disability. An experimental acoustic composite score differentiated mild from moderate (P < 0.001) and moderate from severe subgroups (P = 0.003), and correlated with overall neurological disability (r = 0.6, P < 0.001), quality of life (r = 0.5, P < 0.001), white matter volume (r = 0.3, P = 0.007) and lesion load (r = 0.3, P = 0.008). Acoustic metrics also correlated with disability scores in people with no perceivable dysarthria.

CONCLUSIONS

Acoustic analysis offers a valuable insight into the development of speech impairment in multiple sclerosis. These results highlight the potential of automated analysis of speech to assist in monitoring disease progression and treatment response.

The Construction of Primary Screening Model and Discriminant Model for Chronic Obstructive Pulmonary Disease in Northeast China

Objective

The diagnosis of chronic obstructive pulmonary disease (COPD) is challenging, especially in the primary institution which lacks spirometer. To reduce the rate of COPD missed diagnoses in Northeast China, which has a higher prevalence of COPD, this study aimed to establish efficient primary screening and discriminant models of COPD in this region.

Patients and Methods

Subjects from Northeast China were enrolled from December 2017 to April 2019 from The First Hospital of China Medical University. Pulmonary function tests and questionnaire were given to all participants. Using illness or no illness as the goal for screening models and disease severity as the goal for discriminant models, multivariate linear regression, logical regression, linear discriminant analysis, K-nearest neighbor, decision tree and support vector machine were constructed through R language and Python software. After comparing effectiveness among them, the most optimal primary screening and discriminant models were established.

Results

Enrolled were 232 COPD patients (124 GOLD I–II and 108 GOLD III–IV) and 218 normal controls. Eight primary screening models were established. The optimal model was Y = −1.2562–0.3891X₄ (education level) + 1.7996X₅ (dyspnea) + 0.5102X₆ (cooking fuel grade) + 1.498X₇ (smoking index) + 0.8077X₉ (family history)-0.5552X₁₁ (BMI) + 0.538X₁₃ (cough with sputum) + 2.0328X₁₄ (wheezing) + 1.3378X₁₆ (farmers) + 0.8187X₁₇ (mother’s smoking exposure history during pregnancy)-0.389X₁₈ (kitchen ventilation) + 0.6888X₁₉ (childhood heating). Six discriminant models were established. The optimal model was decision tree (the optimal variables: dyspnea (x₅), cooking fuel grade (x₆), second-hand smoking index (x₈), BMI (x₁₁), cough (x₁₂), cough with sputum (x₁₃), wheezing (x₁₄), farmer (x₁₆), kitchen ventilation (x₁₈), and childhood heating (x₁₉)). The code was established to combine the discriminant model with computer technology.

Conclusion

Many factors were related to COPD in Northeast China. Stepwise logistic regression and decision tree were the optimal screening and discriminant models for COPD in this region.