A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson's Disease: Past Studies and Future Perspectives

A pilot study for speech assessment to detect the severity of Parkinson's disease: An ensemble approach

BACKGROUND

Changes in voice are a symptom of Parkinson's disease and used to assess the progression of the condition. However, natural differences in the voices of people can make this challenging. Computerized binary speech classification can identify people with PD (PwPD), but its multiclass application to detect the severity of the disease remains difficult.

METHOD

This study investigated six diadochokinetic (DDK) tasks, four features (phonation, articulation, prosody, and their fusion), and three machine learning models for four severity levels of PwPD. The four binary classifications were: (i) Normal vs Not Normal, (ii) Slight vs Not Slight, (iii) Mild vs Not Mild and (iv) Moderate vs. Not Moderate. The best task and features for each class were identified and the models were ensembled to develop a multiclass model to distinguish between Normal vs. Slight vs. Mild vs. Moderate.

RESULTS

For Normal vs Not-normal, logistic regression (LR) using the prosody from "ka-ka-ka" task, Random Forest (RF) using articulation from "petaka" for Slight vs Not Slight, RF for the fusion from "ka-ka-ka" for Mild vs Not Mild and Gradient Boosting (GB) using prosody from "ta-ta-ta" for Moderate vs Not Moderate gave the best results. Combining these using LR achieved an accuracy of 72%.

CONCLUSION

Dividing the multiclass problem into four binary problems gives the optimum speech features for each class. This pilot study, conducted on a small public dataset, shows the potential of computerized speech analysis using DDK to evaluate the severity of Parkinson's disease voice symptoms.

Incorporating label uncertainty during the training of convolutional neural networks improves performance for the discrimination between certain and inconclusive cases in dopamine transporter SPECT

PURPOSE

Deep convolutional neural networks (CNN) hold promise for assisting the interpretation of dopamine transporter (DAT)-SPECT. For improved communication of uncertainty to the user it is crucial to reliably discriminate certain from inconclusive cases that might be misclassified by strict application of a predefined decision threshold on the CNN output. This study tested two methods to incorporate existing label uncertainty during the training to improve the utility of the CNN sigmoid output for this task.

METHODS

Three datasets were used retrospectively: a "development" dataset (n = 1740) for CNN training, validation and testing, two independent out-of-distribution datasets (n = 640, 645) for testing only. In the development dataset, binary classification based on visual inspection was performed carefully by three well-trained readers. A ResNet-18 architecture was trained for binary classification of DAT-SPECT using either a randomly selected vote ("random vote training", RVT), the proportion of "reduced" votes ( "average vote training", AVT) or the majority vote (MVT) across the three readers as reference standard. Balanced accuracy was computed separately for "inconclusive" sigmoid outputs (within a predefined interval around the 0.5 decision threshold) and for "certain" (non-inconclusive) sigmoid outputs.

RESULTS

The proportion of "inconclusive" test cases that had to be accepted to achieve a given balanced accuracy in the "certain" test case was lower with RVT and AVT than with MVT in all datasets (e.g., 1.9% and 1.2% versus 2.8% for 98% balanced accuracy in "certain" test cases from the development dataset). In addition, RVT and AVT resulted in slightly higher balanced accuracy in all test cases independent of their certainty (97.3% and 97.5% versus 97.0% in the development dataset).

CONCLUSION

Making between-readers-discrepancy known to CNN during the training improves the utility of their sigmoid output to discriminate certain from inconclusive cases that might be misclassified by the CNN when the predefined decision threshold is strictly applied. This does not compromise on overall accuracy.

Machine learning and wearable sensors for automated Parkinson's disease diagnosis aid: a systematic review

BACKGROUND

The diagnosis of Parkinson's disease is currently based on clinical evaluation. Despite clinical hallmarks, unfortunately, the error rate is still significant. Low in-vivo diagnostic accuracy of clinical evaluation mainly relies on the lack of quantitative biomarkers for an objective motor performance assessment. Non-invasive technologies, such as wearable sensors, coupled with machine learning algorithms, assess quantitatively and objectively the motor performances, with possible benefits either for in-clinic and at-home settings. We conducted a systematic review of the literature on machine learning algorithms embedded in smart devices in Parkinson's disease diagnosis.

METHODS

Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we searched PubMed for articles published between December, 2007 and July, 2023, using a search string combining "Parkinson's disease" AND ("healthy" or "control") AND "diagnosis", within the Groups and Outcome domains. Additional search terms included "Algorithm", "Technology" and "Performance".

RESULTS

From 89 identified studies, 47 met the inclusion criteria based on the search string and four additional studies were included based on the Authors' expertise. Gait emerged as the most common parameter analysed by machine learning models, with Support Vector Machines as the prevalent algorithm. The results suggest promising accuracy with complex algorithms like Random Forest, Support Vector Machines, and K-Nearest Neighbours.

DISCUSSION

Despite the promise shown by machine learning algorithms, real-world applications may still face limitations. This review suggests that integrating machine learning with wearable sensors has the potential to improve Parkinson's disease diagnosis. These tools could provide clinicians with objective data, potentially aiding in earlier detection.

Balance assessment in selected stages of Parkinson's disease using trend change analysis

BACKGROUND

Balance disorders in patients diagnosed with Parkinson's disease (PD) are associated with a change in balance-keeping strategy and reflex disorders which regulate the maintenance of vertical body posture. Center of foot pressure (COP) displacement signals were analyzed during quiet standing experiments to define such changes. The research aimed to apply stock exchange indices based on the trend change analyses to the assessment of a level of the Parkinson disease progression on the grounds of the analysis of the COP signals.

METHODS

30 patients in two stages of PD, 40 elderly participants, and 20 individuals at a young age were studied. Each person was subjected to 3 measurements with open and closed eyes. A technical analysis of the COP displacement signal was performed, and the following quantities were determined: indices related to the number of trend changes (TCI), indices defining a mean time (TCI_dT), and mean displacement (TCI_dS) and mean velocity (TCI_dV) between such changes.

RESULTS

The results indicate a higher TCI value for PD than for aged-matched control group (p < 0.05). In the case of PD patients, there was also an increase in the TCI_dS value by 2-5 mm, which mainly contributed to the increase in TCI_dV. Statistically significant differences for the TCI_dT values occurred between all groups in which differences in the average COP velocity were noted.

CONCLUSIONS

The TCI and TCI_dV results obtained for the healthy participants enabled the development of indices supporting PD diagnostics. The causes of the TCI_dV changes in patients were determined, i.e., whether they resulted from an increase in the TCI_dT or TCI_dS between the moments of trend changes indicated by the developed algorithm. The developed methodology provides new information on the impact of PD on the strategy of maintaining balance, which was impossible to obtain using currently used analyses. Trial registration The conducted research is an observational study and does not include a health care intervention. Participants gave their consent to participate in the research and the procedure was approved by the Institutional Bioethics Committee.