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| Name | Class |
|---|---|
| Excellence Center at Linköping - Lund in Information Technology (ELLIIT) | UNKNOWN |
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This work aims to evaluate whether voice recordings collected from patients diagnosed with COPD and healthy control groups can be used to detect the disease using machine learning techniques.
Voice data and sociodemographic data on gender and age will be collected through the "VoiceDiganostic" application from the company Voice Diagnostic, which allows one to participate without location dependency. Participants with a diagnosis will be marked as the COPD group, and others will be marked as the healthy control group. Private information such as known comorbidities, personal security numbers, health parameters and communication information will be separately noticed in a participation table for each group.
The collected data will be transformed into mathematical vocal measures called voice features. A dataset consisting of voice features in conjunction with demographics and health data will be constructed for further usage as an input to ML techniques.
Descriptive statistical analysis will be held on attributes containing information on input data and gained outcomes from ML algorithms. The achieved results will be presented in the form of summary tables and graphs.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| COPD | Participants with clinically diagnosed Chronic obstructive pulmonary disease. Total 34 recruitment, 18 Female, 16 Male |
| |
| HC | Participants without Chronic obstructive pulmonary disease diagnosis. Total 38 recruitment, 20 Female, 18 Male |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| COPD | Other | A data set consisting of information from COPD and HC groups will be used to experiment with the classification performance of several Machine Learning techniques. |
|
| Measure | Description | Time Frame |
|---|---|---|
| Accuracy | Binary detection performance of the ML algorithm | Week 51 |
| Input data importance scale | Features used as input data will be ranked from most important to less important one. | Week 51 |
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Inclusion Criteria:
Exclusion Criteria:
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Data will be collected from participants 18 years old and older with and without COPD diagnosis will be recruited.
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| Name | Affiliation | Role |
|---|---|---|
| Johan Sanmartin Berglund, MD, PhD | Blekinge Institute of Technology | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Blekinge Institute of Technology | Karlskrona | Blekinge County | 37179 | Sweden |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 39222579 | Derived | Idrisoglu A, Dallora AL, Cheddad A, Anderberg P, Jakobsson A, Sanmartin Berglund J. COPDVD: Automated classification of chronic obstructive pulmonary disease on a new collected and evaluated voice dataset. Artif Intell Med. 2024 Oct;156:102953. doi: 10.1016/j.artmed.2024.102953. Epub 2024 Aug 15. |
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Participant data can not be shared due to the GDPR. However, the dataset created can be available upon request from the institution.
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| ID | Term |
|---|---|
| D029424 | Pulmonary Disease, Chronic Obstructive |
| ID | Term |
|---|---|
| D008173 | Lung Diseases, Obstructive |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |
| D002908 | Chronic Disease |
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| D020969 |
| Disease Attributes |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |