The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
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Timeline
X Demographics
Mendeley readers
| Title |
Artificial intelligence approaches for tinnitus diagnosis: leveraging high-frequency audiometry data for enhanced clinical predictions
|
|---|---|
| Published in |
Frontiers in Artificial Intelligence, May 2024
|
| DOI | 10.3389/frai.2024.1381455 |
| Pubmed ID | |
| Authors |
Seyed-Ali Sadegh-Zadeh, Alireza Soleimani Mamalo, Kaveh Kavianpour, Hamed Atashbar, Elham Heidari, Reza Hajizadeh, Amir Sam Roshani, Shima Habibzadeh, Shayan Saadat, Majid Behmanesh, Mozafar Saadat, Sahar Sayyadi Gargari |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 5 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 1 | 20% |
| Student > Master | 1 | 20% |
| Lecturer | 1 | 20% |
| Unknown | 2 | 40% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 1 | 20% |
| Computer Science | 1 | 20% |
| Immunology and Microbiology | 1 | 20% |
| Unknown | 2 | 40% |