Can Deep Learning Models Acquire Specialist Knowledge? Identifying Carotid Bruits in Type 2 Diabetes Using an Electronic Stethoscope
The present study explores the potential of artificial intelligence as a substitute for trained clinician identification of a carotid bruit from multiple auditory recordings from an electronic stethoscope in 98 people with type 2 diabetes, 48 of whom had a bruit. We employed various deep networks, i...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11050375/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849427497177841664 |
|---|---|
| author | Aref Miri Rekavandi Mohammed Bennamoun Farid Boussaid Wendy A. Davis Timothy M. E. Davis |
| author_facet | Aref Miri Rekavandi Mohammed Bennamoun Farid Boussaid Wendy A. Davis Timothy M. E. Davis |
| author_sort | Aref Miri Rekavandi |
| collection | DOAJ |
| description | The present study explores the potential of artificial intelligence as a substitute for trained clinician identification of a carotid bruit from multiple auditory recordings from an electronic stethoscope in 98 people with type 2 diabetes, 48 of whom had a bruit. We employed various deep networks, including ResNet and VGGish, alongside tailor-made deep learning models for this purpose. Our findings indicated that in scenarios such as this, where the data show high variability and yet are limited, the highest model accuracy achieved was 67.8%. This suggests the inadequacy of AI-driven systems in accurately detecting carotid bruits in the present participant sample. Detailed results for each deep learning and machine learning approach are presented separately, and the dataset has been established as a benchmark for future studies. Given the complexity observed with the current data volume, additional studies with larger sample sizes are warranted. |
| format | Article |
| id | doaj-art-54d9a1bbdc2f4e47b8d91de8201a7a27 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-54d9a1bbdc2f4e47b8d91de8201a7a272025-08-20T03:29:02ZengIEEEIEEE Access2169-35362025-01-011311802311803210.1109/ACCESS.2025.358293811050375Can Deep Learning Models Acquire Specialist Knowledge? Identifying Carotid Bruits in Type 2 Diabetes Using an Electronic StethoscopeAref Miri Rekavandi0https://orcid.org/0000-0001-9542-759XMohammed Bennamoun1https://orcid.org/0000-0002-6603-3257Farid Boussaid2https://orcid.org/0000-0001-7250-7407Wendy A. Davis3https://orcid.org/0000-0002-5709-8235Timothy M. E. Davis4https://orcid.org/0000-0003-0749-7411Department of Computer Science and Software Engineering, The University of Western Australia, Perth, AustraliaDepartment of Computer Science and Software Engineering, The University of Western Australia, Perth, AustraliaDepartment of Electrical, Electronic and Computer Engineering, The University of Western Australia, Perth, AustraliaMedical School, Fremantle Hospital, The University of Western Australia, Fremantle, AustraliaMedical School, Fremantle Hospital, The University of Western Australia, Fremantle, AustraliaThe present study explores the potential of artificial intelligence as a substitute for trained clinician identification of a carotid bruit from multiple auditory recordings from an electronic stethoscope in 98 people with type 2 diabetes, 48 of whom had a bruit. We employed various deep networks, including ResNet and VGGish, alongside tailor-made deep learning models for this purpose. Our findings indicated that in scenarios such as this, where the data show high variability and yet are limited, the highest model accuracy achieved was 67.8%. This suggests the inadequacy of AI-driven systems in accurately detecting carotid bruits in the present participant sample. Detailed results for each deep learning and machine learning approach are presented separately, and the dataset has been established as a benchmark for future studies. Given the complexity observed with the current data volume, additional studies with larger sample sizes are warranted.https://ieeexplore.ieee.org/document/11050375/Clinical decision makingcarotid bruitelectronic stethoscopetype 2 diabetes |
| spellingShingle | Aref Miri Rekavandi Mohammed Bennamoun Farid Boussaid Wendy A. Davis Timothy M. E. Davis Can Deep Learning Models Acquire Specialist Knowledge? Identifying Carotid Bruits in Type 2 Diabetes Using an Electronic Stethoscope IEEE Access Clinical decision making carotid bruit electronic stethoscope type 2 diabetes |
| title | Can Deep Learning Models Acquire Specialist Knowledge? Identifying Carotid Bruits in Type 2 Diabetes Using an Electronic Stethoscope |
| title_full | Can Deep Learning Models Acquire Specialist Knowledge? Identifying Carotid Bruits in Type 2 Diabetes Using an Electronic Stethoscope |
| title_fullStr | Can Deep Learning Models Acquire Specialist Knowledge? Identifying Carotid Bruits in Type 2 Diabetes Using an Electronic Stethoscope |
| title_full_unstemmed | Can Deep Learning Models Acquire Specialist Knowledge? Identifying Carotid Bruits in Type 2 Diabetes Using an Electronic Stethoscope |
| title_short | Can Deep Learning Models Acquire Specialist Knowledge? Identifying Carotid Bruits in Type 2 Diabetes Using an Electronic Stethoscope |
| title_sort | can deep learning models acquire specialist knowledge identifying carotid bruits in type 2 diabetes using an electronic stethoscope |
| topic | Clinical decision making carotid bruit electronic stethoscope type 2 diabetes |
| url | https://ieeexplore.ieee.org/document/11050375/ |
| work_keys_str_mv | AT arefmirirekavandi candeeplearningmodelsacquirespecialistknowledgeidentifyingcarotidbruitsintype2diabetesusinganelectronicstethoscope AT mohammedbennamoun candeeplearningmodelsacquirespecialistknowledgeidentifyingcarotidbruitsintype2diabetesusinganelectronicstethoscope AT faridboussaid candeeplearningmodelsacquirespecialistknowledgeidentifyingcarotidbruitsintype2diabetesusinganelectronicstethoscope AT wendyadavis candeeplearningmodelsacquirespecialistknowledgeidentifyingcarotidbruitsintype2diabetesusinganelectronicstethoscope AT timothymedavis candeeplearningmodelsacquirespecialistknowledgeidentifyingcarotidbruitsintype2diabetesusinganelectronicstethoscope |