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Artificial Intelligence in the Diagnostic Use of Transcranial Doppler and Sonography: A Scoping Review of Current Applications and Future Directions
Giuseppe Miceli, Maria Grazia Basso, Elena Cocciola, Antonino Tuttolomondo. Bioengineering, 2025-06-21
Topic: Risk, prevention and care in population studies
Artificial intelligence (AI) is revolutionizing the field of medical imaging, offering unprecedented capabilities in data analysis, image interpretation, and decision support. Transcranial Doppler (TCD) and Transcranial Color-Coded Doppler (TCCD) are widely used, non-invasive modalities for evaluating cerebral hemodynamics in acute and chronic conditions. Yet, their reliance on operator expertise and subjective interpretation limits their full potential. AI, particularly machine learning and deep learning algorithms, has emerged as a transformative tool to address these challenges by automating image acquisition, optimizing signal quality, and enhancing diagnostic accuracy. Key applications reviewed include the automated identification of cerebrovascular abnormalities such as vasospasm and embolus detection in TCD, AI-guided workflow optimization, and real-time feedback in general ultrasound imaging. Despite promising advances, significant challenges remain, including data standardization, algorithm interpretability, and the integration of these tools into clinical practice. Developing robust, generalizable AI models and integrating multimodal imaging data promise to enhance diagnostic and prognostic capabilities in TCD and ultrasound. By bridging the gap between technological innovation and clinical utility, AI has the potential to reshape the landscape of neurovascular and diagnostic imaging, driving advancements in personalized medicine and improving patient outcomes. This review highlights the critical role of interdisciplinary collaboration in achieving these goals, exploring the current applications and future directions of AI in TCD and TCCD imaging. This review included 41 studies on the application of artificial intelligence (AI) in neurosonology in the diagnosis and monitoring of vascular and parenchymal brain pathologies. Machine learning, deep learning, and convolutional neural network algorithms have been effectively utilized in the analysis of TCD and TCCD data for several conditions. Conversely, the application of artificial intelligence techniques in transcranial sonography for the assessment of parenchymal brain disorders, such as dementia and space-occupying lesions, remains largely unexplored. Nonetheless, this area holds significant potential for future research and clinical innovation.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Miceli, G., Basso, M. G., Cocciola, E., & Tuttolomondo, A. (2025). Artificial Intelligence in the Diagnostic Use of Transcranial Doppler and Sonography: A Scoping Review of Current Applications and Future Directions. Bioengineering. https://doi.org/10.3390/bioengineering12070681
Vancouver
Miceli G, Basso MG, Cocciola E, Tuttolomondo A. Artificial Intelligence in the Diagnostic Use of Transcranial Doppler and Sonography: A Scoping Review of Current Applications and Future Directions. Bioengineering. 2025. doi:10.3390/bioengineering12070681. PMID: 40722373.
BibTeX
@article{miceli2025-40722373,
author = {Miceli, Giuseppe and Basso, Maria Grazia and Cocciola, Elena and Tuttolomondo, Antonino},
title = {{Artificial Intelligence in the Diagnostic Use of Transcranial Doppler and Sonography: A Scoping Review of Current Applications and Future Directions}},
journal = {Bioengineering},
year = {2025},
doi = {10.3390/bioengineering12070681},
pmid = {40722373},
url = {https://doi.org/10.3390/bioengineering12070681}
}
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
Key findings
- Despite promising advances, significant challenges remain, including data standardization, algorithm interpretability, and the integration of these tools into clinical practice.
- Nonetheless, this area holds significant potential for future research and clinical innovation.
Why it matters
This review describes how AI is being applied to transcranial Doppler and ultrasound imaging, such as detecting vasospasm and emboli, but notes that data standardization, algorithm interpretability, and clinical integration remain unresolved. It reports no treatment, cure, or prevention of Alzheimer's disease.
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Study details
- Participants41
- Evidence strengthLow
How this study scored 45/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Bioengineering) |
| Journal's citation record | 1 / 4 | Bioengineering: journal h-index 100 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 1 / 4 | 41 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 3 / 7 | Cited 16 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 5.3 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 3 / 6 | Cited 3.3× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 2 / 4 | Cited 16 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Antonino Tuttolomondo: h-index 63 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | University of Palermo: institution h-index 473 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40722373) |
| Free to read | 2 / 2 | Free to read |
| Total | 45 / 100 | — |
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Read the original
- DOI: 10.3390/bioengineering12070681
- PubMed: 40722373
- Original source →
- Free full text: Bioengineering →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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Referenced by this study and also in the alzhub library, so you can read them here.
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