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Artificial intelligence in the diagnosis of glaucoma and neurodegenerative diseases
Md Mahmudul Hasan, Jack Phu, Arcot Sowmya, Erik Meijering, Michael Kalloniatis. Clinical & experimental optometry, 2023-09-06
Topic: Brain imaging, connectivity and assessment
Artificial Intelligence is a rapidly expanding field within computer science that encompasses the emulation of human intelligence by machines. Machine learning and deep learning - two primary data-driven pattern analysis approaches under the umbrella of artificial intelligence - has created considerable interest in the last few decades. The evolution of technology has resulted in a substantial amount of artificial intelligence research on ophthalmic and neurodegenerative disease diagnosis using retinal images. Various artificial intelligence-based techniques have been used for diagnostic purposes, including traditional machine learning, deep learning, and their combinations. Presented here is a review of the literature covering the last 10 years on this topic, discussing the use of artificial intelligence in analysing data from different modalities and their combinations for the diagnosis of glaucoma and neurodegenerative diseases. The performance of published artificial intelligence methods varies due to several factors, yet the results suggest that such methods can potentially facilitate clinical diagnosis. Generally, the accuracy of artificial intelligence-assisted diagnosis ranges from 67-98%, and the area under the sensitivity-specificity curve (AUC) ranges from 0.71-0.98, which outperforms typical human performance of 71.5% accuracy and 0.86 area under the curve. This indicates that artificial intelligence-based tools can provide clinicians with useful information that would assist in providing improved diagnosis. The review suggests that there is room for improvement of existing artificial intelligence-based models using retinal imaging modalities before they are incorporated into clinical practice.
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APA 7th
Hasan, M. M., Phu, J., Sowmya, A., Meijering, E., & Kalloniatis, M. (2024). Artificial intelligence in the diagnosis of glaucoma and neurodegenerative diseases. Clinical & experimental optometry. https://doi.org/10.1080/08164622.2023.2235346
Vancouver
Hasan MM, Phu J, Sowmya A, Meijering E, Kalloniatis M. Artificial intelligence in the diagnosis of glaucoma and neurodegenerative diseases. Clinical & experimental optometry. 2024. doi:10.1080/08164622.2023.2235346. PMID: 37674264.
BibTeX
@article{hasan2024-37674264,
author = {Hasan, Md Mahmudul and Phu, Jack and Sowmya, Arcot and Meijering, Erik and Kalloniatis, Michael},
title = {{Artificial intelligence in the diagnosis of glaucoma and neurodegenerative diseases}},
journal = {Clinical \& experimental optometry},
year = {2024},
doi = {10.1080/08164622.2023.2235346},
pmid = {37674264},
url = {https://doi.org/10.1080/08164622.2023.2235346}
}
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Key findings
- The performance of published artificial intelligence methods varies due to several factors, yet the results suggest that such methods can potentially facilitate clinical diagnosis.
- This indicates that artificial intelligence-based tools can provide clinicians with useful information that would assist in providing improved diagnosis.
- The review suggests that there is room for improvement of existing artificial intelligence-based models using retinal imaging modalities before they are incorporated into clinical practice.
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Study details
- Effect size71.5%
- Evidence strengthLow
How this study scored 46/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, studied in people and randomised / blinded because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record, multi-centre collaboration and free to read, so those were left out: it earned 26 of the 56 points we could judge it on, which is 46 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Clinical & experimental optometry) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| 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 | 2024 — within 3 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 37674264) |
| Free to read | — / 2 | Access not recorded — left out of this score |
| Points | 26 / 56 | Scored on 10 of 20 parts. We have no data for the rest, so they are left out: 26 of the 56 points we could judge it on is 46 out of 100. |
| Total | 46 / 100 | — |
What each of these 20 components measures, and what this score is not →
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