Artificial Intelligence and Diabetic Retinopathy : AI Framework, Prospective Studies, Head-to-head Validation, and Cost-effectiveness
© 2023 by the American Diabetes Association..
Current guidelines recommend that individuals with diabetes receive yearly eye exams for detection of referable diabetic retinopathy (DR), one of the leading causes of new-onset blindness. For addressing the immense screening burden, artificial intelligence (AI) algorithms have been developed to autonomously screen for DR from fundus photography without human input. Over the last 10 years, many AI algorithms have achieved good sensitivity and specificity (>85%) for detection of referable DR compared with human graders; however, many questions still remain. In this narrative review on AI in DR screening, we discuss key concepts in AI algorithm development as a background for understanding the algorithms. We present the AI algorithms that have been prospectively validated against human graders and demonstrate the variability of reference standards and cohort demographics. We review the limited head-to-head validation studies where investigators attempt to directly compare the available algorithms. Next, we discuss the literature regarding cost-effectiveness, equity and bias, and medicolegal considerations, all of which play a role in the implementation of these AI algorithms in clinical practice. Lastly, we highlight ongoing efforts to bridge gaps in AI model data sets to pursue equitable development and delivery.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:46 |
|---|---|
| Enthalten in: |
Diabetes care - 46(2023), 10 vom: 01. Okt., Seite 1728-1739 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Rajesh, Anand E [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Journal Article |
|---|
| Anmerkungen: |
Date Completed 22.09.2023 Date Revised 25.09.2023 published: Print Citation Status MEDLINE |
|---|
| doi: |
10.2337/dci23-0032 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM362262950 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM362262950 | ||
| 003 | DE-627 | ||
| 005 | 20231226090855.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.2337/dci23-0032 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1207.xml |
| 035 | |a (DE-627)NLM362262950 | ||
| 035 | |a (NLM)37729502 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Rajesh, Anand E |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Artificial Intelligence and Diabetic Retinopathy |b AI Framework, Prospective Studies, Head-to-head Validation, and Cost-effectiveness |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 22.09.2023 | ||
| 500 | |a Date Revised 25.09.2023 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2023 by the American Diabetes Association. | ||
| 520 | |a Current guidelines recommend that individuals with diabetes receive yearly eye exams for detection of referable diabetic retinopathy (DR), one of the leading causes of new-onset blindness. For addressing the immense screening burden, artificial intelligence (AI) algorithms have been developed to autonomously screen for DR from fundus photography without human input. Over the last 10 years, many AI algorithms have achieved good sensitivity and specificity (>85%) for detection of referable DR compared with human graders; however, many questions still remain. In this narrative review on AI in DR screening, we discuss key concepts in AI algorithm development as a background for understanding the algorithms. We present the AI algorithms that have been prospectively validated against human graders and demonstrate the variability of reference standards and cohort demographics. We review the limited head-to-head validation studies where investigators attempt to directly compare the available algorithms. Next, we discuss the literature regarding cost-effectiveness, equity and bias, and medicolegal considerations, all of which play a role in the implementation of these AI algorithms in clinical practice. Lastly, we highlight ongoing efforts to bridge gaps in AI model data sets to pursue equitable development and delivery | ||
| 650 | 4 | |a Review | |
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 700 | 1 | |a Davidson, Oliver Q |e verfasserin |4 aut | |
| 700 | 1 | |a Lee, Cecilia S |e verfasserin |4 aut | |
| 700 | 1 | |a Lee, Aaron Y |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Diabetes care |d 1979 |g 46(2023), 10 vom: 01. Okt., Seite 1728-1739 |w (DE-627)NLM00042983X |x 1935-5548 |7 nnns |
| 773 | 1 | 8 | |g volume:46 |g year:2023 |g number:10 |g day:01 |g month:10 |g pages:1728-1739 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.2337/dci23-0032 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 46 |j 2023 |e 10 |b 01 |c 10 |h 1728-1739 | ||