602-P: Explaining an Artificial Intelligence (AI) System for Diabetic Retinopathy (DR) Screening in Primary Care

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 602-P
Author(s):  
NISHIT UMESH PAREKH ◽  
MALAVIKA BHASKARANAND ◽  
CHAITHANYA RAMACHANDRA ◽  
SANDEEP BHAT ◽  
KAUSHAL SOLANKI
Keyword(s):  
Artificial Intelligence ◽  
Primary Care ◽  
Diabetic Retinopathy

scholarly journals Evaluation of Artificial Intelligence–Based Grading of Diabetic Retinopathy in Primary Care

2018 ◽  
Vol 1 (5) ◽  
pp. e182665 ◽  
Author(s):  
Yogesan Kanagasingam ◽  
Di Xiao ◽  
Janardhan Vignarajan ◽  
Amita Preetham ◽  
Mei-Ling Tay-Kearney ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Primary Care ◽  
Diabetic Retinopathy

scholarly journals Diagnosing Diabetic Retinopathy With Artificial Intelligence: What Information Should Be Included to Ensure Ethical Informed Consent?

2021 ◽  
Vol 8 ◽  
Author(s):  
Frank Ursin ◽  
Cristian Timmermann ◽  
Marcin Orzechowski ◽  
Florian Steger
Keyword(s):  
Artificial Intelligence ◽  
Primary Care ◽  
Diabetic Retinopathy ◽  
Informed Consent ◽  
Care Setting ◽  
Primary Care Setting ◽  
Information Process ◽  
Novel Technologies ◽  
Ethical Implications ◽  
Aided Diagnosis

Purpose: The method of diagnosing diabetic retinopathy (DR) through artificial intelligence (AI)-based systems has been commercially available since 2018. This introduces new ethical challenges with regard to obtaining informed consent from patients. The purpose of this work is to develop a checklist of items to be disclosed when diagnosing DR with AI systems in a primary care setting.Methods: Two systematic literature searches were conducted in PubMed and Web of Science databases: a narrow search focusing on DR and a broad search on general issues of AI-based diagnosis. An ethics content analysis was conducted inductively to extract two features of included publications: (1) novel information content for AI-aided diagnosis and (2) the ethical justification for its disclosure.Results: The narrow search yielded n = 537 records of which n = 4 met the inclusion criteria. The information process was scarcely addressed for primary care setting. The broad search yielded n = 60 records of which n = 11 were included. In total, eight novel elements were identified to be included in the information process for ethical reasons, all of which stem from the technical specifics of medical AI.Conclusions: Implications for the general practitioner are two-fold: First, doctors need to be better informed about the ethical implications of novel technologies and must understand them to properly inform patients. Second, patient's overconfidence or fears can be countered by communicating the risks, limitations, and potential benefits of diagnostic AI systems. If patients accept and are aware of the limitations of AI-aided diagnosis, they increase their chances of being diagnosed and treated in time.

scholarly journals The Real-World Impact of Artificial Intelligence on Diabetic Retinopathy Screening in Primary Care

2020 ◽  
pp. 193229682091428
Author(s):  
Jorge Cuadros
Keyword(s):  
Artificial Intelligence ◽  
Primary Care ◽  
Diabetic Retinopathy ◽  
Real World ◽  
Screening Program ◽  
High Sensitivity ◽  
Primary Care Clinics ◽  
Diabetic Retinopathy Screening ◽  
Significant Burden ◽  
And Control

The study by Shah et al published in this issue of the Journal of Diabetes Science and Technology validates the IDx autonomous diabetic retinopathy (DR) screening program in a real-world setting. The study found high sensitivity (100%) but low specificity (82%) for referable DR. The resulting positive predictive value of 19% means that four out of five patients without referable DR would be referred to ophthalmology causing a significant burden to ophthalmologists, primary care clinics, and patients. Artificial intelligence programs that provide better specificity, multiple levels of DR, and annotations of where lesions are located in the retina may function better than a simple referral/no referral output. This will allow for better engagement of patients through the difficult process of adhering to treatment recommendations and control their diabetes.

scholarly journals Artificial Intelligence for the Detection of Diabetic Retinopathy in Primary Care: Protocol for Algorithm Development

10.2196/12539 ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. e12539 ◽  
Author(s):  
Josep Vidal-Alaball ◽  
Dídac Royo Fibla ◽  
Miguel A Zapata ◽  
Francesc X Marin-Gomez ◽  
Oscar Solans Fernandez
Keyword(s):  
Artificial Intelligence ◽  
Primary Care ◽  
Diabetic Retinopathy ◽  
Algorithm Development ◽  
Care Protocol

scholarly journals Artificial Intelligence for the Detection of Diabetic Retinopathy in Primary Care: Protocol for Algorithm Development (Preprint)

2018 ◽  
Author(s):  
Josep Vidal-Alaball ◽  
Dídac Royo Fibla ◽  
Miguel A Zapata ◽  
Francesc X Marin-Gomez ◽  
Oscar Solans Fernandez
Keyword(s):  
Artificial Intelligence ◽  
Primary Care ◽  
Diabetic Retinopathy ◽  
Prospective Study ◽  
Confusion Matrix ◽  
False Negative ◽  
Superior Performance ◽  
Validation Dataset ◽  
Diabetic Patients ◽  
Care Protocol

BACKGROUND Diabetic retinopathy (DR) is one of the most important causes of blindness worldwide, especially in developed countries. In diabetic patients, periodic examination of the back of the eye using a nonmydriatic camera has been widely demonstrated to be an effective system to control and prevent the onset of DR. Convolutional neural networks have been used to detect DR, achieving very high sensitivities and specificities. OBJECTIVE The objective of this is paper was to develop an artificial intelligence (AI) algorithm for the detection of signs of DR in diabetic patients and to scientifically validate the algorithm to be used as a screening tool in primary care. METHODS Under this project, 2 studies will be conducted in a concomitant way: (1) Development of an algorithm with AI to detect signs of DR in patients with diabetes and (2) A prospective study comparing the diagnostic capacity of the AI algorithm with respect to the actual system of family physicians evaluating the images. The standard reference to compare with will be a blinded double reading conducted by retina specialists. For the development of the AI algorithm, different iterations and workouts will be performed on the same set of data. Before starting each new workout, the strategy of dividing the set date into 2 groups will be used randomly. A group with 80% of the images will be used during the training (training dataset), and the remaining 20% images will be used to validate the results (validation dataset) of each cycle (epoch). During the prospective study, true-positive, true-negative, false-positive, and false-negative values will be calculated again. From here, we will obtain the resulting confusion matrix and other indicators to measure the performance of the algorithm. RESULTS Cession of the images began at the end of 2018. The development of the AI algorithm is calculated to last about 3 to 4 months. Inclusion of patients in the cohort will start in early 2019 and is expected to last 3 to 4 months. Preliminary results are expected to be published by the end of 2019. CONCLUSIONS The study will allow the development of an algorithm based on AI that can demonstrate an equal or superior performance, and that constitutes a complement or an alternative, to the current screening of DR in diabetic patients. INTERNATIONAL REGISTERED REPOR PRR1-10.2196/12539

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