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Applied Artificial Intelligence for Ophthalmic Fields: Clinical Aspects
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Applied Artificial Intelligence for Ophthalmic Fields: Clinical Aspects

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Ophthalmology".

Deadline for manuscript submissions: closed (30 October 2021) | Viewed by 4952

Special Issue Editor

Dr. Hitoshi Tabuchi

E-Mail Website
Guest Editor
1. Department of Ophthalmology, Tsukazaki Hospital, 68-1 Waku, Aboshi-ku, Himeji 671-1227, Japan
2. Department of Technology and Design Thinking for Medicine, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima 734-8551, Japan
Interests: cataract surgery; ICT; ophthalmology; clinical surgery; refractive Surgery; ocular circulation; strabismus; amblyopia; neuro-ophthalmology; pediatric ophthalmology; ophthalmic surgery; glaucoma; retina
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It was at the end of 2016 that a team from Google published a paper on the application of deep learning to the diagnosis of diabetic retinopathy.

It has been less than five years since then, and there have been numerous studies reported on the application of deep learning in all areas of ophthalmology. On the other hand, however, the use of AI for diagnosis in actual medical practice has not been straightforward.

One of the major problems with diagnostic deep learning is the large number of false positives. If the prior probability in the population to be diagnosed is high, this is unlikely to be a problem, but, in a population with low prior probability, deep learning diagnosis is useless in the real world.

I believe that we are now at the stage of discussing how to frame the application of deep learning for ophthalmic diagnosis, with the aim of implementing it in the medical real world.

As Guest Editor, I look forward to receiving research that aims to create more practical applications.

Dr. Hitoshi Tabuchi
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • deep learning
  • machine learning
  • ophthalmology
  • retina
  • cornea
  • glaucoma
  • cataract
  • strabismus

Published Papers (2 papers)

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12 pages, 1510 KiB  
Article
Use of a Machine Learning Method in Predicting Refraction after Cataract Surgery
by Tomofusa Yamauchi, Hitoshi Tabuchi, Kosuke Takase and Hiroki Masumoto
J. Clin. Med. 2021, 10(5), 1103; https://doi.org/10.3390/jcm10051103 - 06 Mar 2021
Cited by 9 | Viewed by 1992
Abstract
The present study aims to describe the use of machine learning (ML) in predicting the occurrence of postoperative refraction after cataract surgery and compares the accuracy of this method to conventional intraocular lens (IOL) power calculation formulas. In total, 3331 eyes from 2010 [...] Read more.
The present study aims to describe the use of machine learning (ML) in predicting the occurrence of postoperative refraction after cataract surgery and compares the accuracy of this method to conventional intraocular lens (IOL) power calculation formulas. In total, 3331 eyes from 2010 patients were assessed. The objects were divided into training data and test data. The constants for the IOL power calculation formulas and model training for ML were optimized using training data. Then, the occurrence of postoperative refraction was predicted using conventional formulas, or ML models were calculated using the test data. We evaluated the SRK/T formula, Haigis formula, Holladay 1 formula, Hoffer Q formula, and Barrett Universal II formula (BU-II); similar to ML methods, we assessed support vector regression (SVR), random forest regression (RFR), gradient boosting regression (GBR), and neural network (NN). Among the conventional formulas, BU-II had the lowest mean and median absolute error of prediction. Therefore, we compared the accuracy of our method with that of BU-II. The absolute errors of some ML methods were lower than those of BU-II. However, no statistically significant difference was observed. Thus, the accuracy of our method was not inferior to that of BU-II. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence for Ophthalmic Fields: Clinical Aspects)
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10 pages, 256 KiB  
Opinion
Artificial Intelligence, Heuristic Biases, and the Optimization of Health Outcomes: Cautionary Optimism
by Michael Feehan, Leah A. Owen, Ian M. McKinnon and Margaret M. DeAngelis
J. Clin. Med. 2021, 10(22), 5284; https://doi.org/10.3390/jcm10225284 - 14 Nov 2021
Cited by 6 | Viewed by 2472
Abstract
The use of artificial intelligence (AI) and machine learning (ML) in clinical care offers great promise to improve patient health outcomes and reduce health inequity across patient populations. However, inherent biases in these applications, and the subsequent potential risk of harm can limit [...] Read more.
The use of artificial intelligence (AI) and machine learning (ML) in clinical care offers great promise to improve patient health outcomes and reduce health inequity across patient populations. However, inherent biases in these applications, and the subsequent potential risk of harm can limit current use. Multi-modal workflows designed to minimize these limitations in the development, implementation, and evaluation of ML systems in real-world settings are needed to improve efficacy while reducing bias and the risk of potential harms. Comprehensive consideration of rapidly evolving AI technologies and the inherent risks of bias, the expanding volume and nature of data sources, and the evolving regulatory landscapes, can contribute meaningfully to the development of AI-enhanced clinical decision making and the reduction in health inequity. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence for Ophthalmic Fields: Clinical Aspects)
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