Expert Performance in Visual Differentiation of Bacterial and Fungal Keratitis

Redd, Travis K.; Prajna, Namperumalsamy Venkatesh; Srinivasan, Muthiah; Lalitha, Prajna; Krishnan, Tiruvengada; Rajaraman, Revathi; Anitha, Venugopal; Lujan, Brandon J.; Acharya, Nisha R.; Seitzman, Gerami D.; Rose‐Nussbaumer, Jennifer; Lietman, Thomas M.; Campbell, J. Peter; Keenan, Jeremy D.; Alvarez-Melloni, Diana; Ayalew, Menen; Balasubramanian, Ashwin; Chan, Elsie; Chan, Matilda F.; Chaudhary, Meenu; Chia, Thomas H.; Chodosh, James; Choong, Yean Yaw; Christenbury, Joseph G.; Christy, Josephine S; Clements, John L; Dart, John; Dastjerdi, Mohammad H.; Denny, Matthew R; Devarajan, Sathish; Elghobaier, Mohamed Gamal; Estopinal, Chris; Gandhi, Preethika; Gokhale, Nikhil S; Halfpenny, Colleen; Hazarbassanov, Rossen Mihaylov; Hernandez, Natalie; Hovakimyan, Anna; Hwang, David G.; Hwang, Frank S.; Jaeschke, Tomas; Jhanji, Vishal; Karas, Faris I.; Karthik, Divya; Kase, Camila; Kattana, Lakshmi; Kim, Tyson; Koreishi, Aaleya; Liang, David Tee; Martinez, Christine E.; Martínez-Costa, Rafael; McLeod, Stephen D.; Moss, A.; Nanji, Afshan; Nataneli, Nathan; Panday, Vasudha; Pradhan, Sayali; Qian, Ying; Rao, Naveen; Schallhorn, Julie M.; Sella, Ruti; Selvaraj, Suvitha; Spokes, David; Shaik, Neha; Shekhawat, Nakul; Sugar, Alan; Rostov, Audrey Talley; Tananuvat, Napaporn; Tangmonkongvoragul, Chulaluck; Trinh, Tanya; Tuli, Sonal S.; Upaphong, Phit; Dooren, Bart Van; Vasudevan, Manoj; Viriya, Elizabeth; Woodward, Maria A.

doi:10.1016/j.ophtha.2021.09.019

Cited by 17 publications

(10 citation statements)

References 7 publications

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“…However, the clinical features are often poorly differentiated, especially in patients with late presentation (common in LMIC populations), and may be complicated by the presence of polymicrobial infections (occurring in 2–15%) (Ting et al, 2019a , 2021a ; Khoo et al, 2020 ). Several studies have highlighted the inability of corneal experts to accurately determine the underlying cause of IK (based on clinical photographs) in >50% cases, highlighting the diagnostic challenges based on clinical presentation alone (Redd et al, 2022a ).…”

Section: Current Diagnostic Approach and Limitationsmentioning

confidence: 99%

Achieving diagnostic excellence for infectious keratitis: A future roadmap

2022

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Section: Current Diagnostic Approach and Limitationsmentioning

confidence: 99%

Achieving diagnostic excellence for infectious keratitis: A future roadmap

2022

Self Cite

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“…Xu et al [ 37 ] compared three image-level algorithms for the classification of BK, FK, HSK, and other corneal disorders, with the DenseNet model achieving optimal accuracy (64.17%), which was superior to 421 ophthalmologists (49.27 ± 11.5%). In addition, a large international study [ 38 ] quantified the performance of 66 cornea specialists in the image-based differentiation of BK and FK, with AUCs of 0.39–0.82 (mean of 0.61).…”

Section: Resultsmentioning

confidence: 99%

Automatic Diagnosis of Infectious Keratitis Based on Slit Lamp Images Analysis

Sun

et al. 2023

JPM

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Infectious keratitis (IK) is a common ophthalmic emergency that requires prompt and accurate treatment. This study aimed to propose a deep learning (DL) system based on slit lamp images to automatically screen and diagnose infectious keratitis. This study established a dataset of 2757 slit lamp images from 744 patients, including normal cornea, viral keratitis (VK), fungal keratitis (FK), and bacterial keratitis (BK). Six different DL algorithms were developed and evaluated for the classification of infectious keratitis. Among all the models, the EffecientNetV2-M showed the best classification ability, with an accuracy of 0.735, a recall of 0.680, and a specificity of 0.904, which was also superior to two ophthalmologists. The area under the receiver operating characteristics curve (AUC) of the EffecientNetV2-M was 0.85; correspondingly, 1.00 for normal cornea, 0.87 for VK, 0.87 for FK, and 0.64 for BK. The findings suggested that the proposed DL system could perform well in the classification of normal corneas and different types of infectious keratitis, based on slit lamp images. This study proves the potential of the DL model to help ophthalmologists to identify infectious keratitis and improve the accuracy and efficiency of diagnosis.

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“…Organisms cause different, but often overlapping, morphologic characteristics. The combination of organisms, patient inflammatory responses, and circumstances of the infection, lead to clinical presentations that make determination of the underlying organism difficult, even for cornea specialists [19]. Artificial intelligence algorithms have the potential to guide clinicians to aid point-of-care diagnosis.…”

Section: Methodsmentioning

confidence: 99%

Artificial intelligence and corneal diseases

Kang¹,

Ballouz²,

Woodward

2022

Current Opinion in Ophthalmology

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Purpose of reviewArtificial intelligence has advanced rapidly in recent years and has provided powerful tools to aid with the diagnosis, management, and treatment of ophthalmic diseases. This article aims to review the most current clinical artificial intelligence applications in anterior segment diseases, with an emphasis on microbial keratitis, keratoconus, dry eye syndrome, and Fuchs endothelial dystrophy.Recent findingsMost current artificial intelligence approaches have focused on developing deep learning algorithms based on various imaging modalities. Algorithms have been developed to detect and differentiate microbial keratitis classes and quantify microbial keratitis features. Artificial intelligence may aid with early detection and staging of keratoconus. Many advances have been made to detect, segment, and quantify features of dry eye syndrome and Fuchs. There is significant variability in the reporting of methodology, patient population, and outcome metrics.SummaryArtificial intelligence shows great promise in detecting, diagnosing, grading, and measuring diseases. There is a need for standardization of reporting to improve the transparency, validity, and comparability of algorithms.

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Expert Performance in Visual Differentiation of Bacterial and Fungal Keratitis

Cited by 17 publications

References 7 publications

Achieving diagnostic excellence for infectious keratitis: A future roadmap

Achieving diagnostic excellence for infectious keratitis: A future roadmap

Automatic Diagnosis of Infectious Keratitis Based on Slit Lamp Images Analysis

Artificial intelligence and corneal diseases

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