Detection of ankle fractures using deep learning algorithms

Ashkani-Esfahani, Soheil; Yazdi, Reza Mojahed; Bhimani, Rohan; Kerkhoffs, Gino M. M. J.; Maas, Mario; DiGiovanni, Christopher W.; Lubberts, Bart; Guss, Daniel

doi:10.1016/j.fas.2022.05.005

Cited by 26 publications

(15 citation statements)

References 22 publications

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“…Another study on ankle fracture detection using deep learning algorithms found that InceptionV3 model had high specificity and sensitivity 71 . Similarly, InceptionV3 was able to identify skin cancer-melanoma with high accuracy 72 .…”

Section: Discussionmentioning

confidence: 99%

Machine learning to identify clinically relevantCandidayeast species

Shankarnarayan,

Charlebois

2023

Preprint

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BackgroundFungal infections, especially due toCandidaspecies, are on the rise. Multi-drug resistant organism such asCandida aurisare difficult and time consuming to identify accurately.Machine learning is increasingly being used in health care, especially in medical imaging. In this study, we evaluated the effectiveness of six convolutional neural networks (CNNs) to identify four clinically importantCandidaspecies.Materials and MethodsWet-mounted images were captured using bright field live-cell microscopy followed by separating single cells, budding cells, and cell group images which were then subjected to different machine learning algorithms (custom CNN, VGG16, ResNet50, InceptionV3, EfficientNetB0, and EfficientNetB7) to learn and predictCandidaspecies.ResultsAmong the six algorithms tested, the InceptionV3 model performed best in predictingCandidaspecies from microscopy images. All models performed poorly on raw images obtained directly from the microscope. The performance of all models increased when trained on single and budding cell images. The InceptionV3 model identified budding cells ofC. albicans, C. auris,C. glabrata(Nakaseomyces glabrata), andC. haemuloniiin 97.0%, 74.0%, 68.0%, and 66.0% cases, respectively. For single cells ofC. albicans, C. auris,C. glabrata, andC. haemuloniiInceptionV3 identified 97.0%, 73.0%, 69.0%, and 73.0% cases, respectively. The sensitivity and specificity of InceptionV3 were respectively 77.1% and 92.4%.ConclusionThis study provides proof of concept that microscopy images from wet mounted slides can be used to identifyCandidayeast species using machine learning quickly and accurately.

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Section: Discussionmentioning

confidence: 99%

Machine learning to identify clinically relevantCandidayeast species

Shankarnarayan,

Charlebois

2023

Preprint

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“…They internally validated each of the CNNs and noted a fair CNN fracture detection accuracy of 81%. Ashkani-Esfahani et al 2 internally validated 2 deep CNNs for the same purpose and achieved a near-perfect area under the curve of 0.99. AI has been used for image analysis throughout foot and ankle surgery, including for Achilles tendinopathy, 21 stress fracture, 22 Lisfranc fracture, 1 and calcaneal fracture.…”

Section: Discussionmentioning

confidence: 99%

Evaluating the Quality and Usability of Artificial Intelligence–Generated Responses to Common Patient Questions in Foot and Ankle Surgery

Anastasio,

Mills,

Karavan

et al. 2023

Foot & Ankle Orthopaedics

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Background: Artificial intelligence (AI) platforms, such as ChatGPT, have become increasingly popular outlets for the consumption and distribution of health care–related advice. Because of a lack of regulation and oversight, the reliability of health care–related responses has become a topic of controversy in the medical community. To date, no study has explored the quality of AI-derived information as it relates to common foot and ankle pathologies. This study aims to assess the quality and educational benefit of ChatGPT responses to common foot and ankle–related questions. Methods: ChatGPT was asked a series of 5 questions, including “What is the optimal treatment for ankle arthritis?” “How should I decide on ankle arthroplasty versus ankle arthrodesis?” “Do I need surgery for Jones fracture?” “How can I prevent Charcot arthropathy?” and “Do I need to see a doctor for my ankle sprain?” Five responses (1 per each question) were included after applying the exclusion criteria. The content was graded using DISCERN (a well-validated informational analysis tool) and AIRM (a self-designed tool for exercise evaluation). Results: Health care professionals graded the ChatGPT-generated responses as bottom tier 4.5% of the time, middle tier 27.3% of the time, and top tier 68.2% of the time. Conclusion: Although ChatGPT and other related AI platforms have become a popular means for medical information distribution, the educational value of the AI-generated responses related to foot and ankle pathologies was variable. With 4.5% of responses receiving a bottom-tier rating, 27.3% of responses receiving a middle-tier rating, and 68.2% of responses receiving a top-tier rating, health care professionals should be aware of the high viewership of variable-quality content easily accessible on ChatGPT. Level of Evidence: Level III, cross sectional study.

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“…Eight studies reported accuracies, for which the best values ranged from 72% to 99%. Ashkani-Esfahani et al 5 internally validated 2 deep convolutional neural networks (DCNNs) for identifying ankle fractures from radiographs and achieved a nearperfect AUC of 0.99. Kitamura et al 26 internally validated 5 separate CNNs for detecting ankle fractures from plain radiographs and achieved a fair fracture detection accuracy of 81%.…”

Section: Image Interpretationmentioning

confidence: 99%

Advancements in Artificial Intelligence for Foot and Ankle Surgery: A Systematic Review

Gupta

Kingston

O’Malley

et al. 2023

Foot & Ankle Orthopaedics

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Background: There has been a rapid increase in research applying artificial intelligence (AI) to various subspecialties of orthopaedic surgery, including foot and ankle surgery. The purpose of this systematic review is to (1) characterize the topics and objectives of studies using AI in foot and ankle surgery, (2) evaluate the performance of their models, and (3) evaluate their validity (internal or external validation). Methods: A systematic literature review was conducted using PubMed/MEDLINE and Embase databases in December 2022. All studies that used AI or its subsets machine learning (ML) and deep learning (DL) in the setting of foot and ankle surgery relevant to orthopaedic surgeons were included. Studies were evaluated for their demographics, subject area, outcomes of interest, model(s) tested, model(s)’ performance, and validity (internal or external). Results: A total of 31 studies met inclusion criteria: 14 studies investigated AI for image interpretation, 13 studies investigated AI for clinical predictions, and 4 studies were grouped as “other.” Studies commonly explored AI for ankle fractures, calcaneus fractures, hallux valgus, Achilles tendon pathologies, plantar fasciitis, and sports injuries. For studies reporting the area under the receiver operating characteristic curve (AUC), AUCs ranged from 0.64 (poor) to 0.99 (excellent). Two studies (6.45%) reported external validation. Conclusion: Applications of AI in the field of foot and ankle surgery are expanding, particularly for image interpretation and clinical predictions. Current model performances range from poor to excellent, and most studies lack external validation, demonstrating a need for further research prior to deploying AI-based clinical applications. Level of Evidence: Level III, retrospective cohort study.

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Detection of ankle fractures using deep learning algorithms

Cited by 26 publications

References 22 publications

Machine learning to identify clinically relevantCandidayeast species

Machine learning to identify clinically relevantCandidayeast species

Evaluating the Quality and Usability of Artificial Intelligence–Generated Responses to Common Patient Questions in Foot and Ankle Surgery

Advancements in Artificial Intelligence for Foot and Ankle Surgery: A Systematic Review

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