Comparison of state-of-the-art machine and deep learning algorithms to classify proximal humeral fractures using radiology text

Dipnall, Joanna F.; Lu, Jueqing; Gabbe, Belinda; Cosic, Filip; Edwards, Elton; Page, Richard; Du, Lan

doi:10.1016/j.ejrad.2022.110366

Cited by 13 publications

(4 citation statements)

References 34 publications

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“…It has also been trained to detect data elements from medical records. One prior study trained a BERT model to automatically detect proximal humeral fractures from radiology reports [6]. In addition, these authors developed different rule-based NLP algorithms and compared them to the BERT.…”

Section: Nlp-based Medical Record Analysismentioning

confidence: 99%

A practical guide to the development and deployment of deep learning models for the orthopaedic surgeon: Part III, focus on registry creation, diagnosis, and data privacy

Oeding,

Yang,

Sanchez‐Sotelo

et al. 2024

Knee surg. sports traumatol. arthrosc.

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Deep learning is a subset of artificial intelligence (AI) with enormous potential to transform orthopaedic surgery. As has already become evident with the deployment of Large Language Models (LLMs) like ChatGPT (OpenAI Inc.), deep learning can rapidly enter clinical and surgical practices. As such, it is imperative that orthopaedic surgeons acquire a deeper understanding of the technical terminology, capabilities and limitations associated with deep learning models. The focus of this series thus far has been providing surgeons with an overview of the steps needed to implement a deep learning‐based pipeline, emphasizing some of the important technical details for surgeons to understand as they encounter, evaluate or lead deep learning projects. However, this series would be remiss without providing practical examples of how deep learning models have begun to be deployed and highlighting the areas where the authors feel deep learning may have the most profound potential. While computer vision applications of deep learning were the focus of Parts I and II, due to the enormous impact that natural language processing (NLP) has had in recent months, NLP‐based deep learning models are also discussed in this final part of the series. In this review, three applications that the authors believe can be impacted the most by deep learning but with which many surgeons may not be familiar are discussed: (1) registry construction, (2) diagnostic AI and (3) data privacy. Deep learning‐based registry construction will be essential for the development of more impactful clinical applications, with diagnostic AI being one of those applications likely to augment clinical decision‐making in the near future. As the applications of deep learning continue to grow, the protection of patient information will become increasingly essential; as such, applications of deep learning to enhance data privacy are likely to become more important than ever before.Level of Evidence: Level IV.

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Section: Nlp-based Medical Record Analysismentioning

confidence: 99%

A practical guide to the development and deployment of deep learning models for the orthopaedic surgeon: Part III, focus on registry creation, diagnosis, and data privacy

Oeding,

Yang,

Sanchez‐Sotelo

et al. 2024

Knee surg. sports traumatol. arthrosc.

View full text Add to dashboard Cite

show abstract

“…The overall AUC for fracture classification was 0.89, including excellent AUC for diaphyseal humerus fractures (0.97), clavicle fractures (0.96), and good AUC for scapula fractures (0.87), which showed that the proposed model could effectively utilize plain radiographs and classify fractures. Dipnall et al [68] assessed the classification performance of several ML algorithms based on the Neer classification system from six input text datasets, including X-ray and/or CT scan data and patient age and/or sex information. They declared that these ML algorithms achieved satisfactory performance, with one special model exhibiting good accuracy at 61% and an excellent One-versusrest score above 0.8, providing valuable assistance to radiologists and orthopedists by speeding up the classification process.…”

Section: Proximal Humeral Fractures (Phfs)mentioning

confidence: 99%

Application of Artificial Intelligence in Shoulder Pathology

Cheng,

Liang,

Guo

et al. 2024

Preprint

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The artificial intelligence (AI) refers to the science and engineering of creating intelligent machines for imitating and expanding human intelligence. Given the ongoing evolution of the multidisciplinary integration trend in modern medicine, numerous studies have investigated the power of AI to address orthopaedic-specific problems. One particular area of investigation focuses on the shoulder pathology, which is a range of disorders or abnormalities of shoulder joint, causing pain, inflammation, stiffness, weakness, and reduced range of motion. There has not yet been a comprehensive review of the recent advancements in this field. Therefore, the purpose of this review is to evaluate the current AI applications in the shoulder pathology. This review mainly summarizes several crucial stages of the clinical practice, including predictive models and prognosis, diagnosis, treatment, and physical therapy. In addition, the challenges and future development of AI are also discussed.

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“…The overall AUC for fracture classification was 0.89, including excellent AUC for diaphyseal humerus fractures (0.97), clavicle fractures (0.96), and good AUC for scapula fractures (0.87), which showed that the proposed model could effectively utilize plain radiographs and classify fractures. Dipnall et al [ 67 ] assessed the classification performance of several ML algorithms based on the Neer classification system from six input text datasets, including X-ray and/or CT scan data and patient age and/or sex information. They declared that these ML algorithms achieved satisfactory performance, with one special model exhibiting good accuracy at 61% and an excellent one-versus-rest score above 0.8, providing valuable assistance to radiologists and orthopedists by speeding up the classification process.…”

Section: Proximal Humeral Fractures (Phfs)mentioning

confidence: 99%

Application of Artificial Intelligence in Shoulder Pathology

Cheng,

Liang,

Guo

et al. 2024

Diagnostics

View full text Add to dashboard Cite

Artificial intelligence (AI) refers to the science and engineering of creating intelligent machines for imitating and expanding human intelligence. Given the ongoing evolution of the multidisciplinary integration trend in modern medicine, numerous studies have investigated the power of AI to address orthopedic-specific problems. One particular area of investigation focuses on shoulder pathology, which is a range of disorders or abnormalities of the shoulder joint, causing pain, inflammation, stiffness, weakness, and reduced range of motion. There has not yet been a comprehensive review of the recent advancements in this field. Therefore, the purpose of this review is to evaluate current AI applications in shoulder pathology. This review mainly summarizes several crucial stages of the clinical practice, including predictive models and prognosis, diagnosis, treatment, and physical therapy. In addition, the challenges and future development of AI technology are also discussed.

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Comparison of state-of-the-art machine and deep learning algorithms to classify proximal humeral fractures using radiology text

Cited by 13 publications

References 34 publications

A practical guide to the development and deployment of deep learning models for the orthopaedic surgeon: Part III, focus on registry creation, diagnosis, and data privacy

A practical guide to the development and deployment of deep learning models for the orthopaedic surgeon: Part III, focus on registry creation, diagnosis, and data privacy

Application of Artificial Intelligence in Shoulder Pathology

Application of Artificial Intelligence in Shoulder Pathology

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