Deep learning enables the differentiation between early and late stages of hip avascular necrosis

Klontzas, Michail E.; Vassalou, Evangelia E.; Spanakis, Konstantinos; Meurer, Felix; Woertler, Klaus; Zibis, Aristeidis; Marias, Kostas; Karantanas, Apostolos H.

doi:10.1007/s00330-023-10104-5

Cited by 7 publications

(21 citation statements)

References 27 publications

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“…One more study was added from grey literature search. Finally, sixteen papers (n = 16) were eligible for data synthesis [33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48].…”

Section: Search Resultsmentioning

confidence: 99%

“…The authors of 16 articles were from four countries: China [34,[39][40][41][43][44][45][46][47][48], South Korea [33,35,36], Greece [37,38] and the United States [42]. Among the studies, two research groups, one from Greece [37,38] and one from China [43][44][45] published more than one article. All studies were published in or after 2019, except one that was published in 1991 [42] (Table 1).…”

Section: A Basic Informationmentioning

confidence: 99%

“…As shown in Table 1 and Fig. 2, magnetic resonance (MR) imaging was the most common modality (n = 10) [35,[37][38][39][41][42][43][44][45][46], while some investigations used digital radiographs or X-rays (n = 5) [33,34,36,40,48]. One study utilized computed tomography (CT) [47].…”

Section: A Basic Informationmentioning

confidence: 99%

“…Out of the 16 papers that met the eligibility criteria for this review, only one did not report the number of participants or hips [42]. The remaining 15 papers accounted for a total of 73,341 hips, among which 19,168 belonged to ONFH patients, 47,389 to normal participants, and 6784 to patients with other hip pathologies [33][34][35][36][37][38][39][40][41][43][44][45][46][47][48]. Although 14 papers provided patient-level information, gender information was disclosed in only eight of them (Table 2).…”

Section: ) Data Source and Participantsmentioning

confidence: 99%

“…All datasets used in the studies were sourced and/or collected from hospitals or clinics, with the exception of one study that did not provide any information regarding the participant source and the sampling approach [42]. Of the remaining 15 studies, participants were recruited from a single center in 8 studies [35, 36, 39-41, 44, 46, 47], while the other 7 studies enrolled participants from multiple institutions [33,34,37,38,43,45,48]. Three of these studies (n = 3) select an institution from the multiple institutions to serve as the external testing ground for their models [34,38,43].…”

Section: ) Data Source and Participantsmentioning

confidence: 99%

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Applications of Deep Learning Models on the Medical Images of Osteonecrosis of the Femoral Head (ONFH): A Comprehensive Review

Wang,

Zheng,

Luo

et al. 2024

IEEE Access

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Deep learning models have demonstrated promising results in the early and accurate diagnosis of osteonecrosis of the femoral head (ONFH), enabling early detection and informed surgical decisionmaking. The objective of this review is to summarize the applications of deep learning models on the medical images of ONFH. English papers were searched from CINAHL via EBSCOhost, Embase, IEEE Xplore® Digital Library, PubMed, Scopus, and Web of Science. Sixteen studies (n = 16) were eligible for data synthesis. Among these, five studies (n = 5) focusing on radiographs, ten studies (n = 10) focusing on magnetic resonance imaging, and one study (n = 1) focusing on computed tomographic images. The applications of these studies included identifying ONFH from normal or other hip pathologies, classifying severity, segmenting, and detecting femoral head and necrotic regions, predicting signs and symptoms of ONFH, and predicting potential ONFH after fracture fixation. Generally, the models demonstrated good to excellent classification performance and excellent discriminatory power; and generally comparable to that of experienced physicians and superior to that of less experienced physicians. However, the external validity of these studies demonstrated only moderate, as evidenced by the performance on the external testing set and might be attributed to the relatively small data size used during model training. we observed a shift from CNN-based models to U-Net based models (i.e., with encoder-decoder architecture). In addition to streamlining the segmentation, detection, and classification procedures, future studies will explore multimodal attention, self-supervised learning, explainable models, and data augmentation through generative models.

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“…One more study was added from grey literature search. Finally, sixteen papers (n = 16) were eligible for data synthesis [33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48].…”

Section: Search Resultsmentioning

confidence: 99%

Section: A Basic Informationmentioning

confidence: 99%

Section: A Basic Informationmentioning

confidence: 99%

Section: ) Data Source and Participantsmentioning

confidence: 99%

Section: ) Data Source and Participantsmentioning

confidence: 99%

See 3 more Smart Citations

Applications of Deep Learning Models on the Medical Images of Osteonecrosis of the Femoral Head (ONFH): A Comprehensive Review

Wang,

Zheng,

Luo

et al. 2024

IEEE Access

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Detecting Avascular Necrosis of the Lunate from Radiographs Using a Deep-Learning Model

Wernér,

Anttila,

Hulkkonen

et al. 2024

J Digit Imaging. Inform. med.

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Deep-learning (DL) algorithms have the potential to change medical image classification and diagnostics in the coming decade. Delayed diagnosis and treatment of avascular necrosis (AVN) of the lunate may have a detrimental effect on patient hand function. The aim of this study was to use a segmentation-based DL model to diagnose AVN of the lunate from wrist postero-anterior radiographs. A total of 319 radiographs of the diseased lunate and 1228 control radiographs were gathered from Helsinki University Central Hospital database. Of these, 10% were separated to form a test set for model validation. MRI confirmed the absence of disease. In cases of AVN of the lunate, a hand surgeon at Helsinki University Hospital validated the accurate diagnosis using either MRI or radiography. For detection of AVN, the model had a sensitivity of 93.33% (95% confidence interval (CI) 77.93–99.18%), specificity of 93.28% (95% CI 87.18–97.05%), and accuracy of 93.28% (95% CI 87.99–96.73%). The area under the receiver operating characteristic curve was 0.94 (95% CI 0.88–0.99). Compared to three clinical experts, the DL model had better AUC than one clinical expert and only one expert had higher accuracy than the DL model. The results were otherwise similar between the model and clinical experts. Our DL model performed well and may be a future beneficial tool for screening of AVN of the lunate.

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Multi-sequence MRI-based radiomics: An objective method to diagnose early-stage osteonecrosis of the femoral head

Wang,

Sun,

Zhang

et al. 2024

European Journal of Radiology

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Deep learning enables the differentiation between early and late stages of hip avascular necrosis

Cited by 7 publications

References 27 publications

Applications of Deep Learning Models on the Medical Images of Osteonecrosis of the Femoral Head (ONFH): A Comprehensive Review

Applications of Deep Learning Models on the Medical Images of Osteonecrosis of the Femoral Head (ONFH): A Comprehensive Review

Detecting Avascular Necrosis of the Lunate from Radiographs Using a Deep-Learning Model

Multi-sequence MRI-based radiomics: An objective method to diagnose early-stage osteonecrosis of the femoral head

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