Automatic detection of early osteonecrosis of the femoral head from various hip pathologies using deep convolutional neural network: a multi-centre study

Shen, Xianyue; He, Ziling; Shi, Yi; Yang, Yuhui; Luo, Jia; Tang, Xiongfeng; Chen, Bin; Liu, Tong; Xu, Shenghao; Xiao, Jianlin; Zhou, You; Qin, Yanguo

doi:10.1007/s00264-023-05813-x

Cited by 6 publications

(17 citation statements)

References 35 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%

“…The main purposes of developing deep learning models in the included studies were to diagnose ONFH or early ONFH from normal hips (n = 5) [33,40,42,43,46] and to classify the severity (or stages) of the ONFH (n = 8) [33, 35, 38-41, 43, 47]. Three studies attempted to differentiate ONFH from other hip pathologies [34,37,45] and two studies distinguished pre-collapse ONFH from normal hips [36,40]. Zhu, et al [48] predicted the risk of ONFH after operation for patients with femoral neck fractures, whereas Liu, et al [41] predicted the symptoms of ONFH, including cystic transformation, trabecular bone disorder, edema, and Line-like sign.…”

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%

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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%