The characteristics of positive and confusing hand X-ray signs in diagnosing Kashin–Beck Disease in children in China

Song, Quanquan; Liu, Hui; Lian, Wei; Wang, Yue; Sun, Lingyun; Gao, Mei; Liu, Yunqi; Deng, Hongxia; Deng, Qi; Liu, Ning; Yu, Jun

doi:10.1038/s41598-018-21675-5

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…14 Data science methods and traditional machine learning methods can also be used to make diagnoses 15,16 and deep learning techniques have been used to aid the diagnosis of KBD. 17 However, due to the challenging features of positive hand X-ray signs in diseased children, 18,19 there is significant scope for improving the extraction and utilization of image information.…”

Section: Introductionmentioning

confidence: 99%

A method for Kashin‐Beck disease auxiliary diagnosis based on the features in regions of the potential lesion

Qiao

Niu

et al. 2023

Medical Physics

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BackgroundKashin‐Beck disease (KBD) is a severe arthropathy that causes deformity. Patients with advanced stages of KBD often show symptoms, such as short stature. Early‐stage diagnosis and treatment can effectively prevent the disease from worsening. Diagnosis of early‐stage patients is usually made by X‐ray examination. However, the time‐consuming image recognition and the lack of professional doctors may delay the patient's condition. Therefore, a method that can efficiently complete the auxiliary diagnosis is necessary.PurposeThis study presents a KBD auxiliary diagnosis method based on radiographs, which uses deep learning to locate potential lesion regions and extract features for accurate diagnosis.MethodsThis work presents a method that relies on hand radiographs to locate eight regions of the potential lesion (RoPL) and finally make the KBD auxiliary diagnosis. The localization of RoPL is achieved through a two‐step model, with the first step predicting a rough location and a deep convolutional neural network (DCNN) with attention mechanism used to generate precise center coordinates based on the previous step's results.Based on the localization result, regional features are extracted, which provides information about the joints and textures of RoPL from a finer granularity. Another DCNN is utilized to obtain general features from hand radiographs, which provide morphological and structural information about the entire hand bone These features offer a concatenated feature for categorization to raise accuracy. A doctor‐like approach is adopted to diagnose based on regional features to enhance performance, and a final decision is made using a vote that considers diagnostic outcomes from all aspects.The dataset used in our study was collected by our research team in KBD‐endemic areas of Tibet since 2017, containing 373 diseased and 764 normal images.ResultOur model guarantees that over 95% of the predicted coordinates are within five pixels of the real coordinates according to Euclidean distance. The accuracy of the diagnostic network achieved 91.3%, with precision and recall achieving 83% and 87%, respectively. Compared to the approach without exact localization of the illness region on the same test set, our method achieved a roughly 6% increase in accuracy and nearly 30% increase in recall rate.ConclusionsBased on hand radiographs, this study suggests a novel method for KBD diagnosis. The high‐precision localization network guarantees precise extraction of lesion‐prone regional features, and the multi‐scale features and innovative classification method further enhance model performance compared to related approaches.

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Section: Introductionmentioning

confidence: 99%

A method for Kashin‐Beck disease auxiliary diagnosis based on the features in regions of the potential lesion

Qiao

Niu

et al. 2023

Medical Physics

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“…Ultrasound is also widely used in the diagnosis of musculoskeletal diseases in children and adolescents because of its advantages of being fast and radiation-free. However, for older children with osteochondral structure becoming more mature, ultrasound is limited in the range of display by bone shadow, resulting in the overlapping phenomenon and affecting the diagnosis results [7,8]. CT examination has a good display effect on fracture fragments and has a good guiding role in preoperative evaluation and planning.…”

Section: Introductionmentioning

confidence: 99%

Diagnostic Value of Magnetic Resonance Image Feature Analysis under Reconstruction Algorithm for Knee Epiphyseal Injury

Tang

Zhou

et al. 2022

Scientific Programming

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To analyze the diagnostic value of magnetic resonance imaging (MRI) in epiphyseal injury of adolescent children, MRI images of 26 adolescent knee joint injuries were selected. The image display before and after reconstruction based on the compressed sensing theory (CS) algorithm was compared and analyzed, so did the signal-to-noise ratio of knee MRI reconstructed images under 2D random sampling and radial sampling at different sampling rates. The results showed that the sharpness, specificity, and accuracy of images reconstructed by the CS algorithm were significantly higher than those before reconstruction P < 0.05 . When the sampling rates were almost the same (20%, 40%, and 60%), the signal-to-noise ratio (SNR) of the reconstructed knee MRI images by random sampling was higher than that by radial sampling (about 9%, 6%, and 3% higher, respectively), that is, the information contained by random sampling was larger than that by radial sampling. It overcame the defects of undisplaced epiphyseal injuries in children and adolescents that cannot be found by plain X-ray films and can clearly and accurately diagnose epiphyseal injuries and fracture types that cannot be seen by plain X-ray films. To sum up, the MRI image reconstruction algorithm based on compressed sensing theory can effectively improve the diagnosis effect of knee epiphyseal injury.

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“…At present, the diagnosis of KBD mainly depends on the X-ray examination [13][14][15][16] which could not reflect early damage of growth plate and cartilage sensitively [17]. A latest systematic review found that the X-ray detective rate of KBD was 11% depending on the study [18].…”

Section: Introductionmentioning

confidence: 99%

Magnetic resonance imaging at 7.0 T for evaluation of early lesions of epiphyseal plate and epiphyseal end in a rat model of Kashin-Beck disease

Kang

Zhou

et al. 2020

BMC Musculoskelet Disord

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Background: Kashin-Beck disease (KBD) is a disabling osteoarticular disease involving growth and joint cartilage. Early diagnosis can effectively prevent the progress of the disease. However, the early diagnosis of it is still very difficult. Our aim was to study the knee joint lesions of a rat KBD model using ultra-high field magnetic resonance imaging (MRI) and compare it with X-ray imaging to analyze the possible MRI manifestations of KBD, and to further explore ways to determine the pathological damage of KBD in the early stage. Methods: A total of 96 Wistar rats were selected and randomly divided into 4 groups: normal diet (Group A), KBDaffected diet (Group B), normal diet+T-2 toxin (Group C), and KBD-affected diet+T-2 toxin (Group D). T-2 toxin was administered at a dose of 0.1 mg/kg/day. In the 4th week, 8th week, and 12th week, eight rats randomly selected in each group were sacrificed by cervical dislocation after undergoing X-ray and 7.0 T MRI imaging, and then knee joints were harvested, sliced, and subjected to hematoxylin-eosin (H&E) staining. Results: Characteristic image changes including of continuity interruption and early closure and fusion of epiphyseal plates were observed on T1WI in rat model of KBD. The total necrosis rates in the H&E stain of group A to group D were 4.35, 52.38, 33.3, and 73.68%, respectively. The positive rate of image change under 7.0 T MRI was 0.833 VS. that under X-ray was 0.33 (P = 0.001). Conclusions: MRI at 7.0 T is highly sensitive to the early pathological changes of the epiphysis, epiphyseal plate, and metaphyseal end, which can improve imaging positive rate of KBD and decrease the rate of missed diagnosis. This imaging modality can be used for research on early joint lesions and for early diagnosis of KBD.

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The characteristics of positive and confusing hand X-ray signs in diagnosing Kashin–Beck Disease in children in China

Cited by 7 publications

References 7 publications

A method for Kashin‐Beck disease auxiliary diagnosis based on the features in regions of the potential lesion

A method for Kashin‐Beck disease auxiliary diagnosis based on the features in regions of the potential lesion

Diagnostic Value of Magnetic Resonance Image Feature Analysis under Reconstruction Algorithm for Knee Epiphyseal Injury

Magnetic resonance imaging at 7.0 T for evaluation of early lesions of epiphyseal plate and epiphyseal end in a rat model of Kashin-Beck disease

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