Primary bone tumor detection and classification in full-field bone radiographs via YOLO deep learning model

Li, Jie; Li, Sudong; Li, Xiaoli; Miao, Sheng; Dong, Cheng; Gao, Chuanping; Liu, Xuejun; Hao, Dapeng; Xu, Wenjian; Huang, Mingqian; Cui, Jiufa

doi:10.1007/s00330-022-09289-y

Cited by 19 publications

(12 citation statements)

References 29 publications

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“…However, the diagnostic accuracy of ultrasound depends critically upon image quality, the professional experience of the radiologists, and the ultrasound instrument itself 28 , 29 . Object detector-based deep learning mode was used in detecting, segmenting, and classifying on lesions 30 . Before the emergence of YOLO, object detection algorithms such as DCNN generally required generating a large number of candidate regions and then classifying targets among them.…”

Section: Discussionmentioning

confidence: 99%

Diagnosis of cervical lymphoma using a YOLO-v7-based model with transfer learning

Wang,

Yang,

Yang

et al. 2024

Sci Rep

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To investigate the ability of an auxiliary diagnostic model based on the YOLO-v7-based model in the classification of cervical lymphadenopathy images and compare its performance against qualitative visual evaluation by experienced radiologists. Three types of lymph nodes were sampled randomly but not uniformly. The dataset was randomly divided into for training, validation, and testing. The model was constructed with PyTorch. It was trained and weighting parameters were tuned on the validation set. Diagnostic performance was compared with that of the radiologists on the testing set. The mAP of the model was 96.4% at the 50% intersection-over-union threshold. The accuracy values of it were 0.962 for benign lymph nodes, 0.982 for lymphomas, and 0.960 for metastatic lymph nodes. The precision values of it were 0.928 for benign lymph nodes, 0.975 for lymphomas, and 0.927 for metastatic lymph nodes. The accuracy values of radiologists were 0.659 for benign lymph nodes, 0.836 for lymphomas, and 0.580 for metastatic lymph nodes. The precision values of radiologists were 0.478 for benign lymph nodes, 0.329 for lymphomas, and 0.596 for metastatic lymph nodes. The model effectively classifies lymphadenopathies from ultrasound images and outperforms qualitative visual evaluation by experienced radiologists in differential diagnosis.

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Section: Discussionmentioning

confidence: 99%

Diagnosis of cervical lymphoma using a YOLO-v7-based model with transfer learning

Wang,

Yang,

Yang

et al. 2024

Sci Rep

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“…Our proposed DL model identified the tumor area and its surrounding normal bone in the full field of the radiograph, rather than only choosing the tumor area [ 18 , 21 ], avoiding the need for tumor area labeling. Additionally, the DL model takes into account not only the bone tumor itself but also the shape and size of the bone in which the tumor is located, thereby capturing important variations associated with age.…”

Section: Discussionmentioning

confidence: 99%

“…As an emerging machine learning technology, deep learning (DL) has been widely applied to medical image analysis of bone tumors [ 15 – 17 ], which can distinguish benign and malignant bone tumors [ 11 , 12 , 14 , 18 – 21 ]. A recent study has highlighted DL’s potential in effectively classifying healthy and pathological X-rays in children [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

“…A recent study has highlighted DL’s potential in effectively classifying healthy and pathological X-rays in children [ 22 ]. However, previous studies have primarily focused on the benign and malignant classification of various bone tumors at different sites throughout the body [ 15 , 18 , 21 ], rather than specifically addressing the differential diagnosis of osteolytic OS and GCT around the knee joint. To our knowledge, there have been no reported studies utilizing DL for this specific purpose.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning for differentiation of osteolytic osteosarcoma and giant cell tumor around the knee joint on radiographs: a multicenter study

Shao,

Lin,

Ding

et al. 2024

Insights Imaging

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Objectives To develop a deep learning (DL) model for differentiating between osteolytic osteosarcoma (OS) and giant cell tumor (GCT) on radiographs. Methods Patients with osteolytic OS and GCT proven by postoperative pathology were retrospectively recruited from four centers (center A, training and internal testing; centers B, C, and D, external testing). Sixteen radiologists with different experiences in musculoskeletal imaging diagnosis were divided into three groups and participated with or without the DL model’s assistance. DL model was generated using EfficientNet-B6 architecture, and the clinical model was trained using clinical variables. The performance of various models was compared using McNemar’s test. Results Three hundred thirty-three patients were included (mean age, 27 years ± 12 [SD]; 186 men). Compared to the clinical model, the DL model achieved a higher area under the curve (AUC) in both the internal (0.97 vs. 0.77, p = 0.008) and external test set (0.97 vs. 0.64, p < 0.001). In the total test set (including the internal and external test sets), the DL model achieved higher accuracy than the junior expert committee (93.1% vs. 72.4%; p < 0.001) and was comparable to the intermediate and senior expert committee (93.1% vs. 88.8%, p = 0.25; 87.1%, p = 0.35). With DL model assistance, the accuracy of the junior expert committee was improved from 72.4% to 91.4% (p = 0.051). Conclusion The DL model accurately distinguished osteolytic OS and GCT with better performance than the junior radiologists, whose own diagnostic performances were significantly improved with the aid of the model, indicating the potential for the differential diagnosis of the two bone tumors on radiographs. Critical relevance statement The deep learning model can accurately distinguish osteolytic osteosarcoma and giant cell tumor on radiographs, which may help radiologists improve the diagnostic accuracy of two types of tumors. Key points • The DL model shows robust performance in distinguishing osteolytic osteosarcoma and giant cell tumor. • The diagnosis performance of the DL model is better than junior radiologists’. • The DL model shows potential for differentiating osteolytic osteosarcoma and giant cell tumor. Graphical Abstract

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“…As a one-step detector, YOLO can simultaneously perform object classification and object detection from a single image. Recently, YOLOv5 was released by Glenn Jocher [32], and several studies applying it to breast cancer [33] and bone tumors [34] show good accuracy and precision.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Analysis of the Median Nerve in Carpal Tunnel Syndrome from Ultrasound Images Using the YOLOv5 Object Detection Model

Tanaka,

Inui,

Mifune

et al. 2023

Applied Sciences

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Carpal tunnel syndrome (CTS) is caused by subsynovial connective tissue fibrosis, resulting in median nerve (MN) mobility. The standard evaluation method is the measurement of the MN cross-sectional area using static images, and dynamic images are not widely used. In recent years, remarkable progress has been made in the field of deep learning (DL) in medical image processing. The aim of the present study was to evaluate MN dynamics in CTS hands using the YOLOv5 model, which is one of the object detection models of DL. We included 20 normal hands (control group) and 20 CTS hands (CTS group). We obtained ultrasonographic short-axis images of the carpal tunnel and the MN and recorded MN motion during finger flexion–extension, and evaluated MN displacement and velocity. The YOLOv5 model showed a score of 0.953 for precision and 0.956 for recall. The radial–ulnar displacement of the MN was 3.56 mm in the control group and 2.04 mm in the CTS group, and the velocity of the MN was 4.22 mm/s in the control group and 3.14 mm/s in the CTS group. The scores were significantly reduced in the CTS group. This study demonstrates the potential of DL-based dynamic MN analysis as a powerful diagnostic tool for CTS.

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Primary bone tumor detection and classification in full-field bone radiographs via YOLO deep learning model

Cited by 19 publications

References 29 publications

Diagnosis of cervical lymphoma using a YOLO-v7-based model with transfer learning

Diagnosis of cervical lymphoma using a YOLO-v7-based model with transfer learning

Deep learning for differentiation of osteolytic osteosarcoma and giant cell tumor around the knee joint on radiographs: a multicenter study

Dynamic Analysis of the Median Nerve in Carpal Tunnel Syndrome from Ultrasound Images Using the YOLOv5 Object Detection Model

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