SLAP Injury of the Shoulder Joint: Application Value of Deep Learning in Diagnosis
29 de junio de 2021 actualizado por: Peking University Third Hospital
This study intends to study the shoulder SLAP injury through deep learning technology and establish a deep learning model through the combination of axial and oblique coronal images to establish a deep learning method that can accurately identify and grade shoulder SLAP injury.
Descripción general del estudio
Estado
Aún no reclutando
Intervención / Tratamiento
Descripción detallada
- Recognition of labrum images based on LeNet: axial and oblique coronal T2-fs images were used, and all images were corrected and standardized. LeNet identified the images with labrum of the shoulder joint, and the images with labrum structure of shoulder joint were selected from the complete sequence. In contrast, the images without labrum structure were deleted. All the data are divided into a training set (70%, 30% in training set as verification set), and the remaining 30% as a test set to evaluate the accuracy of model recognition. Enter the obtained results into the next step.
- Recognition and segmentation of glenoid lip of shoulder joint based on DenseNet: the labrum is recognized by DenseNet in the selected image. The labelimg software based on Python was used to locate the labrum coordinates and then input them into Python for recognition learning. All the data were divided into a training set (70% and 30% of the training set were selected as the verification set). The remaining 30% was used as the test set to evaluate the accuracy of model recognition. After identifying the labrum structure, the labrum structure is locally cut and enlarged to remove the redundant information and improve the recognition efficiency and accuracy. Finally, input the result to the next step.
- Recognition and grading of shoulder SLAP injury based on 3D-CNN: recognition and grading of input data through 3D-CNN model. 3D-CNN is divided into eight layers: input layer, hard wire layer H1, convolution layer C2, downsampling layer S3, convolution layer C4, downsampling layer S5, convolution layer C6 and output layer. 3D-CNN constructs a cube by stacking multiple consecutive frames and then uses a 3D convolution kernel in the cube. Through this structure, the feature images in the convolution layer will be connected with multiple adjacent frames in the previous layer to realize the information acquisition of continuous images. Similarly, the data is divided into a training set (70%, and then 30% of the training set is selected as the verification set), and the remaining 30% is used as the test set to evaluate the classification accuracy to identify whether there is labrum injury and grade the image with injury.
- Establish CNN combined model: after establishing the model for the axial and oblique coronal view according to the above process (1-3), according to the output characteristics of the CNN classification model, predict the probability of different grades before the output results, and the output results are based on these probabilities to select the expression form of the maximum possible probability. Our combined model averages the probabilities of these different classifications, calculates the final prediction probability, and then obtains the final joint model. The test set of the third step (including the mixed data of axial and coronal images) was used to verify the joint model.
Tipo de estudio
De observación
Inscripción (Anticipado)
800
Contactos y Ubicaciones
Esta sección proporciona los datos de contacto de quienes realizan el estudio e información sobre dónde se lleva a cabo este estudio.
Estudio Contacto
- Nombre: huishu Yuan, MD
- Número de teléfono: 15810245738
- Correo electrónico: huishuy@bjmu.edu.cn
Copia de seguridad de contactos de estudio
- Nombre: Ming Ni, MD
- Número de teléfono: +8613884794867
- Correo electrónico: sdyingxiang2017@163.com
Ubicaciones de estudio
-
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Beijing
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Beijing, Beijing, Porcelana, 010
- Peking University Third Hospital
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-
Criterios de participación
Los investigadores buscan personas que se ajusten a una determinada descripción, denominada criterio de elegibilidad. Algunos ejemplos de estos criterios son el estado de salud general de una persona o tratamientos previos.
Criterio de elegibilidad
Edades elegibles para estudiar
- Niño
- Adulto
- Adulto Mayor
Acepta Voluntarios Saludables
N/A
Géneros elegibles para el estudio
Todos
Método de muestreo
Muestra no probabilística
Población de estudio
Collect and analyze patients who underwent shoulder MR examinations in the Department of Radiology, Peking University Third Hospital from September 2018 to September 2020.
Descripción
Inclusion Criteria:
- Without any treatment before imaging examination;
- MR of the shoulder joint was performed within 3 months before the operation and the image quality was good;
- Arthroscopic operation was performed in our hospital, and the operation records were complete.
Exclusion Criteria:
- History of shoulder surgery, tumor, or previous fracture;
- Unclear image, serious artifact, or incomplete clinical data.
Plan de estudios
Esta sección proporciona detalles del plan de estudio, incluido cómo está diseñado el estudio y qué mide el estudio.
¿Cómo está diseñado el estudio?
Detalles de diseño
- Modelos observacionales: Control de caso
- Perspectivas temporales: Retrospectivo
Cohortes e Intervenciones
Grupo / Cohorte |
Intervención / Tratamiento |
|---|---|
|
Normal control group-Grade 0
Arthroscopic examination of the labrum was normal, and the labrum was intact without injury or tear.
|
The results of shoulder arthroscopy were taken as the gold standard, and MRI examination was taken as the research object.
|
|
Ligament injury -Grade 1
Arthroscopic examination of the shoulder showed labrum degeneration or injury, but no local or complete tear.
|
The results of shoulder arthroscopy were taken as the gold standard, and MRI examination was taken as the research object.
|
|
Ligament tear-Grade 2
Arthroscopy of the shoulder revealed partial or complete loss of labrum.
|
The results of shoulder arthroscopy were taken as the gold standard, and MRI examination was taken as the research object.
|
¿Qué mide el estudio?
Medidas de resultado primarias
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
|---|---|---|
|
SLAP Injury of the Shoulder Joint: Application Value of Deep Learning in Diagnosis
Periodo de tiempo: 2021.10.1-2022.7.1
|
The model of deep learning was obtained for diagnosis and grading of SLAP injury and compared with the radiologists of different stages.
|
2021.10.1-2022.7.1
|
Colaboradores e Investigadores
Aquí es donde encontrará personas y organizaciones involucradas en este estudio.
Patrocinador
Fechas de registro del estudio
Estas fechas rastrean el progreso del registro del estudio y los envíos de resultados resumidos a ClinicalTrials.gov. Los registros del estudio y los resultados informados son revisados por la Biblioteca Nacional de Medicina (NLM) para asegurarse de que cumplan con los estándares de control de calidad específicos antes de publicarlos en el sitio web público.
Fechas importantes del estudio
Inicio del estudio (Anticipado)
1 de octubre de 2021
Finalización primaria (Anticipado)
1 de junio de 2022
Finalización del estudio (Anticipado)
1 de julio de 2022
Fechas de registro del estudio
Enviado por primera vez
29 de junio de 2021
Primero enviado que cumplió con los criterios de control de calidad
29 de junio de 2021
Publicado por primera vez (Actual)
7 de julio de 2021
Actualizaciones de registros de estudio
Última actualización publicada (Actual)
7 de julio de 2021
Última actualización enviada que cumplió con los criterios de control de calidad
29 de junio de 2021
Última verificación
1 de junio de 2021
Más información
Términos relacionados con este estudio
Palabras clave
Términos MeSH relevantes adicionales
Otros números de identificación del estudio
- M2020458
Plan de datos de participantes individuales (IPD)
¿Planea compartir datos de participantes individuales (IPD)?
No
Información sobre medicamentos y dispositivos, documentos del estudio
Estudia un producto farmacéutico regulado por la FDA de EE. UU.
No
Estudia un producto de dispositivo regulado por la FDA de EE. UU.
No
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