Deep Learning for the Automatic Diagnosis and Analysis of Bone Metastasis on Bone Scintigrams

This study aimed to explore efficient ways to diagnose bone metastasis early using bone scintigraphy images through negative mining, pre-training, the convolutional neural network, and deep learning. We studied 205 prostate cancer patients and 371 breast cancer patients and used bone scintigraphy data from breast cancer patients to pre-train a YOLO v4 with a false-positive reduction strategy. With the pre-trained model, transferred learning was applied to prostate cancer patients to build a model to detect and identify metastasis locations using bone scintigraphy. Ten-fold cross validation was conducted. The mean sensitivity and precision rates for bone metastasis location detection and classification (lesion-based) in the chests of prostate patients were 0.72 ± 0.04 and 0.90 ± 0.04, respectively. The mean sensitivity and specificity rates for bone metastasis classification (patient-based) in the chests of prostate patients were 0.94 ± 0.09 and 0.92 ± 0.09, respectively. The developed system has the potential to provide pre-diagnostic reports to aid in physicians’ final decisions.

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A Study on 3D Deep Learning-Based Automatic Diagnosis of Nasal Fractures

Sensors ◽

10.3390/s22020506 ◽

2022 ◽

Vol 22 (2) ◽

pp. 506

Author(s):

Yu-Jin Seol ◽

Young-Jae Kim ◽

Yoon-Sang Kim ◽

Young-Woo Cheon ◽

Kwang-Gi Kim

Keyword(s):

Deep Learning ◽

Binary Classification ◽

Characteristic Curve ◽

Nasal Bone ◽

Facial Trauma ◽

Future Research ◽

Automatic Diagnosis ◽

3 Dimensional ◽

Computed Tomography Images ◽

Nasal Fractures

This paper reported a study on the 3-dimensional deep-learning-based automatic diagnosis of nasal fractures. (1) Background: The nasal bone is the most protuberant feature of the face; therefore, it is highly vulnerable to facial trauma and its fractures are known as the most common facial fractures worldwide. In addition, its adhesion causes rapid deformation, so a clear diagnosis is needed early after fracture onset. (2) Methods: The collected computed tomography images were reconstructed to isotropic voxel data including the whole region of the nasal bone, which are represented in a fixed cubic volume. The configured 3-dimensional input data were then automatically classified by the deep learning of residual neural networks (3D-ResNet34 and ResNet50) with the spatial context information using a single network, whose performance was evaluated by 5-fold cross-validation. (3) Results: The classification of nasal fractures with simple 3D-ResNet34 and ResNet50 networks achieved areas under the receiver operating characteristic curve of 94.5% and 93.4% for binary classification, respectively, both indicating unprecedented high performance in the task. (4) Conclusions: In this paper, it is presented the possibility of automatic nasal bone fracture diagnosis using a 3-dimensional Resnet-based single classification network and it will improve the diagnostic environment with future research.

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Automatic Diagnosis of Covid-19 Using Chest X-ray Images Through Deep Learning Models

10.1007/978-981-16-5078-9_26 ◽

2021 ◽

pp. 303-312

Author(s):

Siddharth Gupta ◽

Palak Aggarwal ◽

Sumeshwar Singh ◽

Shiv Ashish Dhondiyal ◽

Manisha Aeri ◽

...

Keyword(s):

Deep Learning ◽

Learning Models ◽

Automatic Diagnosis ◽

X Ray ◽

Chest X Ray

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BreakHis based breast cancer automatic diagnosis using deep learning: Taxonomy, survey and insights

Neurocomputing ◽

10.1016/j.neucom.2019.09.044 ◽

2020 ◽

Vol 375 ◽

pp. 9-24 ◽

Cited By ~ 13

Author(s):

Yassir Benhammou ◽

Boujemâa Achchab ◽

Francisco Herrera ◽

Siham Tabik

Keyword(s):

Breast Cancer ◽

Deep Learning ◽

Automatic Diagnosis

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3D-Deep Learning Based Automatic Diagnosis of Alzheimer’s Disease with Joint MMSE Prediction Using Resting-State fMRI

Neuroinformatics ◽

10.1007/s12021-019-09419-w ◽

2019 ◽

Vol 18 (1) ◽

pp. 71-86 ◽

Cited By ~ 11

Author(s):

Nguyen Thanh Duc ◽

Seungjun Ryu ◽

Muhammad Naveed Iqbal Qureshi ◽

Min Choi ◽

Kun Ho Lee ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Deep Learning ◽

Resting State ◽

Resting State Fmri ◽

Automatic Diagnosis

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Literature Survey on Diabetic Retinopathy Classification Using Deep Learning

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.37733 ◽

2021 ◽

Vol 9 (8) ◽

pp. 2259-2262

Author(s):

Alfiya Md. Shaikh

Keyword(s):

Diabetic Retinopathy ◽

Deep Learning ◽

Transfer Learning ◽

Medical Condition ◽

Literature Survey ◽

Fundus Images ◽

Fundus Image ◽

Automatic Diagnosis ◽

Global Blindness

Abstract: Diabetic retinopathy (DR) is a medical condition that damages eye retinal tissues. Diabetic retinopathy leads to mild to complete blindness. It has been a leading cause of global blindness. The identification and categorization of DR take place through the segmentation of parts of the fundus image or the examination of the fundus image for the incidence of exudates, lesions, microaneurysms, and so on. This research aims to study and summarize various recent proposed techniques applied to automate the process of classification of diabetic retinopathy. In the current study, the researchers focused on the concept of classifying the DR fundus images based on their severity level. Emphasis is on studying papers that proposed models developed using transfer learning. Thus, it becomes vital to develop an automatic diagnosis system to support physicians in their work.

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Constructing an automatic diagnosis and severity-classification model for acromegaly using facial photographs by deep learning

Journal of Hematology & Oncology ◽

10.1186/s13045-020-00925-y ◽

2020 ◽

Vol 13 (1) ◽

Author(s):

Yanguo Kong ◽

Xiangyi Kong ◽

Cheng He ◽

Changsong Liu ◽

Liting Wang ◽

...

Keyword(s):

Deep Learning ◽

Classification Model ◽

Automatic Diagnosis ◽

Severity Classification

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Study of the Usefulness of Bone Scan Index Calculated From 99m-Technetium-Hydroxymethylene Diphosphonate (99mtc-HMDP) Bone Scintigraphy for Bone Metastases from Prostate Cancer Using Deep Learning Algorithms

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405616666200528153453 ◽

2020 ◽

Vol 16 ◽

Author(s):

Shigeaki Higashiyama ◽

Atsushi Yoshida ◽

Joji Kawabe

Keyword(s):

Prostate Cancer ◽

Deep Learning ◽

Bone Metastasis ◽

Bone Metastases ◽

Bone Scintigraphy ◽

Bone Scan ◽

Learning Algorithms ◽

Bone Imaging ◽

Bone Scan Index ◽

Metabolic Biomarkers

Background: BSI calculated from bone scintigraphy using 99mtechnetium-methylene diphosphonate (99mTc-MDP) is used as a quantitative indicator of metastatic bone involvement in bone metastasis diagnosis, therapeutic effect assessment, and prognosis prediction. However, the BONE NAVI, which calculates BSI, only supports bone scintigraphy using 99mTc-MDP. Aims: We developed a method in collaboration with the Tokyo University of Agriculture and Technology to calculate bone scan index (BSI) employing deep learning algorithms with bone scintigraphy images using 99mtechnetiumhydroxymethylene diphosphonate (99mTc-HMDP). We used a convolutional neural network (CNN) enabling the simultaneous processing of anterior and posterior bone scintigraphy images named CNNapis. Objectives: The purpose of this study is to investigate the usefulness of the BSI calculated by CNNapis as bone imaging and bone metabolic biomarkers in patients with bone metastases from prostate cancer. Methods: At our hospital, 121 bone scintigraphy scans using 99mTc-HMDP were performed and analyzed to examine bone metastases from prostate cancer, revealing the abnormal accumulation of radioisotope (RI) at bone metastasis sites. Blood tests for serum prostate-specific antigen (PSA) and alkaline phosphatase (ALP) were performed concurrently. BSI values calculated by CNNapis were used to quantify the metastatic bone tumor involvement. Correlations between BSI and PSA and between BSI and ALP were calculated. Subjects were divided into four groups by BSI values (Group 1, 0 to <1; Group 2, 1 to <3; Group 3, 3 to <10; Group 4, >10), and the PSA and ALP values in each group were statistically compared. Results: Patients diagnosed with bone metastases after bone scintigraphy were also diagnosed with bone metastases using CNNapis. BSI corresponding to the range of abnormal RI accumulation was calculated. PSA and BSI (r = 0.2791) and ALP and BSI (r = 0.6814) correlated positively. Significant intergroup differences in PSA between Groups 1 and 2, Groups 1 and 4, Groups 2 and 3, and Groups 3 and 4 and in ALP between Groups 1 and 4, Groups 2 and 4, and Groups 3 and 4 were found. Conclusion : BSI calculated using CNNapis correlated with ALP and PSA values and is useful as bone imaging and bone metabolic biomarkers, indicative of the activity and spread of bone metastases from prostate cancer.

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