A deep learning system for automated, multi-modality 2D segmentation of vertebral bodies and intervertebral discs

Background/aimsTo apply deep learning technology to develop an artificial intelligence (AI) system that can identify vision-threatening conditions in high myopia patients based on optical coherence tomography (OCT) macular images.MethodsIn this cross-sectional, prospective study, a total of 5505 qualified OCT macular images obtained from 1048 high myopia patients admitted to Zhongshan Ophthalmic Centre (ZOC) from 2012 to 2017 were selected for the development of the AI system. The independent test dataset included 412 images obtained from 91 high myopia patients recruited at ZOC from January 2019 to May 2019. We adopted the InceptionResnetV2 architecture to train four independent convolutional neural network (CNN) models to identify the following four vision-threatening conditions in high myopia: retinoschisis, macular hole, retinal detachment and pathological myopic choroidal neovascularisation. Focal Loss was used to address class imbalance, and optimal operating thresholds were determined according to the Youden Index.ResultsIn the independent test dataset, the areas under the receiver operating characteristic curves were high for all conditions (0.961 to 0.999). Our AI system achieved sensitivities equal to or even better than those of retina specialists as well as high specificities (greater than 90%). Moreover, our AI system provided a transparent and interpretable diagnosis with heatmaps.ConclusionsWe used OCT macular images for the development of CNN models to identify vision-threatening conditions in high myopia patients. Our models achieved reliable sensitivities and high specificities, comparable to those of retina specialists and may be applied for large-scale high myopia screening and patient follow-up.

Download Full-text

A deep-learning system to classify lung X-ray images into normal/pneumonia class

International Journal of Infectious Diseases ◽

10.1016/j.ijid.2020.09.556 ◽

2020 ◽

Vol 101 ◽

pp. 209

Author(s):

R. Baskaran ◽

B. Ajay Rajasekaran ◽

V. Rajinikanth

Keyword(s):

Deep Learning ◽

Learning System ◽

X Ray

Download Full-text

Endoscopic prediction of submucosal invasion in Barrett’s cancer with the use of Artificial Intelligence: A pilot Study

Endoscopy ◽

10.1055/a-1311-8570 ◽

2020 ◽

Author(s):

Alanna Ebigbo ◽

Robert Mendel ◽

Tobias Rückert ◽

Laurin Schuster ◽

Andreas Probst ◽

...

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Pilot Study ◽

White Light ◽

Submucosal Invasion ◽

Learning System ◽

Endoscopic Images ◽

Barrett’S Cancer ◽

Significant Difference ◽

Sensitivity Specificity

Background and aims: The accurate differentiation between T1a and T1b Barrett’s cancer has both therapeutic and prognostic implications but is challenging even for experienced physicians. We trained an Artificial Intelligence (AI) system on the basis of deep artificial neural networks (deep learning) to differentiate between T1a and T1b Barrett’s cancer white-light images. Methods: Endoscopic images from three tertiary care centres in Germany were collected retrospectively. A deep learning system was trained and tested using the principles of cross-validation. A total of 230 white-light endoscopic images (108 T1a and 122 T1b) was evaluated with the AI-system. For comparison, the images were also classified by experts specialized in endoscopic diagnosis and treatment of Barrett’s cancer. Results: The sensitivity, specificity, F1 and accuracy of the AI-system in the differentiation between T1a and T1b cancer lesions was 0.77, 0.64, 0.73 and 0.71, respectively. There was no statistically significant difference between the performance of the AI-system and that of human experts with sensitivity, specificity, F1 and accuracy of 0.63, 0.78, 0.67 and 0.70 respectively. Conclusion: This pilot study demonstrates the first multicenter application of an AI-based system in the prediction of submucosal invasion in endoscopic images of Barrett’s cancer. AI scored equal to international experts in the field, but more work is necessary to improve the system and apply it to video sequences and in a real-life setting. Nevertheless, the correct prediction of submucosal invasion in Barret´s cancer remains challenging for both experts and AI.

Download Full-text

Towards Underwater Sustainability using ROV Equipped with Deep Learning System

2020 International Automatic Control Conference (CACS) ◽

10.1109/cacs50047.2020.9289788 ◽

2020 ◽

Author(s):

Yi-Chia Wu ◽

Po-Yen Shih ◽

Li-Perng Chen ◽

Chia-Chin Wang ◽

Hooman Samani

Keyword(s):

Deep Learning ◽

Learning System

Download Full-text

Minimally invasive debridement and drainage using intraoperative CT-Guide in multilevel spondylodiscitis: a long‐term follow‐up study

BMC Musculoskeletal Disorders ◽

10.1186/s12891-021-03988-1 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Jianbiao Xu ◽

Leiming Zhang ◽

Rongqiang Bu ◽

Yankang Liu ◽

Kai-Uwe Lewandrowski ◽

...

Keyword(s):

Minimally Invasive ◽

Clinical Manifestations ◽

Operation Time ◽

Intervertebral Discs ◽

Intraoperative Ct ◽

Long Term Follow Up ◽

Invasive Method ◽

Vertebral Bodies ◽

Adjacent Segments

Abstract Background Spondylodiscitis is an unusual infectious disease, which usually originates as a pathogenic infection of intervertebral discs and then spreads to neighboring vertebral bodies. The objective of this study is to evaluate percutaneous debridement and drainage using intraoperative CT-Guide in multilevel spondylodiscitis. Methods From January 2002 to May 2017, 23 patients with multilevel spondylodiscitis were treated with minimally invasive debridement and drainage procedures in our department. The clinical manifestations, evolution, and minimally invasive debridement and drainage treatment of this refractory vertebral infection were investigated. Results Of the enrolled patients, the operation time ranged from 30 minutes to 124 minutes every level with an average of 48 minutes. Intraoperative hemorrhage was minimal. The postoperative follow-up period ranged from 12 months to 6.5 years with an average of 3.7 years. There was no reactivation of infection in the treated vertebral segment during follow-up, but two patients with fungal spinal infection continued to progress by affecting adjacent segments prior to final resolution. According to the classification system of Macnab, one patient had a good outcome at the final follow-up, and the rest were excellent. Conclusions Minimally invasive percutaneous debridement and irrigation using intraoperative CT-Guide is an effective minimally invasive method for the treatment of multilevel spondylodiscitis.

Download Full-text

Detection and classification of unilateral cleft alveolus with and without cleft palate on panoramic radiographs using a deep learning system

Scientific Reports ◽

10.1038/s41598-021-95653-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Chiaki Kuwada ◽

Yoshiko Ariji ◽

Yoshitaka Kise ◽

Takuma Funakoshi ◽

Motoki Fukuda ◽

...

Keyword(s):

Deep Learning ◽

Cleft Palate ◽

Panoramic Radiography ◽

False Positive Rate ◽

Normal Subjects ◽

Learning System ◽

Panoramic Radiographs ◽

Positive Rate ◽

Aided Diagnosis

AbstractAlthough panoramic radiography has a role in the examination of patients with cleft alveolus (CA), its appearances is sometimes difficult to interpret. The aims of this study were to develop a computer-aided diagnosis system for diagnosing the CA status on panoramic radiographs using a deep learning object detection technique with and without normal data in the learning process, to verify its performance in comparison to human observers, and to clarify some characteristic appearances probably related to the performance. The panoramic radiographs of 383 CA patients with cleft palate (CA with CP) or without cleft palate (CA only) and 210 patients without CA (normal) were used to create two models on the DetectNet. The models 1 and 2 were developed based on the data without and with normal subjects, respectively, to detect the CAs and classify them into with or without CP. The model 2 reduced the false positive rate (1/30) compared to the model 1 (12/30). The overall accuracy of Model 2 was higher than Model 1 and human observers. The model created in this study appeared to have the potential to detect and classify CAs on panoramic radiographs, and might be useful to assist the human observers.

Download Full-text

Deep learning system compared with expert endoscopists in predicting early gastric cancer and its invasion depth and differentiation status (with videos)

Gastrointestinal Endoscopy ◽

10.1016/j.gie.2021.06.033 ◽

2021 ◽

Author(s):

Lianlian Wu ◽

Jing Wang ◽

Xinqi He ◽

Yijie Zhu ◽

Xiaoda Jiang ◽

...

Keyword(s):

Gastric Cancer ◽

Deep Learning ◽

Early Gastric Cancer ◽

Learning System ◽

Invasion Depth ◽

Differentiation Status

Download Full-text

Pyogenic and tuberculous discitis: magnetic resonance imaging findings for differential diagnosis

Radiologia Brasileira ◽

10.1590/s0100-39842013000300012 ◽

2013 ◽

Vol 46 (3) ◽

pp. 173-177 ◽

Cited By ~ 9

Author(s):

Cristiano Gonzaga de Souza ◽

Emerson Leandro Gasparetto ◽

Edson Marchiori ◽

Paulo Roberto Valle Bahia

Keyword(s):

Magnetic Resonance Imaging ◽

Magnetic Resonance ◽

Correct Diagnosis ◽

Cord Compression ◽

Intervertebral Discs ◽

University Hospital ◽

Imaging Findings ◽

Resonance Imaging ◽

Radiographic Findings ◽

Vertebral Bodies

Spondylodiscitis represents 2%–4% of all bone infections cases. The correct diagnosis and appropriate treatment can prevent complications such as vertebral collapse and spinal cord compression, avoiding surgical procedures. The diagnosis is based on characteristic clinical and radiographic findings and confirmed by blood culture and biopsy of the disc or the vertebra. The present study was developed with Clementino Fraga Filho University Hospital patients with histopathologically and microbiologically confirmed diagnosis of spondylodiscitis, submitted to magnetic resonance imaging of the affected regions. In most cases, pyogenic spondylodiscitis affects the lumbar spine. The following findings are suggestive of the diagnosis: segmental involvement; ill-defined abscesses; early intervertebral disc involvement; homogeneous vertebral bodies and intervertebral discs involvement. Tuberculous spondylodiscitis affects preferentially the thoracic spine. Most suggestive signs include: presence of well-defined and thin-walled abscess; multisegmental, subligamentous involvement; heterogeneous involvement of vertebral bodies; and relative sparing of intervertebral discs. The present pictorial essay is aimed at showing the main magnetic resonance imaging findings of pyogenic and tuberculous discitis.

Download Full-text