Medical instrument manufacture in the People's Republic of Bulgaria

1970 ◽  
Vol 4 (4) ◽  
pp. 226-229
Author(s):  
A. I. Ilcheva ◽  
A. K. Aleksiev ◽  
I. K. Daskalov
Keyword(s):  
Medical Instrument ◽  
Instrument Manufacture ◽  
Medical Instrument Manufacture

Intraoperative Robotics for Patient Specific Instrument Manufacture: A Cadaver Trial

2019 ◽  
Author(s):  
A Darwood ◽  
◽  
S Hurst ◽  
G Villatte ◽  
R Fenton ◽  
...  
Keyword(s):  
Patient Specific ◽  
Instrument Manufacture ◽  
Specific Instrument

scholarly journals Torsional waveguide modeling of an ultrasonic medical instrument

2021 ◽  
Vol 1902 (1) ◽  
pp. 012009
Author(s):  
Alexander Mikhailovich Gouskov ◽  
Yuri Vsevolodovich Grigoryev ◽  
Phyo Aung Pyae
Keyword(s):  
Medical Instrument

Adverse drug and medical instrument reactions in a pediatric intensive care unit

2009 ◽  
Vol 18 (8) ◽  
pp. 761-762 ◽  
Author(s):  
Shockrollah Farrokhi ◽  
Zahra Pourpak ◽  
Fatemeh Fattahi ◽  
Mahmood Reza Ashrafi ◽  
Parvin Majdinasab ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Pediatric Intensive Care Unit ◽  
Medical Instrument ◽  
Pediatric Intensive Care

scholarly journals AN INTELLIGENT CBMIR SYSTEM FOR DETECTION AND LOCALIZATION OF LUNG DISEASES

2021 ◽  
Vol 9 (08) ◽  
pp. 651-660
Author(s):  
Nora I. Yahia ◽  
◽  
Ayman I. Al-Dosouki ◽  
Sahar A. Mokhtar ◽  
Hany M. Harb ◽  
...  
Keyword(s):  
Medical Image ◽  
Lung Diseases ◽  
Medical Instrument ◽  
Detection Accuracy ◽  
Retrieval Process ◽  
Recognition Efficiency ◽  
Imaging Signs ◽  
Detection And Localization ◽  
Aided Diagnosis ◽  
Detector Model

The diagnosis of lung diseases is a complicated and time-consuming task for radiologists. Often radiologists struggle with accurately diagnosing lung diseases, They use Commonly CT imaging signs (CISs) which common appear in CT lung nodules in the diagnosis of lung diseases. Computer-aided diagnosis systems (CAD) can automatically diagnose and detect these signs by analyzing CT scans, which will reduce radiologists workload. The diagnosis and recognition efficiency and accuracy can be improved by using content-based medical image retrieval (CBMIR). This paper proposes a new intelligent CBMIR method to retrieve CISs helping in diagnosing and recognize lung diseases by using deep Convolutional Neural Network (CNN). Fine-tuned YOLOv4 (You Only Look Once) object detector model are proposed to fast detect and efficiently localize signs in real-time. The proposed CBMIR system can be applied as a useful and accurate medical instrument for diagnostics. The experimental results show an average detection accuracy of CT signs lung diseases as high as 92% and a mean average precision (MAP) of 0.92 is achieved using the proposed technique. Also, it takes only 0.1 milliseconds for the retrieval process. The proposed system presents high improvement as compared to the other system. It achieved better precision of retrieval results and the fastest run of the retrieval time.

scholarly journals Medical Instrument Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning

2021 ◽  
pp. 1-1
Author(s):  
Hongxu Yang ◽  
Caifeng Shan ◽  
R. Arthur Bouwman ◽  
Lukas Dekker ◽  
Alexander Kolen ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Medical Instrument
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