Bringing Structural Implications and Deep Learning-Based Drug Identification for KRAS Mutants

2021 ◽  
Vol 61 (2) ◽  
pp. 571-586
Author(s):  
Aamir Mehmood ◽  
Aman Chandra Kaushik ◽  
Qiankun Wang ◽  
Cheng-Dong Li ◽  
Dong-Qing Wei
Keyword(s):  
Deep Learning ◽  
Drug Identification

scholarly journals A Drug Identification Model developed using Deep Learning Technologies: Experience of a Medical Center in Taiwan

2020 ◽  
Author(s):  
Hsien-Wei Ting ◽  
Sheng-Luen Chung ◽  
Chih-Fang Chen ◽  
Hsin-Yi Chiu ◽  
Yow-Wen Hsieh
Keyword(s):  
Patient Safety ◽  
Deep Learning ◽  
Medication Errors ◽  
Medical Center ◽  
Identification Problem ◽  
Learning Technologies ◽  
Back Side ◽  
Front Side ◽  
Training Time ◽  
Drug Identification

Abstract Background: Issuing of correct prescriptions is a foundation of patient safety. Medication errors represent one of the most important problems in health care, with ‘look-alike and sound-alike’ (LASA) being the lead error. Existing solutions to prevent LASA still have their limitations. Deep learning techniques have revolutionized identification classifiers in many fields. In search of better image-based solutions for blister package identification problem, this study using a baseline deep learning drug identification (DLDI) aims to understand how identification confusion of look-alike images by human occurs through the cognitive counterpart of deep learning solutions and thereof to suggest further solutions to approach them. Methods: We collected images of 250 types of blister-packaged drug from the Out-Patient Department (OPD) of a medical center for identification. The deep learning framework of You Only Look Once (YOLO) was adopted for implementation of the proposed deep learning. The commonly-used F1 score, defined by precision and recall for large numbers of identification tests, was used as the performance criterion. This study trained and compared the proposed models based on images of either the front-side or back-side of blister-packaged drugs. Results: Our results showed that the total training time for the front-side model and back-side model was 5 hours 34 minutes and 7 hours 42 minutes, respectively. The F1 score of the back-side model (95.99%) was better than that of the front-side model (93.72%). Conclusions: In conclusion, this study constructed a deep learning-based model for blister-packaged drug identification, with an accuracy greater than 90%. This model outperformed identification using conventional computer vision solutions, and could assist pharmacists in identifying drugs while preventing medication errors caused by look-alike blister packages. By integration into existing prescription systems in hospitals, the results of this study indicated that using this model, drugs dispensed could be verified in order to achieve automated prescription and dispensing. Key words: deep learning; drug identification; look-alike and sound-alike (lasa); medication error; patient safety.

scholarly journals A Drug Identification Model developed using Deep Learning Technologies: Experience of a Medical Center in Taiwan

2019 ◽  
Author(s):  
Hsien-Wei Ting ◽  
Sheng-Luen Chung ◽  
Chih-Fang Chen ◽  
Hsin-Yi Chiu ◽  
Yow-Wen Hsieh
Keyword(s):  
Deep Learning ◽  
Medication Errors ◽  
Medical Center ◽  
Performance Criterion ◽  
Learning Technologies ◽  
Back Side ◽  
Front Side ◽  
Training Time ◽  
Drug Identification ◽  
Learning Framework

Abstract Background: Issuing of correct prescriptions is a foundation of patient safety. Medication errors represent one of the most important problems in health care, with ‘look-alike and sound-alike’ (LASA) being the lead error. Existing solutions to prevent LASA still have their limitations. Deep learning techniques have revolutionized identification classifiers in many fields. Aiming to identify blister packages, this study employed a visual-based identification solution, called the deep learning drug identification (DLDI) model, which reduced medication identification errors caused by look-alike drugs. Methods: We collected images of 250 types of blister-packaged drug from the Out-Patient Department (OPD) of a medical center for identification. The deep learning framework of You Only Look Once (YOLO) was adopted for implementation of the proposed deep learning. The commonly-used F1 score, defined by precision and recall for large numbers of identification tests, was used as the performance criterion. This study trained and compared the proposed models based on images of either the front-side or back-side of blister-packaged drugs. Results: Our results showed that the total training time for the front-side model and back-side model was 5 hours 34 minutes and 7 hours 42 minutes, respectively. The F1 score of the back-side model (95.99%) was better than that of the front-side model (93.72%). Conclusions: In conclusion, this study constructed a deep learning-based model for blister-packaged drug identification, with an accuracy greater than 90%. This model outperformed identification using conventional computer vision solutions, and could assist pharmacists in identifying drugs while preventing medication errors caused by look-alike blister packages. By integration into existing prescription systems in hospitals, the results of this study indicated that using this model, drugs dispensed could be verified in order to achieve automated prescription and dispensing.

scholarly journals A Drug Identification Model developed using Deep Learning Technologies: Experience of a Medical Center in Taiwan

2020 ◽  
Author(s):  
Hsien-Wei Ting ◽  
Sheng-Luen Chung ◽  
Chih-Fang Chen ◽  
Hsin-Yi Chiu ◽  
Yow-Wen Hsieh
Keyword(s):  
Deep Learning ◽  
Medication Errors ◽  
Medical Center ◽  
Identification Problem ◽  
Performance Criterion ◽  
Learning Technologies ◽  
Back Side ◽  
Front Side ◽  
Training Time ◽  
Drug Identification

Abstract Background: Issuing of correct prescriptions is a foundation of patient safety. Medication errors represent one of the most important problems in health care, with ‘look-alike and sound-alike’ (LASA) being the lead error. Existing solutions to prevent LASA still have their limitations. Deep learning techniques have revolutionized identification classifiers in many fields. In search of better image-based solutions for blister package identification problem, this study using a baseline deep learning drug identification (DLDI) aims to understand how identification confusion of look-alike images by human occurs through the cognitive counterpart of deep learning solutions and thereof to suggest further solutions to approach them.Methods: We collected images of 250 types of blister-packaged drug from the Out-Patient Department (OPD) of a medical center for identification. The deep learning framework of You Only Look Once (YOLO) was adopted for implementation of the proposed deep learning. The commonly-used F1 score, defined by precision and recall for large numbers of identification tests, was used as the performance criterion. This study trained and compared the proposed models based on images of either the front-side or back-side of blister-packaged drugs. Results: Our results showed that the total training time for the front-side model and back-side model was 5 hours 34 minutes and 7 hours 42 minutes, respectively. The F1 score of the back-side model (95.99%) was better than that of the front-side model (93.72%). Conclusions: In conclusion, this study constructed a deep learning-based model for blister-packaged drug identification, with an accuracy greater than 90%. This model outperformed identification using conventional computer vision solutions, and could assist pharmacists in identifying drugs while preventing medication errors caused by look-alike blister packages. By integration into existing prescription systems in hospitals, the results of this study indicated that using this model, drugs dispensed could be verified in order to achieve automated prescription and dispensing.

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning

Intelligence artificielle : le futur de l’Orthodontie ?

2019 ◽  
Vol 53 (3) ◽  
pp. 281-294
Author(s):  
Jean-Michel Foucart ◽  
Augustin Chavanne ◽  
Jérôme Bourriau
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligence Artificielle ◽  
Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

Primäre ZNS-Lymphome: Vollautomatisierte Detektion und Segmentierung auf multiparametrischen MRT-Datensätzen mittels Deep-Learning

2020 ◽  
Author(s):  
L Pennig ◽  
L Lourenco Caldeira ◽  
C Hoyer ◽  
L Görtz ◽  
R Shahzad ◽  
...  
Keyword(s):  
Deep Learning

Computertomografie: Bildrekonstruktion mit Deep Learning in der klinischen Routine

2020 ◽  
Author(s):  
A Heinrich ◽  
M Engler ◽  
D Dachoua ◽  
U Teichgräber ◽  
F Güttler
Keyword(s):  
Deep Learning
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