N25 pill identification and clinical use

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Pill Identification Technologies: Deep Learning and AI for N25 Pill Recognition

Accurate pill identification is essential for medication safety, especially as medication errors are a leading cause of preventable harm in healthcare settings. Advanced technologies, particularly deep learning and AI, are now widely used to identify pills like the "N25" pill from images, helping both healthcare professionals and patients avoid mistakes 123456.

Deep Learning Models for Pill Identification

Deep convolutional networks (DCNs) and other deep learning models have shown high accuracy in pill identification tasks. For example, DCN-based systems can identify pills from mobile phone images with a Top-1 accuracy of over 95%, even when images are taken under different lighting and angles . Other deep learning applications, such as those using the MobileNet architecture, also provide fast and accurate pill recognition, making them suitable for real-world clinical use .

Real-Time Pill Detection and Speed

Object detection models like YOLO v3, RetinaNet, and SSD have been compared for real-time pill identification. YOLO v3 stands out for its speed and satisfactory accuracy, making it ideal for deployment in hospital settings where quick identification is crucial . These models can process images rapidly, supporting real-time workflows in pharmacies and clinics .

Mobile and Automated Pill Identification Applications

Mobile-based applications, such as ClinicYA, use deep learning and color clustering to identify pills with high accuracy, even in unconstrained environments. These apps can recognize pill shapes and colors with over 99% accuracy, making them practical for both healthcare providers and patients . Automated systems also connect detected pills to databases, providing detailed information about the pill, including its name and clinical use .

Clinical Use and Medication Safety

AI-powered pill identification systems help reduce medication errors by ensuring that the correct pill is administered or consumed. These systems are especially valuable when pill containers are missing or labels are damaged, as they can still accurately identify pills from images 26. By integrating these technologies into clinical workflows, healthcare teams can improve medication reconciliation, reduce errors, and enhance patient safety 256.

Conclusion

Modern pill identification systems, powered by deep learning and AI, offer fast, accurate, and reliable recognition of pills like the N25. These technologies support both healthcare professionals and patients by reducing medication errors, providing detailed drug information, and improving overall medication safety in clinical practice 123456.

Sources and full results

Most relevant research papers on this topic

Development of fine-grained pill identification algorithm using deep convolutional network

The Deep Convolutional Network (DCN) model effectively identifies and verifies oral pills with high accuracy, surpassing existing methods and achieving higher accuracy rates even in suboptimal image quality.

2017·

32citations

·Y. Wong et al.

Fast and accurate medication identification

Our deep-learning application accurately identifies prescription medications from mobile images with 94% accuracy, reducing medication errors and improving patient care.

Rigorous Journal

2019·

39citations

·Natalia Larios Delgado et al.

Comparison of RetinaNet, SSD, and YOLO v3 for real-time pill identification

YOLO v3 is the best object detection model for real-time pill identification in hospital pharmacies, with a slightly lower mean average precision but faster detection speed.

Highly Cited

2021·

133citations

·Lu Tan et al.

ClinicYA: An Application for Pill Identification Using Deep Learning and K-means Clustering

ClinicYA, a mobile-based application, accurately identifies pills in real-world conditions with over 99.27% accuracy in pill shape recognition and 93.85% accuracy in color detection.

2022·

1citation

·Supawadee Srikamdee et al.

Detection and Identification of Pills using Machine Learning Models

This paper proposes a machine learning system using Keras and Tensor Flow for quick and easy identification of various pills, reducing medication errors and improving patient safety.

2023·

14citations

·S. M et al.

Revolutionizing Healthcare: A Smart Pill Detection System Empowered by Deep Learning for Intelligent Drug Recognition

This smart pill detection system powered by deep learning can accurately identify pills and medicinal drugs, reducing medication errors and improving healthcare efficiency.

2024·

0citations

·A. Dhayanithi et al.

Pill Detection Model for Medicine Inspection Based on Deep Learning

Our deep learning algorithm improves pill identification performance using limited training data, reducing loss of productivity and human error in medicine inspection.

2021·

32citations

·Hyuk-Ju Kwon et al.

Measurement of Adherence to Antiretroviral Medications

Measurement of adherence to antiretroviral medications is crucial for understanding why patients fail therapy, with electronic monitoring being the gold standard.

Highly Cited

2002·

151citations

·D. Paterson et al.

Novel histone deacetylase inhibitor N25 exerts anti-tumor effects and induces autophagy in human glioma cells by inhibiting HDAC3

N25 has strong anti-tumor activity in glioma cells, partly due to inhibition of HDAC3, and may induce autophagy through this mechanism.

In Vitro StudyRigorous Journal

2017·

11citations

·Xin-Yuan Sun et al.

Identification and quantitation of NF-κB inhibitory components in Weichang'an pill based on UHPLC-QE-MS and spectrum-effect relationships

Weichang'an pill contains 10 potential NF-B inhibitory compounds, which can be used for quality control and understanding of traditional Chinese patent medicines.

2023·

3citations

·Xiaoxia Cao et al.

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