Application of Deep Learning Convolutional Neural Networks for Internal Tablet Defect Detection: High Accuracy, Throughput, and Adaptability

Ma, Xiangyu; Kittikunakorn, Nada; Sorman, Bradley; Xi, Hanmi; Chen, Antong; Marsh, Mike; Mongeau, Arthur; Piché, Nicolas; Williams, Robert O.; Skomski, Daniel

doi:10.1016/j.xphs.2020.01.014

Cited by 58 publications

(31 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another focus of XMT measurements is the investigation of microcracks inside tablets and the correlation of the findings with the occurrence of tablet defects, (e.g. capping and lamination), as well as tablet tensile strength ( Hancock and Mullarney, 2005 ; Garner et al, 2014 ; Yost et al, 2019 ; Mazel et al, 2018 ; Ma et al, 2020 ; Wu et al, 2008 ). Besides normal tablets, bilayer tablets were investigated to determine the impact of local density distributions on the delamination of the two layers ( Akseli et al, 2013 ; Inman et al, 2007 ).…”

Section: Introductionmentioning

confidence: 99%

The use of X-ray microtomography to investigate the microstructure of pharmaceutical tablets: Potentials and comparison to common physical methods

Schomberg

Diener

Wünsch

et al. 2021

International Journal of Pharmaceutics: X

View full text Add to dashboard Cite

Within this study, tablets microstructure was investigated by X-ray microtomgraphy. The aim was to gain information about their microstructure, and thus, derive deeper interpretation of tablet properties (mechanical strength, component distribution) and qualified property functions. Challenges in image processing are discussed for the correct identification of solids and voids. Furthermore, XMT measurements are critically compared with complementary physical methods for characterizing active pharmaceutical ingredient (API) content and porosity and its distribution (mercury porosimetry, calculated tablet porosity, Focused Ion Beam-Scanning Electron Microscopy (FIB-SEM)). The derived porosity by XMT is generally lower than the calculated porosity based on geometrical data due to the resolution of the XMT in relation to the pore sizes in tablets. With rising compactions stress and API concentration, deviations between the actual and the calculated API decrease. XMT showed that API clusters are present for all tablets containing >1 wt% of ibuprofen. The 3D orientation of the components is assessable by deriving cord lengths along all dimensions of the tablets. An increasing compaction stress leads to rising cord lengths, showing higher connectivity of the respective material. Its lesser extent in the z-direction illustrates the anisotropy of the compaction process. Additionally, cracks in the fabric are identified in tablets without visible macroscopic damage. Finally, the application of XMT provides valuable structural insights if its limitations are taken into account and its strengths are fostered by advanced pre- and post-processing.

show abstract

Section: Introductionmentioning

confidence: 99%

The use of X-ray microtomography to investigate the microstructure of pharmaceutical tablets: Potentials and comparison to common physical methods

Schomberg

Diener

Wünsch

et al. 2021

International Journal of Pharmaceutics: X

View full text Add to dashboard Cite

show abstract

“…Hyperparameter optimization is a machine learning task that involves choosing a set of optimal hyperparameters for a training algorithm. The settings of hyperparameters were discussed by He et al [41], Ma et al [42], and Aydo gan et al [43].…”

Section: Architecture and Advantages Of Cnnmentioning

confidence: 99%

Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review

2021

View full text Add to dashboard Cite

Automation, including machine learning technologies, are becoming increasingly crucial in agriculture to increase productivity. Machine vision is one of the most popular parts of machine learning and has been widely used where advanced automation and control have been required. The trend has shifted from classical image processing and machine learning techniques to modern artificial intelligence (AI) and deep learning (DL) methods. Based on large training datasets and pre-trained models, DL-based methods have proven to be more accurate than previous traditional techniques. Machine vision has wide applications in agriculture, including the detection of weeds and pests in crops. Variation in lighting conditions, failures to transfer learning, and object occlusion constitute key challenges in this domain. Recently, DL has gained much attention due to its advantages in object detection, classification, and feature extraction. DL algorithms can automatically extract information from large amounts of data used to model complex problems and is, therefore, suitable for detecting and classifying weeds and crops. We present a systematic review of AI-based systems to detect weeds, emphasizing recent trends in DL. Various DL methods are discussed to clarify their overall potential, usefulness, and performance. This study indicates that several limitations obstruct the widespread adoption of AI/DL in commercial applications. Recommendations for overcoming these challenges are summarized.

show abstract

“…It is impractical to run every experiment probing all aspects of the composition of matter, process, and stability, even with high throughput technologies. Machine learning affords methods toward enhancing the speed of analysis and reducing time-intensive manual labor . Further, there is a high interest in using AI to predict product performance and long-term stability as well as improve the translation from small-scale to commercial-scale manufacture, while narrowing down the experimental space using theoretical modeling.…”

Section: Model-based Drug Product Developmentmentioning

confidence: 99%

Flexibility in Drug Product Development: A Perspective

et al. 2021

Self Cite

View full text Add to dashboard Cite

The process of bringing a drug to market involves innumerable decisions to refine a concept into a final product. The final product goes through extensive research and development to meet the target product profile and to obtain a product that is manufacturable at scale. Historically, this process often feels inflexible and linear, as ideas and development paths are eliminated early on to allow focus on the workstream with the highest probability of success. Carrying multiple options early in development is both time-consuming and resource-intensive. Similarly, changing development pathways after significant investment carries a high "penalty of change" (PoC), which makes pivoting to a new concept late in development inhibitory. Can drug product (DP) development be made more flexible? The authors believe that combining a nonlinear DP development approach, leveraging state-ofthe art data sciences, and using emerging process and measurement technologies will offer enhanced flexibility and should become the new normal. Through the use of iterative DP evaluation, "smart" clinical studies, artificial intelligence, novel characterization techniques, automation, and data collection/modeling/interpretation, it should be possible to significantly reduce the PoC during development. In this Perspective, a review of ideas/techniques along with supporting technologies that can be applied at each stage of DP development is shared. It is further discussed how these contribute to an improved and flexible DP development through the acceleration of the iterative build−measure−learn cycle in laboratories and clinical trials.

show abstract

Application of Deep Learning Convolutional Neural Networks for Internal Tablet Defect Detection: High Accuracy, Throughput, and Adaptability

Cited by 58 publications

References 35 publications

The use of X-ray microtomography to investigate the microstructure of pharmaceutical tablets: Potentials and comparison to common physical methods

The use of X-ray microtomography to investigate the microstructure of pharmaceutical tablets: Potentials and comparison to common physical methods

Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review

Flexibility in Drug Product Development: A Perspective

Contact Info

Product

Resources

About