Disintegration testing augmented by computer Vision technology

Floryanzia, Sydney; Ramesh, Preethi; Mills, Madeline M.; Kulkarni, Sanjana; Chen, Grace; Shah, Prashant; Lavrich, David

doi:10.1016/j.ijpharm.2022.121668

Cited by 15 publications

(5 citation statements)

References 18 publications

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“…In manufacturing, MV systems can be employed for real-time QC monitoring, allowing them to perform tasks such as inspection and identification [26]. By automating visual tasks and providing fast and accurate results, MV is helping to improve efficiency, reduce errors, and enhance safety in various applications [27][28][29][30]. In pharmaceutics, MV was found to achieve high accuracy in the real-time detection of measuring coating thickness and detecting defects in film-coated tablets [28].…”

Section: Introductionmentioning

confidence: 99%

“…In pharmaceutics, MV was found to achieve high accuracy in the real-time detection of measuring coating thickness and detecting defects in film-coated tablets [28]. It has also been used for inspecting liquid droplets in pharmaceutical injections [27], data augmentation [30], and in situ monitoring of the selective laser sintering process [29].…”

Section: Introductionmentioning

confidence: 99%

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Virtually Possible: Enhancing Quality Control of 3D-Printed Medicines with Machine Vision Trained on Photorealistic Images

Sun,

Alkahtani,

Gaisford

et al. 2023

Pharmaceutics

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Three-dimensional (3D) printing is an advanced pharmaceutical manufacturing technology, and concerted efforts are underway to establish its applicability to various industries. However, for any technology to achieve widespread adoption, robustness and reliability are critical factors. Machine vision (MV), a subset of artificial intelligence (AI), has emerged as a powerful tool to replace human inspection with unprecedented speed and accuracy. Previous studies have demonstrated the potential of MV in pharmaceutical processes. However, training models using real images proves to be both costly and time consuming. In this study, we present an alternative approach, where synthetic images were used to train models to classify the quality of dosage forms. We generated 200 photorealistic virtual images that replicated 3D-printed dosage forms, where seven machine learning techniques (MLTs) were used to perform image classification. By exploring various MV pipelines, including image resizing and transformation, we achieved remarkable classification accuracies of 80.8%, 74.3%, and 75.5% for capsules, tablets, and films, respectively, for classifying stereolithography (SLA)-printed dosage forms. Additionally, we subjected the MLTs to rigorous stress tests, evaluating their scalability to classify over 3000 images and their ability to handle irrelevant images, where accuracies of 66.5% (capsules), 72.0% (tablets), and 70.9% (films) were obtained. Moreover, model confidence was also measured, and Brier scores ranged from 0.20 to 0.40. Our results demonstrate promising proof of concept that virtual images exhibit great potential for image classification of SLA-printed dosage forms. By using photorealistic virtual images, which are faster and cheaper to generate, we pave the way for accelerated, reliable, and sustainable AI model development to enhance the quality control of 3D-printed medicines.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Virtually Possible: Enhancing Quality Control of 3D-Printed Medicines with Machine Vision Trained on Photorealistic Images

Sun,

Alkahtani,

Gaisford

et al. 2023

Pharmaceutics

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“…[ 40 , 41 ]. Recent studies have also reported the use of convolutional neural networks for product characterization based on imaging studies [ 42 , 43 ]. To the best of our knowledge, in the field of LSS, only Barmpalexis et al [ 44 ] have used ML tools, more specifically artificial neural networks and genetic programming, to investigate the effects of formulation variables on the dissolution of LSS using aprepitant as a model drug.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine-Learning Algorithms for Better Understanding the Properties of Liquisolid Systems Prepared with Three Mesoporous Silica Based Carriers

et al. 2023

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The processing of liquisolid systems (LSS), which are considered a promising approach to improving the oral bioavailability of poorly soluble drugs, has proven challenging due to the relatively high amount of liquid phase incorporated within them. The objective of this study was to apply machine-learning tools to better understand the effects of formulation factors and/or tableting process parameters on the flowability and compaction properties of LSS with silica-based mesoporous excipients as carriers. In addition, the results of the flowability testing and dynamic compaction analysis of liquisolid admixtures were used to build data sets and develop predictive multivariate models. In the regression analysis, six different algorithms were used to model the relationship between tensile strength (TS), the target variable, and eight other input variables. The AdaBoost algorithm provided the best-fit model for predicting TS (coefficient of determination = 0.94), with ejection stress (ES), compaction pressure, and carrier type being the parameters that influenced its performance the most. The same algorithm was best for classification (precision = 0.90), depending on the type of carrier used, with detachment stress, ES, and TS as variables affecting the performance of the model. Furthermore, the formulations with Neusilin® US2 were able to maintain good flowability and satisfactory values of TS despite having a higher liquid load compared to the other two carriers.

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“…Achishuler argues that only patents are original, while the rest take advantage of existing ideas or concepts, plus novel methods. He rmly believed that the basic principles of solving invention problems existed objectively and could be organized into a complete theoretical system, which could be used to improve the success rate of invention, shorten the invention cycle, and make the solution of invention problems predictable [7][8][9][10][11]. After more than half a century of development and rapid explosive popularization and application in the past ten years, it has become an e ective tool in the eld of engineering technology to creatively solve the problems of product design and manufacturing process and has helped many well-known companies to achieve signi cant economic and social bene ts [12].…”

Section: Introductionmentioning

confidence: 99%

Design Method of Product Concept Model Based on CAD Technology

Meng

2022

Scientific Programming

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With the comprehensive development of information technology, the continuous optimization of products can make enterprises stand in the market. Various industries are involved in the upsurge of computer-enabling products; computer-aided (CAD) technology has also been rapid development. CAD technology is composed of problem analysis, innovative method, problem transformation and problem-solving invention principle, standard solution, and so on. In this paper, the theoretical system, basic scheme, and solving algorithm of CAD technology are described in detail. Meanwhile, the content of value evaluation and root cause analysis is analyzed and solved to further illustrate the application process of CAD technology optimization products and gradually form a more perfect application method of CAD innovation technology so that its solution process is more scientific, operable, and universal.

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Disintegration testing augmented by computer Vision technology

Cited by 15 publications

References 18 publications

Virtually Possible: Enhancing Quality Control of 3D-Printed Medicines with Machine Vision Trained on Photorealistic Images

Virtually Possible: Enhancing Quality Control of 3D-Printed Medicines with Machine Vision Trained on Photorealistic Images

Application of Machine-Learning Algorithms for Better Understanding the Properties of Liquisolid Systems Prepared with Three Mesoporous Silica Based Carriers

Design Method of Product Concept Model Based on CAD Technology

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