TY - CHAP AU - Knyihár, Gábor AU - Csorba, Kristóf ED - Vajk, István ED - Dunaev, Dmitriy TI - In vitro Dissolution Prediction using Deep Convolutional Neural Networks T2 - Proceedings of the Automation and Applied Computer Science Workshop 2023 (AACS'23) PB - Budapesti Műszaki Egyetem, Automatizálási és Alkalmazott Informatikai Tanszék CY - Budapest SN - 9789634219262 PY - 2023 SP - 27 EP - 38 PG - 12 UR - https://m2.mtmt.hu/api/publication/34567948 ID - 34567948 LA - English DB - MTMT ER - TY - JOUR AU - Knyihár, Gábor AU - Csorba, Kristóf AU - Charaf, Hassan TI - Evaluating the Accuracy of a Linear Regression Model in Predicting the Dissolution of Tablets based on Raman Maps JF - SIGNAL & IMAGE PROCESSING: AN INTERNATIONAL JOURNAL J2 - SIPIJ VL - 14 PY - 2023 IS - 1 SP - 23 EP - 33 PG - 11 SN - 2229-3922 DO - 10.5121/sipij.2023.14102 UR - https://m2.mtmt.hu/api/publication/33604474 ID - 33604474 AB - Investigation of the dissolution of tablets is an important area of pharmaceutical research. Such research aims to predict the dissolution process as accurately as possible without destroying the tablets. Several methods have been published that can estimate dissolution with approximate accuracy, but they are primarily complex learning algorithms that are time-consuming and require many samples to train. This article seeks to answer whether these complex models are necessary or whether a similar result can be achieved with the help of more straightforward methods. Therefore, during this work, a simpler linear regression model was created and analysed its effectiveness in estimating the dissolution curves. The investigation concluded that the results are not as accurate as in the case of more complex methods, but the model is more robust and can be used in the case of fewer samples. Thus, by further developing these and combining the methods, we can achieve better results in the future. LA - English DB - MTMT ER - TY - CHAP AU - Knyihár, Gábor AU - Csorba, Kristóf AU - Charaf, Hassan TI - Tabletták kioldódásának előrejelzése Raman térképek alapján lineáris regressziós modell segítségével T2 - Hungarian Association for Image Analysis and Pattern Recognition - 14th Conference PB - Képfeldolgozók és Alakfelismerők Társasága CY - Gyula PY - 2023 PG - 11 UR - https://m2.mtmt.hu/api/publication/33604424 ID - 33604424 AB - A tabletták oldódásának vizsgálata a gyógyszerészeti kutatások egyik fontos területe. Az ilyen kutatások célja az oldódás folyamatának előrejelzése a lehető legpontosabban a tabletták roncsolása nélkül. Számos olyan módszert publikáltak már, ami közelítő pontossággal meg tudja becsülni a kioldódást, de ezek többnyire olyan összetett tanuló algoritmusok, amelyek időigényesek és betanításukhoz nagy számú mintára van szükség. Ez a cikk arra a kérdésre keresi a választ, hogy szükségeseke ezek az összetett modellek, vagy egyszerűbb módszerek segítségével is elérhető hasonló eredmény. A munka során ezért egy egyszerűbb lineáris regressziós modellt készítettünk, és ennek hatékonyságát mértük a kioldódási görbék becslésében. A vizsgálattal arra a következtetésre jutottunk, hogy az eredmények ugyan nem annyira pontosak, mint a komplexebb módszerek esetében, ellenben a modell robosztusabb és kevesebb minta esetében is használható. Így a jövőben ezek továbbfejlesztésével és a módszerek kombinálásával akár jobb eredményeket is elérhetünk. LA - Hungarian DB - MTMT ER - TY - CHAP AU - Knyihár, Gábor AU - Csorba, Kristóf AU - Charaf, Hassan TI - Predicting the Dissolution of Tablets based on Raman Maps using a Linear Regression Model T2 - Computer Science and Machine Learning Trends 2023 PB - Academy and Industry Research Collaboration Center (AIRCC) SN - 9781925953848 PY - 2023 SP - 79 EP - 85 PG - 7 DO - 10.5121/csit.2023.130107 UR - https://m2.mtmt.hu/api/publication/33543170 ID - 33543170 AB - Investigation of the dissolution of tablets is an important area of pharmaceutical research. Such research aims to predict the dissolution process as accurately as possible without destroying the tablets. Several methods have been published that can estimate dissolution with approximate accuracy, but they are mostly complex and time-consuming. This article seeks to answer whether these complex models are necessary or whether a similar result can be achieved with the help of more straightforward methods. Therefore, during this work, a simpler linear regression model was created and analysed its effectiveness in estimating the dissolution curves. The investigation concluded that the results are not as accurate as in the case of more complex methods, but they are not far behind. Thus, even similar results may be achieved by fine-tuning and possibly developing these methods. LA - English DB - MTMT ER - TY - CHAP AU - Mrad, Mohamed Azouz AU - Csorba, Kristóf AU - Galata, Dorián László AU - Nagy, Zsombor Kristóf AU - Nagy, Brigitta ED - Vajk, István ED - Dunaev, Dmitriy TI - Droplet Based Prediction of Viscosity of Water-PVP Solutions Using Convolutional Neural Networks T2 - Proceedings of the Automation and Applied Computer Science Workshop 2023 (AACS'23) PB - Budapesti Műszaki Egyetem, Automatizálási és Alkalmazott Informatikai Tanszék CY - Budapest SN - 9789634219262 PY - 2023 SP - 1 EP - 15 PG - 15 UR - https://m2.mtmt.hu/api/publication/34476285 ID - 34476285 AB - The viscosity of a liquid is the property that measures the liquid internal resistance to flow. Monitoring viscosity is a vital component of quality control in several industrial fields including chemical, pharmaceutical, food, and energy-related industries. The most commonly used instrument for measuring viscosity is capillary viscometers, but their cost and complexity pose challenges for industries where accurate and real-time viscosity information is vital. In this work, we prepared thirteen solutions with different water and PVP (Polyvinylpyrrolidone) ratios, measured their different viscosity values, and produced videos of their droplets. We extracted the images of the fully developed droplets from the videos and we used the images to train a Convolutional neural network model to estimate the viscosity values of the WaterPVP solutions. The proposed model was able to predict the viscosity values of the samples using images of their droplets with a high accuracy on the test dataset. LA - English DB - MTMT ER - TY - JOUR AU - Mrad, Mohamed Azouz AU - Csorba, Kristóf AU - Galata, Dorián László AU - Nagy, Zsombor Kristóf AU - Charaf, Hassan TI - Droplet Based Estimation of Viscosity of Water–PVP Solutions Using Convolutional Neural Networks JF - PROCESSES J2 - PROCESSES VL - 11 PY - 2023 IS - 7 PG - 13 SN - 2227-9717 DO - 10.3390/pr11071917 UR - https://m2.mtmt.hu/api/publication/34045465 ID - 34045465 N1 - Department of Automation and Applied Informatics, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Műegyetem rkp.3, Budapest, H-1111, Hungary Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Budafoki út 8., F. II, Budapest, H-1111, Hungary Export Date: 14 August 2023 Correspondence Address: Mrad, M.A.; Department of Automation and Applied Informatics, Műegyetem rkp.3, Hungary; email: mmrad@edu.bme.hu AB - The viscosity of a liquid is the property that measures the liquid’s internal resistance to flow. Monitoring viscosity is a vital component of quality control in several industrial fields, including chemical, pharmaceutical, food, and energy-related industries. In many industries, the most commonly used instrument for measuring viscosity is capillary viscometers, but their cost and complexity pose challenges for these industries where accurate and real-time viscosity information is vital. In this work, we prepared fourteen solutions with different water and PVP (Polyvinylpyrrolidone) ratios, measured their different viscosity values, and produced videos of their droplets. We extracted the images of the fully developed droplets from the videos and we used the images to train a convolutional neural network model to estimate the viscosity values of the water–PVP solutions. The proposed model was able to accurately estimate the viscosity values of samples of unseen chemical formulations with the same composition with a low MSE score of 0.0243 and R2 score of 0.9576. The proposed method has potential applications in scenarios where real-time monitoring of liquid viscosity is required. LA - English DB - MTMT ER - TY - CHAP AU - Mrad, Mohamed Azouz AU - Csorba, Kristóf AU - Galata, Dorián László AU - Nagy, Zsombor Kristóf AU - Nagy, Brigitta ED - Zurada, Jacek M. ED - Tadeusiewicz, Ryszard ED - Pedrycz, Witold ED - Korytkowski, Marcin ED - Scherer, Rafał ED - Rutkowski, Leszek TI - Viscosity Estimation of Water-PVP Solutions from Droplets using Artificial Neural Networks and Image Processing T2 - Artificial Intelligence and Soft Computing PB - Springer Nature Switzerland AG CY - Cham SN - 9783031425059 T3 - Lecture Notes in Computer Science, ISSN 0302-9743 ; 14125. PY - 2023 SP - 157 EP - 166 PG - 10 DO - 10.1007/978-3-031-42505-9_14 UR - https://m2.mtmt.hu/api/publication/33605280 ID - 33605280 N1 - Export Date: 6 October 2023 Correspondence Address: Mrad, M.A.; Budapest University of Technology and Economics, Budapest Muegyetem rkp. 3, Hungary; email: mmrad@edu.bme.hu LA - English DB - MTMT ER - TY - CHAP AU - Mrad, Mohamed Azouz AU - Csorba, Kristóf AU - Galata, Dorián László AU - Nagy, Zsombor Kristóf AU - Nagy, Brigitta ED - Zurada, Jacek M. ED - Tadeusiewicz, Ryszard ED - Pedrycz, Witold ED - Korytkowski, Marcin ED - Scherer, Rafał ED - Rutkowski, Leszek TI - Spectroscopy-Based Prediction of In Vitro Dissolution Profile Using Random Decision Forests T2 - Artificial Intelligence and Soft Computing PB - Springer Netherlands CY - Cham SN - 9783031234927 T3 - Lecture Notes in Computer Science, ISSN 0302-9743 ; 13588. PY - 2023 SP - 411 EP - 422 PG - 12 DO - 10.1007/978-3-031-23492-7_35 UR - https://m2.mtmt.hu/api/publication/33595245 ID - 33595245 LA - English DB - MTMT ER - TY - JOUR AU - Budai, Ádám AU - Suhai, Ferenc Imre AU - Csorba, Kristóf AU - Dohy, Zsófia AU - Szabó, Liliána AU - Merkely, Béla Péter AU - Vágó, Hajnalka TI - Automated Classification of Left Ventricular Hypertrophy on Cardiac MRI JF - APPLIED SCIENCES-BASEL J2 - APPL SCI-BASEL VL - 12 PY - 2022 IS - 9 PG - 16 SN - 2076-3417 DO - 10.3390/app12094151 UR - https://m2.mtmt.hu/api/publication/32793973 ID - 32793973 N1 - Department of Automation and Applied Informatics, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Budapest, H-1111, Hungary The Heart and Vascular Center, Faculty of Medicine, Semmelweis University, Budapest, H-1122, Hungary Export Date: 9 November 2022 Correspondence Address: Budai, A.; Department of Automation and Applied Informatics, Hungary; email: budai.adam@aut.bme.hu Funding text 1: Funding: This study was supported by the National Research, Development and Innovation Office of Hungary (NKFIA; 019-1.1.1-PIACI-KFI-2019-00263). This study was also supported by the National Research, Development and Innovation Office of Hungary (NKFIA; NVKP-16-1-2016-0017). The study was financed by the Research Excellence Programme of the Ministry for Innovation and Technology in Hungary within the framework of the Bioimaging Thematic Programme of Semmelweis University and by the Ministry of Innovation and Technology NRDI Office within the framework of the Artificial Intelligence National Laboratory Program. Project no. TKP2021-NKTA-46 has been implemented with the support provided by the Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund, financed under the TKP2021-NKTA funding scheme. AB - Left ventricular hypertrophy is an independent predictor of coronary artery disease, stroke, and heart failure. Our aim was to detect LVH cardiac magnetic resonance (CMR) scans with automatic methods. We developed an ensemble model based on a three-dimensional version of ResNet. The input of the network included short-axis and long-axis images. We also introduced a standardization methodology to unify the input images for noise reduction. The output of the network is the decision whether the patient has hypertrophy or not. We included 428 patients (mean age: 49 ± 18 years, 262 males) with LVH (346 hypertrophic cardiomyopathy, 45 cardiac amyloidosis, 11 Anderson–Fabry disease, 16 endomyocardial fibrosis, 10 aortic stenosis). Our control group consisted of 234 healthy subjects (mean age: 35 ± 15 years; 126 males) without any known cardiovascular diseases. The developed machine-learning-based model achieved a 92% F1-score and 97% recall on the hold-out dataset, which is comparable to the medical experts. Experiments showed that the standardization method was able to significantly boost the performance of the algorithm. The algorithm could improve the diagnostic accuracy, and it could open a new door to AI applications in CMR. LA - English DB - MTMT ER - TY - JOUR AU - Domokos, András AU - Madarász, Lajos AU - Stoffán, György Nimród AU - Tacsi, Kornélia AU - Galata, Dorián László AU - Csorba, Kristóf AU - Vass, Panna AU - Nagy, Zsombor Kristóf AU - Pataki, Hajnalka TI - Real-Time Monitoring of Continuous Pharmaceutical Mixed Suspension Mixed Product Removal Crystallization Using Image Analysis JF - ORGANIC PROCESS RESEARCH & DEVELOPMENT J2 - ORG PROCESS RES DEV VL - 26 PY - 2022 IS - 1 SP - 149 EP - 158 PG - 10 SN - 1083-6160 DO - 10.1021/acs.oprd.1c00372 UR - https://m2.mtmt.hu/api/publication/32601462 ID - 32601462 N1 - Budapest University of Technology and Economics, Department of Organic Chemistry and Technology, Budapest, H-1111, Hungary Budapest University of Technology and Economics, Department of Automation and Applied Informatics, Budapest, H-1111, Hungary Export Date: 26 January 2022 CODEN: OPRDF Correspondence Address: Vass, P.; Budapest University of Technology and Economics, Hungary; email: panna.vass@oct.bme.hu AB - In this work, we developed an in-line image analysis system for the monitoring of the continuous crystallization of an active pharmaceutical ingredient. Acetylsalicylic acid was crystallized in a mixed suspension mixed product removal crystallizer, which was equipped with overflow tubing as an outlet. A steep glass plate was placed under the outlet onto which the slurry dripped on its surface. The glass plate spread and guided the droplets toward the product collection filter. A high-speed process camera was mounted above the glass plate to capture images of the crystals. Several light sources were tested in various positions to find the appropriate experimental setup for the optimal image quality. Samples were taken during continuous operation for off-line particle size analysis in order to compare to the crystal size distributions calculated from the images. The results were in good agreement, and the trends of the process could be followed well using the images. As a next step, image analysis was operated throughout the entire continuous crystallization experiment, and a huge quantity of information was collected from the process. The crystal size distribution of the product was calculated every 30 s, which provided a thorough and detailed insight into the crystallization process. LA - English DB - MTMT ER -