RCMNet: A deep learning model assists CAR-T therapy for leukemia

Zhang, Ruitao; Han, Xueying; Lei, Zhengyang; Jiang, Chenyao; Gul, Ijaz; Hu, Qiuyue; Zhai, Shiyao; Lu, Hong; Lian, Lijin; Liu, Ying; Zhang, Yongbing; Dong, Yuhan; Zhang, Can Yang; Lam, Tsz Kwan; Han, Yuxing; Yu, Dongmei; Zhou, Jin; Qin, Peiwu

doi:10.1016/j.compbiomed.2022.106084

Cited by 21 publications

(10 citation statements)

References 27 publications

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“…Use of NLP in the field of CAR-T therapy is extremely rare. As of January 2023, we found no references in the Web of Science engine while the Scopus search engine produced one relevant, extremely recent research paper by Zhang et al ( 26 ). We therefore believe that our application of advanced NLP to mine and analyze CAR-T government regulations is the first of its kind in the pharmaceutical industry.…”

Section: Methodsmentioning

confidence: 87%

Search, identification, and curation of cell and gene therapy product regulations using augmented intelligent systems

et al. 2023

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BackgroundManually keeping up-to-date with regulations such as directives, guidance, laws, and ordinances related to cell and gene therapy is a labor-intensive process. We used machine learning (ML) algorithms to create an augmented intelligent system to optimize systematic screening of global regulations to improve efficiency and reduce overall labor and missed regulations.MethodsCombining Boolean logic and artificial intelligence (i.e., augmented intelligence) for the search process, ML algorithms were used to identify and suggest relevant cell and gene therapy regulations. Suggested regulations were delivered to a landing page for further subject matter expert (SME) tagging of words/phrases to provide system relevance on functional words. Ongoing learning from the repository regulations continued to increase system reliability and performance. The automated ability to train and retrain the system allows for continued refinement and improvement of system accuracy. Automated daily searches for applicable regulations in global databases provide ongoing opportunities to update the repository.ResultsCompared to manual searching, which required 3–4 SMEs to review ~115 regulations, the current system performance, with continuous system learning, requires 1 full-time equivalent to process approximately 9,000 regulations/day. Currently, system performance has 86% overall accuracy, a recommend recall of 87%, and a reject recall of 84%. A conservative search strategy is intentionally used to permit SMEs to assess low-recommended regulations in order to prevent missing any applicable regulations.ConclusionCompared to manual searches, our custom automated search system greatly improves the management of cell and gene therapy regulations and is efficient, cost effective, and accurate.

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Section: Methodsmentioning

confidence: 87%

Search, identification, and curation of cell and gene therapy product regulations using augmented intelligent systems

et al. 2023

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“…The involvement of AI facilitates the diagnosis of diseases by reducing the time and effort of manual (radiologist) examinations. Furthermore, deep learning models can effectively aid in the diagnosis of diseases, including stroke (Xie et al, 2021), leukemia (Zhang et al, 2022), etc.…”

mentioning

confidence: 99%

AI-boosted CRISPR-Cas13a and total internal reflection fluorescence microscopy system for SARS-CoV-2 detection

Zhang

Lei²,

Xiao³

et al. 2022

Front. Sens.

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Integrating artificial intelligence with SARS-CoV-2 diagnostics can help in the timely execution of pandemic control and monitoring plans. To improve the efficiency of the diagnostic process, this study aims to classify fluorescent images via traditional machine learning and deep learning-based transfer learning. A previous study reported a CRISPR-Cas13a system combined with total internal reflection fluorescence microscopy (TIRFM) to detect the existence and concentrations of SARS-CoV-2 by fluorescent images. However, the lack of professional software and excessive manual labor hinder the practicability of the system. Here, we construct a fluorescent image dataset and develop an AI-boosted CRISPR-Cas13a and total internal reflection fluorescence microscopy system for the rapid diagnosis of SARS-CoV-2. Our study proposes Fluorescent Images Classification Transfer learning based on DenseNet-121 (FICTransDense), an approach that uses TIRF images (before and after sample introduction, respectively) for preprocessing, including outlier exclusion and setting and division preprocessing (i.e., SDP). Classification results indicate that the FICTransDense and Decision Tree algorithms outperform other approaches on the SDP dataset. Most of the algorithms benefit from the proposed SDP technique in terms of Accuracy, Recall, F1 Score, and Precision. The use of AI-boosted CRISPR-Cas13a and TIRFM systems facilitates rapid monitoring and diagnosis of SARS-CoV-2.

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“…DL models extract independent features unknown to clinicians; however, face challenges of explainability and interpretability which have been attempted to address by a neuro-symbolic learning study [20]. Deep learning has been applied in different domains specifically in diseases diagnosing, such as melanoma and diabetic retinopathy, and achieved comparable accuracy to that of human experts [44].…”

Section: Literature Studymentioning

confidence: 99%

Futuristic Variations and Analysis in Fundus Images Corresponding to Biological Traits

Hassan¹,

Zhang²,

Ameen³

et al. 2023

Preprint

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Fundus image captures rear of an eye, and which has been studied for the diseases identification, classification, segmentation, generation, and biological traits association using handcrafted, conventional, and deep learning methods. In biological traits estimation, most of the studies have been carried out for the age prediction and gender classification with convincing results. However, the current study utilizes the cutting-edge deep learning (DL) algorithms to estimate biological traits in terms of age and gender together with associating traits to retinal visuals. For the trait's association, our study embeds aging as the label information into the proposed DL model to learn knowledge about the effected regions with aging. Our proposed DL models, named FAG-Net and FGC-Net, correspondingly estimate biological traits (age and gender) and generates fundus images. FAG-Net can generate multiple variants of an input fundus image given a list of ages as conditions. Our study analyzes fundus images and their corresponding association with biological traits, and predicts of possible spreading of ocular disease on fundus images given age as condition to the generative model. Our proposed models outperform the randomly selected stateof-the-art DL models.

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RCMNet: A deep learning model assists CAR-T therapy for leukemia

Cited by 21 publications

References 27 publications

Search, identification, and curation of cell and gene therapy product regulations using augmented intelligent systems

Search, identification, and curation of cell and gene therapy product regulations using augmented intelligent systems

AI-boosted CRISPR-Cas13a and total internal reflection fluorescence microscopy system for SARS-CoV-2 detection

Futuristic Variations and Analysis in Fundus Images Corresponding to Biological Traits

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