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Radiology Radiology

Emerging Role of Artificial Intelligence in Diagnosis, Classification and Clinical Management of Glioma.

In Seminars in cancer biology

Glioma represents a dominant primary intracranial malignancy in the central nervous system. Artificial intelligence that mainly includes machine learning, and deep learning computational approaches, presents a unique opportunity to enhance clinical management of glioma through improving tumor segmentation, diagnosis, differentiation, grading, treatment, prediction of clinical outcomes (prognosis, and recurrence), molecular features, clinical classification, characterization of the tumor microenvironment, and drug discovery. A growing body of recent studies apply artificial intelligence-based models to disparate data sources of glioma, covering imaging modalities, digital pathology, high-throughput multi-omics data (especially emerging single-cell RNA sequencing and spatial transcriptome), etc. While these early findings are promising, future studies are required to normalize artificial intelligence-based models to improve the generalizability and interpretability of the results. Despite prominent issues, targeted clinical application of artificial intelligence approaches in glioma will facilitate the development of precision medicine of this field. If these challenges can be overcome, artificial intelligence has the potential to profoundly change the way patients with or at risk of glioma are provided with more rational care.

Luo Jiefeng, Pan Mika, Mo Ke, Mao Yingwei, Zou Donghua

2023-Mar-10

artificial intelligence, clinical practice, deep learning, digital pathology, glioma, machine learning, radiology

General General

AlphaFold, allosteric, and orthosteric drug discovery: Ways forward.

In Drug discovery today ; h5-index 68.0

Drug discovery is arguably a highly challenging and significant interdisciplinary aim. The stunning success of the artificial intelligence-powered AlphaFold, whose latest version is buttressed by an innovative machine-learning approach that integrates physical and biological knowledge about protein structures, raised drug discovery hopes that unsurprisingly, have not come to bear. Even though accurate, the models are rigid, including the drug pockets. AlphaFold's mixed performance poses the question of how its power can be harnessed in drug discovery. Here we discuss possible ways of going forward wielding its strengths, while bearing in mind what AlphaFold can and cannot do. For kinases and receptors, an input enriched in active (ON) state models can better AlphaFold's chance of rational drug design success. Teaser AlphaFold was heralded as promising to transform drug discovery. To date it has not. We discuss what AlphaFold can and cannot do and suggest how to harness AlphaFold's power, circumventing its lack of structural flexibility.

Nussinov Ruth, Zhang Mingzhen, Liu Yonglan, Jang Hyunbum

2023-Mar-10

ESMfold, activating mutations, artificial intelligence, inhibitors, machine learning, molecular dynamics simulations, orthosteric drugs

General General

Machine learning in additive manufacturing & Microfluidics for smarter and safer drug delivery systems.

In International journal of pharmaceutics ; h5-index 67.0

A new technological passage has emerged in the pharmaceutical field, concerning the management, application, and transfer of knowledge from humans to machines, as well as the implementation of advanced manufacturing and product optimisation processes. Machine Learning (ML) methods have been introduced to Additive Manufacturing (AM) and Microfluidics (MFs) to predict and generate learning patterns for precise fabrication of tailor-made pharmaceutical treatments. Moreover, regarding the diversity and complexity of personalised medicine, ML has been part of quality by design strategy, targeting towards the development of safe and effective drug delivery systems. The utilisation of different and novel ML techniques along with Internet of Things sensors in AM and MFs, have shown promising aspects regarding the development of well-defined automated procedures towards the production of sustainable and quality-based therapeutic systems. Thus, the effective data utilisation, prospects on a flexible and broader production of "on demand" treatments. In this study, a thorough overview has been achieved, concerning scientific achievements of the past decade, which aims to trigger the research interest on incorporating different types of ML in AM and MFs, as essential techniques for the enhancement of quality standards of customised medicinal applications, as well as the reduction of variability potency, throughout a pharmaceutical process.

Dedeloudi Aikaterini, Weaver Edward, Lamprou Dimitrios A

2023-Mar-10

3D printing, Additive manufacturing, Algorithms, Machine learning, Microfluidics, Quality by design

Surgery Surgery

A Machine Learning-Based Prediction of Diabetes Insipidus in Patients Undergoing Endoscopic Transsphenoidal Surgery for Pituitary Adenoma.

In World neurosurgery ; h5-index 47.0

BACKGROUND : Diabetes insipidus (DI) is a common complication following endoscopic transsphenoidal surgery (TSS) for pituitary adenoma (PA), which affects the quality of life in patients. Therefore, there is a need to develop prediction models of postoperative DI specifically for patients who undergo endoscopic TSS. This study establishes and validates prediction models of DI after endoscopic TSS for PA patients using machine learning algorithms.

METHODS : We retrospectively collected PA patients who underwent endoscopic TSS in otorhinolaryngology and neurosurgery departments between January 2018 to December 2020. The patients were randomly split into a training set (70%) and a test set (30%). The four machine learning (ML) algorithms (logistic regression, random forest, support vector machine, and decision tree) were used to establish the prediction models. Area under the receiver operating characteristic curve were calculated to compare the performance of the models.

RESULTS : A total of 232 patients were included, and 78 patients (33.6%) developed transient DI after surgery. Data were randomly divided into a training set (n = 162) and a test set (n = 70) for development and validation of the model, respectively. The Area under the receiver operating characteristic curve was highest in the random forest model (0.815), and lowest in the Logistic regression model (0.601). (Invasion of pituitary stalk was the most important feature for model performance, closely followed by macroadenomas, size classification of PA, tumor texture, and Hardy-Willson suprasellar grade.

CONCLUSIONS : Machine learning algorithms identify preoperative features of importance, and reliably predict DI after endoscopic TSS for PA patients. Such a prediction model may enable clinicians to develop individualized treatment strategy and follow-up management.

Hou Siyuan, Li Xiaomin, Meng Fanyue, Liu Shaokun, Wang Zhenlin

2023-Mar-10

diabetes insipidus, machine learning, pituitary adenoma, prediction model, transsphenoidal surgery

Surgery Surgery

The imitation game: a review of the use of artificial intelligence in colonoscopy, and endoscopists' perceptions thereof.

In Annals of coloproctology

The development of deep learning systems in artificial intelligence (AI) has enabled advances in endoscopy, and AI-aided colonoscopy has recently been ushered into clinical practice as a clinical decision-support tool. This has enabled real-time AI-aided detection of polyps with a higher sensitivity than the average endoscopist, and evidence to support its use has been promising thus far. This review article provides a summary of currently published data relating to AI-aided colonoscopy, discusses current clinical applications, and introduces ongoing research directions. We also explore endoscopists' perceptions and attitudes toward the use of this technology, and discuss factors influencing its uptake in clinical practice.

Tham Sarah, Koh Frederick H, Ladlad Jasmine, Chue Koy-Min, Lin Cui-Li, Teo Eng-Kiong, Foo Fung-Joon

2023-Mar-10

Adenoma, Artificial intelligence, Biodmedical technology assessment, Colonic polyps, Colonoscopy

Radiology Radiology

Predicting muscle invasion in bladder cancer based on MRI: A comparison of radiomics, and single-task and multi-task deep learning.

In Computer methods and programs in biomedicine

BACKGROUND AND OBJECTIVES : Radiomics and deep learning are two popular technologies used to develop computer-aided detection and diagnosis schemes for analysing medical images. This study aimed to compare the effectiveness of radiomics, single-task deep learning (DL) and multi-task DL methods in predicting muscle-invasive bladder cancer (MIBC) status based on T2-weighted imaging (T2WI).

METHODS : A total of 121 tumours (93 for training, from Centre 1; 28 for testing, from Centre 2) were included. MIBC was confirmed with pathological examination. A radiomics model, a single-task model, and a multi-task model based on T2WI were constructed in the training cohort with five-fold cross-validation, and validation was conducted in the external test cohort. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of each model. DeLong's test and a permutation test were used to compare the performance of the models.

RESULTS : The area under the ROC curve (AUC) values of the radiomics, single-task and multi-task models in the training cohort were: 0.920, 0.933 and 0.932, respectively; and were 0.844, 0.884 and 0.932, respectively, in the test cohort. The multi-task model achieved better performance in the test cohort than did the other models. No statistically significant differences in AUC values and Kappa coefficients were observed between pairwise models, in either the training or test cohorts. According to the Grad-CAM feature visualization results, the multi-task model focused more on the diseased tissue area in some samples of the test cohort compared with the single-task model.

CONCLUSIONS : The T2WI-based radiomics, single-task, and multi-task models all exhibited good diagnostic performance in preoperatively predicting MIBC, in which the multi-task model had the best diagnostic performance. Compared with the radiomics method, our multi-task DL method had the advantage of saving time and effort. Compared with the single-task DL method, our multi-task DL method had the advantage of being more lesion-focused and more reliable for clinical reference.

Li Jianpeng, Qiu Zhengxuan, Cao Kangyang, Deng Lei, Zhang Weijing, Xie Chuanmiao, Yang Shuiqing, Yue Peiyan, Zhong Jian, Lyu Jiegeng, Huang Xiang, Zhang Kunlin, Zou Yujian, Huang Bingsheng

2023-Mar-05

Bladder cancer, Deep learning, Magnetic resonance imaging, Multi-task learning, Muscle invasion, Radiomics