Predicting Pancreatic Cancer Using Support Vector Machine

This report presents an approach to predict pancreatic cancer using Support Vector Machine Classification algorithm. The research objective of this project it to predict pancreatic cancer on just genomic, just clinical and combination of genomic and clinical data. We have used real genomic data having 22,763 samples and 154 features per sample. We have also created Synthetic Clinical data having 400 samples and 7 features per sample in order to predict accuracy of just clinical data. To validate the hypothesis, we have combined synthetic clinical data with subset of features from real genomic data. In our results, we observed that prediction accuracy, precision, recall with just genomic data is 80.77%, 20%, 4%. Prediction accuracy, precision, recall with just synthetic clinical data is 93.33%, 95%, 30%. While prediction accuracy, precision, recall for combination of real genomic and synthetic clinical data is 90.83%, 10%, 5%. The combination of real genomic and synthetic clinical data decreased the accuracy since the genomic data is weakly correlated. Thus we conclude that the combination of genomic and clinical data does not improve pancreatic cancer prediction accuracy. A dataset with more significant genomic features might help to predict pancreatic cancer more accurately

Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future.

Treatment planning is an essential step of the radiotherapy workflow. It has become more sophisticated over the past couple of decades with the help of computer science, enabling planners to design highly complex radiotherapy plans to minimize the normal tissue damage while persevering sufficient tumor control. As a result, treatment planning has become more labor intensive, requiring hours or even days of planner effort to optimize an individual patient case in a trial-and-error fashion. More recently, artificial intelligence has been utilized to automate and improve various aspects of medical science. For radiotherapy treatment planning, many algorithms have been developed to better support planners. These algorithms focus on automating the planning process and/or optimizing dosimetric trade-offs, and they have already made great impact on improving treatment planning efficiency and plan quality consistency. In this review, the smart planning tools in current clinical use are summarized in 3 main categories: automated rule implementation and reasoning, modeling of prior knowledge in clinical practice, and multicriteria optimization. Novel artificial intelligence-based treatment planning applications, such as deep learning-based algorithms and emerging research directions, are also reviewed. Finally, the challenges of artificial intelligence-based treatment planning are discussed for future works

Diabetes Prediction Using Artificial Neural Network

Diabetes is one of the most common diseases worldwide where a cure is not found for it yet. Annually it cost a lot of money to care for people with diabetes. Thus the most important issue is the prediction to be very accurate and to use a reliable method for that. One of these methods is using artificial intelligence systems and in particular is the use of Artificial Neural Networks (ANN). So in this paper, we used artificial neural networks to predict whether a person is diabetic or not. The criterion was to minimize the error function in neural network training using a neural network model. After training the ANN model, the average error function of the neural network was equal to 0.01 and the accuracy of the prediction of whether a person is diabetics or not was 87.3

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Personalized pancreatic cancer management : a systematic review of how machine learning is supporting decision-making

This review critically analyzes how machine learning is being utilized to support clinical decision-making in the management of potentially resectable pancreatic cancer. Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, electronic searches of MEDLINE, Embase, PubMed and Cochrane Database were undertaken. Studies were assessed using the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modeling Studies (CHARMS) checklist. In total 89,959 citations were retrieved. Six studies met the inclusion criteria. Three studies were Markov decision-analysis models comparing neoadjuvant therapy versus upfront surgery. Three studies predicted survival time using Bayesian modeling (n = 1), Artificial Neural Network (n = 1), and one study explored machine learning algorithms including: Bayesian Network, decision trees, nearest neighbor, and Artificial Neural Networks. The main methodological issues identified were: limited data sources which limits generalizability and potentiates bias, lack of external validation, and the need for transparency in methods of internal validation, consecutive sampling, and selection of candidate predictors. The future direction of research relies on expanding our view of the multidisciplinary team to include professionals from computing and data science with algorithms developed in conjunction with clinicians and viewed as aids, not replacement, to traditional clinical decision making

Applying artificial intelligence to big data in hepatopancreatic and biliary surgery: a scoping review

Aim: Artificial Intelligence (AI) and its applications in healthcare are rapidly developing. The healthcare industry generates ever-increasing volumes of data that should be used to improve patient care. This review aims to examine the use of AI and its applications in hepatopancreatic and biliary (HPB) surgery, highlighting studies leveraging large datasets.Methods: A PRISMA-ScR compliant scoping review using Medline and Google Scholar databases was performed (5th August 2022). Studies focusing on the development and application of AI to HPB surgery were eligible for inclusion. We undertook a conceptual mapping exercise to identify key areas where AI is under active development for use in HPB surgery. We considered studies and concepts in the context of patient pathways - before surgery (including diagnostics), around the time of surgery (supporting interventions) and after surgery (including prognostication).Results: 98 studies were included. Most studies were performed in China or the USA (n = 45). Liver surgery was the most common area studied (n = 51). Research into AI in HPB surgery has increased rapidly in recent years, with almost two-thirds published since 2019 (61/98). Of these studies, 11 have focused on using “big data” to develop and apply AI models. Nine of these studies came from the USA and nearly all focused on the application of Natural Language Processing. We identified several critical conceptual areas where AI is under active development, including improving preoperative optimization, image guidance and sensor fusion-assisted surgery, surgical planning and simulation, natural language processing of clinical reports for deep phenotyping and prediction, and image-based machine learning.Conclusion: Applications of AI in HPB surgery primarily focus on image analysis and computer vision to address diagnostic and prognostic uncertainties. Virtual 3D and augmented reality models to support complex HPB interventions are also under active development and likely to be used in surgical planning and education. In addition, natural language processing may be helpful in the annotation and phenotyping of disease, leading to new scientific insights

Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Applications

As the most lethal major cancer, pancreatic cancer is a global healthcare challenge. Personalized medicine utilizing cutting-edge multi-omics data holds potential for major breakthroughs in tackling this critical problem. Radiomics and deep learning, two trendy quantitative imaging methods that take advantage of data science and modern medical imaging, have shown increasing promise in advancing the precision management of pancreatic cancer via diagnosing of precursor diseases, early detection, accurate diagnosis, and treatment personalization and optimization. Radiomics employs manually-crafted features, while deep learning applies computer-generated automatic features. These two methods aim to mine hidden information in medical images that is missed by conventional radiology and gain insights by systematically comparing the quantitative image information across different patients in order to characterize unique imaging phenotypes. Both methods have been studied and applied in various pancreatic cancer clinical applications. In this review, we begin with an introduction to the clinical problems and the technology. After providing technical overviews of the two methods, this review focuses on the current progress of clinical applications in precancerous lesion diagnosis, pancreatic cancer detection and diagnosis, prognosis prediction, treatment stratification, and radiogenomics. The limitations of current studies and methods are discussed, along with future directions. With better standardization and optimization of the workflow from image acquisition to analysis and with larger and especially prospective high-quality datasets, radiomics and deep learning methods could show real hope in the battle against pancreatic cancer through big data-based high-precision personalization