Cancer is a collection of genetic diseases, with large phenotypic differences and genetic heterogeneity between different types of cancers and even within the same cancer type. Recent advances in genome-wide profiling provide an opportunity to investigate global molecular changes during the development and progression of cancer. Meanwhile, numerous statistical and machine learning algorithms have been designed for the processing and interpretation of high-throughput molecular data. Molecular subtyping studies have allowed the allocation of cancer into homogeneous groups that are considered to harbor similar molecular and clinical characteristics. Furthermore, this has helped researchers to identify both actionable targets for drug design as well as biomarkers for response prediction. In this review, we introduce five frequently applied techniques for generating molecular data, which are microarray, RNA sequencing, quantitative polymerase chain reaction, NanoString and tissue microarray. Commonly used molecular data for cancer subtyping and clinical applications are discussed. Next, we summarize a workflow for molecular subtyping of cancer, including data preprocessing, cluster analysis, supervised classification and subtype characterizations. Finally, we identify and describe four major challenges in the molecular subtyping of cancer that may preclude clinical implementation. We suggest that standardized methods should be established to help identify intrinsic subgroup signatures and build robust classifiers that pave the way toward stratified treatment of cancer patients.

Molecular subtyping of cancer: Current status and moving toward clinical applications

Mobile health apps (MHAs) and medical apps (MAs) are becoming increasingly popular as digital interventions in a wide range of health-related applications in almost all sectors of healthcare. The surge in demand for digital medical solutions has been accelerated by the need for new diagnostic and therapeutic methods in the current coronavirus disease 2019 pandemic. This also applies to clinical practice in gastroenterology, which has, in many respects, undergone a recent digital transformation with numerous consequences that will impact patients and health care professionals in the near future. MHAs and MAs are considered to have great potential, especially for chronic diseases, as they can support the self-management of patients in many ways. Despite the great potential associated with the application of MHAs and MAs in gastroenterology and health care in general, there are numerous challenges to be met in the future, including both the ethical and legal aspects of applying this technology. The aim of this article is to provide an overview of the current status of MHA and MA use in the field of gastroenterology, describe the future perspectives in this field and point out some of the challenges that need to be addressed.

Impact of mobile health and medical applications on clinical practice in gastroenterology.

Capsule endoscopy (CE) has been increasingly utilised in recent years as a minimally invasive tool to investigate the whole gastrointestinal (GI) tract and a range of capsules are currently available for evaluation of upper GI, small bowel, and lower GI pathology. Although CE is undoubtedly an invaluable test for the investigation of small bowel pathology, it presents considerable challenges and limitations, such as long and laborious reading times, risk of missing lesions, lack of bowel cleansing score and lack of locomotion. Artificial intelligence (AI) seems to be a promising tool that may help improve the performance metrics of CE, and consequently translate to better patient care. In the last decade, significant progress has been made to apply AI in the field of endoscopy, including CE. Although it is certain that AI will find soon its place in day-to-day endoscopy clinical practice, there are still some open questions and barriers limiting its widespread application. In this review, we provide some general information about AI, and outline recent advances in AI and CE, issues around implementation of AI in medical practice and potential future applications of AI-aided CE.

Role of Artificial Intelligence in Video Capsule Endoscopy

Artificial intelligence (AI) has received enormous attention and has gone through a transition stage from being a pure statistical tool to being one of the main drivers of modern medicine. The purpose of this systematic review was to determine the use of this technology in dental specialties. This systematic review was conducted according to the PRISMA protocol and the Cochrane Handbook for Systematic Reviews of Interventions. Online databases (PubMed, Ovid via Medline, and web of Science) and manual retrieval of cross references were searched. The selection process yielded 28 studies investigating the acceptability, effectiveness, or feasibility of AI models in various dental subspecialties. The methodological quality and risk of bias of the included studies were analyzed and appraised using the Prediction Study Risk of Bias Assessment Tool (PROBAST). The authors included 28 studies that investigated the use of AI in the dental fields. Six studies in periodontics reported 480 training data sets and 171 test data sets. Eight studies in orthodontics reported 1336 training and 80 test data sets. Five studies in prosthodontics reported 4659 training and 1759 test data sets. Five studies in oral medicine and pathology reported 1151 training and 68 test data sets. Two studies in maxillofacial surgery reported 47 training data sets. Three studies in endodontics reported 142 training and 103 test data sets. AI represents an effective approach to analyze clinical dental data. Further studies, including randomized clinical trials, are needed to confirm the value of this concept in dental practice with the goal of providing data-driven, high performance dental care that can rapidly improve the science, economics, and delivery of optimum treatment options for patients. 

Artificial Intelligence in Dentistry

Fosil yakitlar ile calisan tasitlar, cevrede zararli gazlarin artmasina ve petrol rezervlerinin azalmasina neden olmaktadir. Bu zararlarin minimize edilmesi icin pek cok arastirmaci, buji ateslemeli motorlarda istenilen performansi elde edebilen ve cevreye az oranda egzoz emisyonu birakabilen alternatif yakitlardan birinin de alkollerin oldugunu ileri surmusler ve pek cok deneysel calismalar gerceklestirmislerdir. Bu arastirma calismasinda; buji ateslemeli motorlarda alkol yakit kullanilmasi durumunda motor performansinda, emisyonlarda ve yanma karakteristiklerinde ne gibi degisikliklerin oldugu uzerine gerceklestirilmis calismalar detayli bir sekilde incelenerek tablolar olusturulmus ve bu degisikliklerin nedenleri aciklanmistir. Arastirma ile alkol kullanilmasiyla motor performansinda ve yanma karakteristiklerinde artis oldugu ve egzoz emisyonlarinda ise azalmalarin gerceklestigi sonucuna varilmistir.

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Buji Ateşlemeli Motorlarda Alkol Yakıt Kullanımının Performans, Emisyon ve Yanma Karakteristikleri Açısından İncelenmesi

Machine learning has undergone a transition phase from being a pure statistical tool to being one of the main drivers of modern medicine. In gastroenterology, this technology is motivating a growing number of studies that rely on these innovative methods to deal with critical issues related to this practice. Hence, in the light of the burgeoning research on the use of machine learning in gastroenterology, a systematic review of the literature is timely. In this work, we present the results gleaned through a systematic review of prominent gastroenterology literature using machine learning techniques. Based on the analysis of 88 journal articles, we delimit the scope of application, we discuss current limitations including bias, lack of transparency, accountability, and data availability, and we put forward future avenues.

/pdf/gastroenterology-meets-machine-learning-status-quo-and-quo-3ifgowrgap.pdf

Safae Adadi

Papers

Gastroenterology Meets Machine Learning: Status Quo and Quo Vadis.