Recent Advances of Artificial Intelligence Applications in Interstitial Lung Diseases.

Interstitial lung diseases (ILDs) comprise a rather heterogeneous group of diseases varying in pathophysiology, presentation, epidemiology, diagnosis, treatment and prognosis. Even though they have been recognized for several years, there are still areas of research debate. In the majority of ILDs, imaging modalities and especially high-resolution Computed Tomography (CT) scans have been the cornerstone in patient diagnostic approach and follow-up. The intricate nature of ILDs and the accompanying data have led to an increasing adoption of artificial intelligence (AI) techniques, primarily on imaging data but also in genetic data, spirometry and lung diffusion, among others. In this literature review, we describe the most prominent applications of AI in ILDs presented approximately within the last five years. We roughly stratify these studies in three categories, namely: (i) screening, (ii) diagnosis and classification, (iii) prognosis.

Adherence and quality of life assessment in patients with asthma treatment with budesonide/formoterol via the Elpenhaler device: the COMPLETE study.

BACKGROUND: Asthma is a chronic inflammatory disease of the airways that causes recurring episodes of wheezing, breathlessness, chest tightness and coughing. Inhaled drugs on a daily basis are the cornerstone of asthma treatment, therefore, patient adherence is very important. METHODS: We performed a multicenter, open, non-interventional, observational, prospective study of 716 adult patients diagnosed with asthma receiving FDC (Fixed-dose combination) budesonide/formoterol via the Elpenhaler device. We assessed the adherence to treatment at 3 and 6 months (based on the MMAS-8: 8-item Morisky Medication Adherence Scale), the quality of life and change in forced expiratory volume in 1 s (FEV1) from baseline to follow-up. RESULTS: Approximately 80% of the patients showed medium to high adherence throughout the study. The mean (SD) MMAS-8 score at 6 months was 6.85 (1.54) and we observed a statistically significant shift of patients from the low adherence group to the high adherence group at 6 months. Moreover, after 6 months of treatment with FDC budesonide/formoterol, we observed an increase in the patients' quality of life that as expressed by a change 2.01 (95%CI 1.93-2.10) units in Mini AQLQ (p < 0.0001) that was more pronounced in the high adherence group. The same trend was also observed in terms of spirometry (mean FEV1 2.58 L (0.85) at the end of the study, increased by 220 mL from baseline) with a higher improvement in the medium and high adherence groups. CONCLUSIONS: Treatment with FDC of budesonide/formoterol via the Elpenhaler device was associated with improvement in asthma-related quality of life and lung function over 6 months that were more prominent in patients with higher adherence. TRIAL REGISTRATION: 2017-HAL-EL-74 (ClinicalTrials.gov Identifier: NCT03300076).

Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis.

Artificial Intelligence (AI) has recently altered the landscape of cancer research and medical oncology using traditional Machine Learning (ML) algorithms and cutting-edge Deep Learning (DL) architectures. In this review article we focus on the ML aspect of AI applications in cancer research and present the most indicative studies with respect to the ML algorithms and data used. The PubMed and dblp databases were considered to obtain the most relevant research works of the last five years. Based on a comparison of the proposed studies and their research clinical outcomes concerning the medical ML application in cancer research, three main clinical scenarios were identified. We give an overview of the well-known DL and Reinforcement Learning (RL) methodologies, as well as their application in clinical practice, and we briefly discuss Systems Biology in cancer research. We also provide a thorough examination of the clinical scenarios with respect to disease diagnosis, patient classification and cancer prognosis and survival. The most relevant studies identified in the preceding year are presented along with their primary findings. Furthermore, we examine the effective implementation and the main points that need to be addressed in the direction of robustness, explainability and transparency of predictive models. Finally, we summarize the most recent advances in the field of AI/ML applications in cancer research and medical oncology, as well as some of the challenges and open issues that need to be addressed before data-driven models can be implemented in healthcare systems to assist physicians in their daily practice.

Artificial intelligence techniques in asthma: a systematic review and critical appraisal of the existing literature.

Artificial intelligence (AI) when coupled with large amounts of well characterised data can yield models that are expected to facilitate clinical practice and contribute to the delivery of better care, especially in chronic diseases such as asthma.The purpose of this paper is to review the utilisation of AI techniques in all aspects of asthma research, i.e. from asthma screening and diagnosis, to patient classification and the overall asthma management and treatment, in order to identify trends, draw conclusions and discover potential gaps in the literature.We conducted a systematic review of the literature using PubMed and DBLP from 1988 up to 2019, yielding 425 articles; after removing duplicate and irrelevant articles, 98 were further selected for detailed review.The resulting articles were organised in four categories, and subsequently compared based on a set of qualitative and quantitative factors. Overall, we observed an increasing adoption of AI techniques for asthma research, especially within the last decade.AI is a scientific field that is in the spotlight, especially the last decade. In asthma there are already numerous studies; however, there are certain unmet needs that need to be further elucidated.

Validation of the portable Bluetooth® Air Next spirometer in patients with different respiratory diseases.

BACKGROUND: Chronic respiratory diseases constitute a considerable part in the practice of pulmonologists and primary care physicians; spirometry is integral for the diagnosis and monitoring of these diseases, yet remains underutilized. The Air Next spirometer (NuvoAir, Sweden) is a novel ultra-portable device that performs spirometric measurements connected to a smartphone or tablet via Bluetooth®. METHODS: The objective of this study was to assess the accuracy and validity of these measurements by comparing them with the ones obtained with a conventional desktop spirometer. Two hundred subjects were enrolled in the study with various spirometric patterns (50 patients with asthma, 50 with chronic obstructive pulmonary disease and 50 with interstitial lung disease) as well as 50 healthy individuals. RESULTS: For the key spirometric parameters in the interpretation of spirometry, i.e. FEV1, FVC, FEV1/FVC and FEF25-75%, Pearson correlation and Interclass Correlation Coefficient were greater than 0.94, exhibiting perfect concordance between the two spirometers. Similar results were observed in an exploratory analysis of the subgroups of patients. Using Bland-Altman plots we have shown good reproducibility in the measurements between the two devices, with small mean differences for the evaluated spirometric parameters and the majority of measurements being well within the limits of agreement. CONCLUSIONS: Our results support the use of Air Next as a reliable spirometer for the screening and diagnosis of various spirometric patterns in clinical practice.

Prediction of time dependent survival in HF patients after VAD implantation using pre- and post-operative data.

Heart failure is one of the most common diseases worldwide. In recent years, Ventricular Assist Devices (VADs) have become a valuable option for patients with advanced HF. Although it has been shown that VADs improve patient survival rates, several complications persist during left VAD (LVAD) support. The stratification scores currently employed are mostly generic, i.e. not specifically built for LVAD patients, and are based on pre-implantation patient data. In this work we apply data mining approaches for the prediction of time dependent survival in patients after LVAD implantation. Moreover, the predictions acquired with the use of pre-implantation data are enriched by employing post-implantation data, i.e. follow-up data. Different clinical scenarios have been depicted and the subsequent conditions are tested in order to identify the optimal set of pre- and post-implant features, as well as the most suitable algorithms for feature selection and prediction. The proposed approach is applied to a real dataset of 71 patients, reporting an accuracy of 84.5%, sensitivity of 87% and specificity of 82%. Based on the reported results, expert cardio-surgeons can be supported in planning the treatment of VAD patients.

Prediction of oral cancer recurrence using dynamic Bayesian networks.

We propose a methodology for predicting oral cancer recurrence using Dynamic Bayesian Networks. The methodology takes into consideration time series gene expression data collected at the follow-up study of patients that had or had not suffered a disease relapse. Based on that knowledge, our aim is to infer the corresponding dynamic Bayesian networks and subsequently conjecture about the causal relationships among genes within the same time-slice and between consecutive time-slices. Moreover, the proposed methodology aims to (i) assess the prognosis of patients regarding oral cancer recurrence and at the same time, (ii) provide important information about the underlying biological processes of the disease.

Machine learning applications in cancer prognosis and prediction.

Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The importance of classifying cancer patients into high or low risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions. In addition, the ability of ML tools to detect key features from complex datasets reveals their importance. A variety of these techniques, including Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs) and Decision Trees (DTs) have been widely applied in cancer research for the development of predictive models, resulting in effective and accurate decision making. Even though it is evident that the use of ML methods can improve our understanding of cancer progression, an appropriate level of validation is needed in order for these methods to be considered in the everyday clinical practice. In this work, we present a review of recent ML approaches employed in the modeling of cancer progression. The predictive models discussed here are based on various supervised ML techniques as well as on different input features and data samples. Given the growing trend on the application of ML methods in cancer research, we present here the most recent publications that employ these techniques as an aim to model cancer risk or patient outcomes.

A Bayesian Network-based approach for discovering oral cancer candidate biomarkers.

Oral cancer can arise in the head and neck region. Due to the aggressive nature of the disease, which often leads to poor prognosis, Oral Squamous Cell Carcinoma (OSCC) constitutes the 8(th) most common neoplasms in humans. In the present work we formulate gene interaction network from oral cancer genomic data using Dynamic Bayesian Networks (DBNs). Four modules were extracted after applying a clustering technique to the network. We consequently explore them by applying topological and functional analysis methods in order to identify significant network nodes. Our analysis revealed that these important nodes may correspond to candidate biomarkers of the disease.

Sequence patterns mediating functions of disordered proteins.

Disordered proteins lack specific 3D structure in their native state and have been implicated with numerous cellular functions as well as with the induction of severe diseases, e.g., cardiovascular and neurodegenerative diseases as well as diabetes. Due to their conformational flexibility they are often found to interact with a multitude of protein molecules; this one-to-many interaction which is vital for their versatile functioning involves short consensus protein sequences, which are normally detected using slow and cumbersome experimental procedures. In this work we exploit information from disorder-oriented protein interaction networks focused specifically on humans, in order to assemble, by means of overrepresentation, a set of sequence patterns that mediate the functioning of disordered proteins; hence, we are able to identify how a single protein achieves such functional promiscuity. Next, we study the sequential characteristics of the extracted patterns, which exhibit a striking preference towards a very limited subset of amino acids; specifically, residues leucine, glutamic acid, and serine are particularly frequent among the extracted patterns, and we also observe a nontrivial propensity towards alanine and glycine. Furthermore, based on the extracted patterns we set off to infer potential functional implications in order to verify our findings and potentially further extrapolate our knowledge regarding the functioning of disordered proteins. We observe that the extracted patterns are primarily involved with regulation, binding and posttranslational modifications, which constitute the most prominent functions of disordered proteins.

A multiscale approach for modeling atherosclerosis progression.

Progression of atherosclerotic process constitutes a serious and quite common condition due to accumulation of fatty materials in the arterial wall, consequently posing serious cardiovascular complications. In this paper, we assemble and analyze a multitude of heterogeneous data in order to model the progression of atherosclerosis (ATS) in coronary vessels. The patient's medical record, biochemical analytes, monocyte information, adhesion molecules, and therapy-related data comprise the input for the subsequent analysis. As indicator of coronary lesion progression, two consecutive coronary computed tomography angiographies have been evaluated in the same patient. To this end, a set of 39 patients is studied using a twofold approach, namely, baseline analysis and temporal analysis. The former approach employs baseline information in order to predict the future state of the patient (in terms of progression of ATS). The latter is based on an approach encompassing dynamic Bayesian networks whereby snapshots of the patient's status over the follow-up are analyzed in order to model the evolvement of ATS, taking into account the temporal dimension of the disease. The quantitative assessment of our work has resulted in 93.3% accuracy for the case of baseline analysis, and 83% overall accuracy for the temporal analysis, in terms of modeling and predicting the evolvement of ATS. It should be noted that the application of the SMOTE algorithm for handling class imbalance and the subsequent evaluation procedure might have introduced an overestimation of the performance metrics, due to the employment of synthesized instances. The most prominent features found to play a substantial role in the progression of the disease are: diabetes, cholesterol and cholesterol/HDL. Among novel markers, the CD11b marker of leukocyte integrin complex is associated with coronary plaque progression.

Assessment of optimized Markov models in protein fold classification.

Protein fold classification is a challenging task strongly associated with the determination of proteins' structure. In this work, we tested an optimization strategy on a Markov chain and a recently introduced Hidden Markov Model (HMM) with reduced state-space topology. The proteins with unknown structure were scored against both these models. Then the derived scores were optimized following a local optimization method. The Protein Data Bank (PDB) and the annotation of the Structural Classification of Proteins (SCOP) database were used for the evaluation of the proposed methodology. The results demonstrated that the fold classification accuracy of the optimized HMM was substantially higher compared to that of the Markov chain or the reduced state-space HMM approaches. The proposed methodology achieved an accuracy of 41.4% on fold classification, while Sequence Alignment and Modeling (SAM), which was used for comparison, reached an accuracy of 38%.

Prediction of coronary atherosclerosis progression using dynamic Bayesian networks.

In this paper we propose a methodology for predicting the progression of atherosclerosis in coronary arteries using dynamic Bayesian networks. The methodology takes into account patient data collected at the baseline study and the same data collected in the follow-up study. Our aim is to analyze all the different sources of information (Demographic, Clinical, Biochemical profile, Inflammatory markers, Treatment characteristics) in order to predict possible manifestations of the disease; subsequently, our purpose is twofold: i) to identify the key factors that dictate the progression of atherosclerosis and ii) based on these factors to build a model which is able to predict the progression of atherosclerosis for a specific patient, providing at the same time information about the underlying mechanism of the disease.

A multiscale and multiparametric approach for modeling the progression of oral cancer.

BACKGROUND: In this work, we propose a multilevel and multiparametric approach in order to model the growth and progression of oral squamous cell carcinoma (OSCC) after remission. OSCC constitutes the major neoplasm of the head and neck region, exhibiting a quite aggressive nature, often leading to unfavorable prognosis. METHODS: We formulate a Decision Support System assembling a multitude of heterogeneous data sources (clinical, imaging tissue and blood genomic), aiming to capture all manifestations of the disease. Our primary aim is to identify the factors that dictate OSCC progression and subsequently predict potential relapses of the disease. The discrimination potential of each source of data is initially explored separately, and afterwards the individual predictions are combined to yield a consensus decision achieving complete discrimination between patients with and without a disease relapse. Moreover, we collect and analyze gene expression data from circulating blood cells throughout the follow-up period in consecutive time-slices, in order to model the temporal dimension of the disease. For this purpose a Dynamic Bayesian Network (DBN) is employed which is able to capture in a transparent manner the underlying mechanism dictating the disease evolvement, and employ it for monitoring the status and prognosis of the patients after remission. RESULTS: By feeding as input to the DBN data from the baseline visit we achieve accuracy of 86%, which is further improved to complete discrimination when data from the first follow-up visit are also employed. CONCLUSIONS: Knowing in advance the progression of the disease, i.e. identifying groups of patients with higher/lower risk of reoccurrence, we are able to determine the subsequent treatment protocol in a more personalized manner.

Multiparametric decision support system for the prediction of oral cancer reoccurrence.

Oral squamous cell carcinoma (OSCC) constitutes the predominant neoplasm of the head and neck region, featuring particularly aggressive nature, associated with quite unfavorable prognosis. In this work we formulate a Decision Support System (DSS) which integrates a multitude of heterogeneous data (clinical, imaging and genomic), thus, framing all manifestations of the disease. Our primary aim is to identify the factors that dictate OSCC progression and subsequently predict potential relapses (local or metastatic) of the disease. The discrimination potential of each source of data is initially explored separately, and afterwards the individual predictions are combined to yield a consensus decision achieving complete discrimination between patients with and without a disease relapse.

Extraction of consensus protein patterns in regions containing non-proline cis peptide bonds and their functional assessment.

BACKGROUND: In peptides and proteins, only a small percentile of peptide bonds adopts the cis configuration. Especially in the case of amide peptide bonds, the amount of cis conformations is quite limited thus hampering systematic studies, until recently. However, lately the emerging population of databases with more 3D structures of proteins has produced a considerable number of sequences containing non-proline cis formations (cis-nonPro). RESULTS: In our work, we extract regular expression-type patterns that are descriptive of regions surrounding the cis-nonPro formations. For this purpose, three types of pattern discovery are performed: i) exact pattern discovery, ii) pattern discovery using a chemical equivalency set, and iii) pattern discovery using a structural equivalency set. Afterwards, using each pattern as predicate, we search the Eukaryotic Linear Motif (ELM) resource to identify potential functional implications of regions with cis-nonPro peptide bonds. The patterns extracted from each type of pattern discovery are further employed, in order to formulate a pattern-based classifier, which is used to discriminate between cis-nonPro and trans-nonPro formations. CONCLUSIONS: In terms of functional implications, we observe a significant association of cis-nonPro peptide bonds towards ligand/binding functionalities. As for the pattern-based classification scheme, the highest results were obtained using the structural equivalency set, which yielded 70% accuracy, 77% sensitivity and 63% specificity.

Gene expression profiling towards the prediction of oral cancer reoccurrence.

In this work we perform gene expression profiling on tissue specimen obtained from patients with oral squamous cell carcinoma with a twofold aim: i) to identify a limited number of genes that capture perturbations at molecular level dictating the development of a potential disease relapse after remission, and ii) to employ these genes in order to build a classifier that is able to calculate the probability of disease reoccurrence for new patients, subsequently discriminating patients into high and low risk groups based on reoccurrence probability. The proposed analysis yielded 94% overall accuracy, 100% sensitivity and 89% specificity, for discriminating patients with and without a disease relapse.

PBOND: web server for the prediction of proline and non-proline cis/trans isomerization.

PBOND is a web server that predicts the conformation of the peptide bond between any two amino acids. PBOND classifies the peptide bonds into one out of four classes, namely cis imide (cis-Pro), cis amide (cis-nonPro), trans imide (trans-Pro) and trans amide (trans-nonPro). Moreover, for every prediction a reliability index is computed. The underlying structure of the server consists of three stages: (1) feature extraction, (2) feature selection and (3) peptide bond classification. PBOND can handle both single sequences as well as multiple sequences for batch processing. The predictions can either be directly downloaded from the web site or returned via e-mail. The PBOND web server is freely available at http://195.251.198.21/pbond.html.