Artificial intelligence in diagnosis of obstructive lung disease: current status and future potential

Artificial intelligence to assist decision-making on pharmacotherapy: A feasibility study

Background

Artificial intelligence (AI) has the capability to analyze vast amounts of data and has been applied in various healthcare sectors. However, its effectiveness in aiding pharmacotherapy decision-making remains uncertain due to the intricate, patient-specific, and dynamic nature of this field.

Objective

This study sought to investigate the potential of AI in guiding pharmacotherapy decisions using clinical data such as diagnoses, laboratory results, and vital signs obtained from routine patient care.

Methods

Data of a previous study on medication therapy optimization was updated and adapted for the purpose of this study. Analysis was conducted using R software along with the tidymodels extension packages. The dataset was split into 74% for training and 26% for testing. Decision trees were selected as the primary model due to their simplicity, transparency, and interpretability. To prevent overfitting, bootstrapping techniques were employed, and hyperparameters were fine-tuned. Performance metrics such as areas under the curve and accuracies were computed.

Results

The study cohort comprised 101 elderly patients with multiple diagnoses and complex medication regimens. The AI model demonstrated prediction accuracies ranging from 38% to 100% for various cardiovascular drug classes. Laboratory data and vital signs could not be interpreted, as the effect and dependence were unclear for the model. The study revealed that the issue of AI lag time in responding to sudden changes could be addressed by manually adjusting decision trees, a task not feasible with neural networks.

Conclusion

In conclusion, the AI model exhibited promise in recommending appropriate medications for individual patients. While the study identified several obstacles during model development, most were successfully resolved. Future AI studies need to include the drug effect, not only the drug, if laboratory data is part of the decision. This could assist with interpreting their potential relationship. Human oversight and intervention remain essential for an AI-driven pharmacotherapy decision support system to ensure safe and effective patient care.

Clinical applications of forced oscillation technique (FOT) for diagnosis and management of obstructive lung diseases in children

Obstructive lung diseases such as bronchial asthma, COPD, and cystic fibrosis are a burden on many patients across the globe. Spirometry is considered the gold standard for diagnosing airflow obstruction, but it can be difficult for pediatric patients to do and requires a lot of effort. As a result, healthcare providers need new, effortless methods to diagnose airway obstructions, particularly in young children and individuals unable to perform the spirometry maneuver. The forced oscillation technique is a modern method requiring only tidal breathing combined with the application of external, source of low-amplitude oscillations to evaluate the respiratory system's response. It might be essential for identifying early respiratory changes caused by smoking, childhood asthma, and may prove more sensitive than spirometry in identifying peripheral airway disturbances or evaluating the long-term success of therapy. This review describes the methodology and the indications for the forced oscillation technique and outlines its relevance in clinical practice.

Examining the effectiveness of artificial intelligence applications in asthma and COPD outpatient support in terms of patient health and public cost: SWOT analysis

This research aimed to examine the effectiveness of artificial intelligence applications in asthma and chronic obstructive pulmonary disease (COPD) outpatient treatment support in terms of patient health and public costs. The data obtained in the research using semiotic analysis, content analysis and trend analysis methods were analyzed with strengths, weakness, opportunities, threats (SWOT) analysis. In this context, 18 studies related to asthma, COPD and artificial intelligence were evaluated. The strengths of artificial intelligence applications in asthma and COPD outpatient treatment stand out as early diagnosis, access to more patients and reduced costs. The points that stand out among the weaknesses are the acceptance and use of technology and vulnerabilities related to artificial intelligence. Opportunities arise in developing differential diagnoses of asthma and COPD and in examining prognoses for the diseases more effectively. Malicious use, commercial data leaks and data security issues stand out among the threats. Although artificial intelligence applications provide great convenience in the outpatient treatment process for asthma and COPD diseases, precautions must be taken on a global scale and with the participation of international organizations against weaknesses and threats. In addition, there is an urgent need for accreditation for the practices to be carried out in this regard.

Screening and staging of chronic obstructive pulmonary disease with deep learning based on chest X-ray images and clinical parameters

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is underdiagnosed with the current gold standard measure pulmonary function test (PFT). A more sensitive and simple option for early detection and severity evaluation of COPD could benefit practitioners and patients.

METHODS

In this multicenter retrospective study, frontal chest X-ray (CXR) images and related clinical information of 1055 participants were collected and processed. Different deep learning algorithms and transfer learning models were trained to classify COPD based on clinical data and CXR images from 666 subjects, and validated in internal test set based on 284 participants. External test including 105 participants was also performed to verify the generalization ability of the learning algorithms in diagnosing COPD. Meanwhile, the model was further used to evaluate disease severity of COPD by predicting different grads.

RESULTS

The Ensemble model showed an AUC of 0.969 in distinguishing COPD by simultaneously extracting fusion features of clinical parameters and CXR images in internal test, better than models that used clinical parameters (AUC = 0.963) or images (AUC = 0.946) only. For the external test set, the AUC slightly declined to 0.934 in predicting COPD based on clinical parameters and CXR images. When applying the Ensemble model to determine disease severity of COPD, the AUC reached 0.894 for three-classification and 0.852 for five-classification respectively.

CONCLUSION

The present study used DL algorithms to screen COPD and predict disease severity based on CXR imaging and clinical parameters. The models showed good performance and the approach might be an effective case-finding tool with low radiation dose for COPD diagnosis and staging.

Dallal Bashi O, Nik Ali N, Mahmood Al Kubaisi Y. Lung disease recognition methods using audio-based analysis with machine learning

The use of computer-based automated approaches and improvements in lung sound recording techniques have made lung sound-based diagnostics even better and devoid of subjectivity errors. Using a computer to evaluate lung sound features more thoroughly with the use of analyzing changes in lung sound behavior, recording measurements, suppressing the presence of noise contaminations, and graphical representations are all made possible by computer-based lung sound analysis. This paper starts with a discussion of the need for this research area, providing an overview of the field and the motivations behind it. Following that, it details the survey methodology used in this work. It presents a discussion on the elements of sound-based lung disease classification using machine learning algorithms. This includes commonly prior considered datasets, feature extraction techniques, pre-processing methods, artifact removal methods, lung-heart sound separation, deep learning algorithms, and wavelet transform of lung audio signals. The study introduces studies that review lung screening including a summary table of these references and discusses the literature gaps in the existing studies. It is concluded that the use of sound-based machine learning in the classification of respiratory diseases has promising results. While we believe this material will prove valuable to physicians and researchers exploring sound-signal-based machine learning, large-scale investigations remain essential to solidify the findings and foster wider adoption within the medical community.

Using artificial intelligence to promote equitable care for inpatients with language barriers and complex medical needs: clinical stakeholder perspectives

OBJECTIVES

Inpatients with language barriers and complex medical needs suffer disparities in quality of care, safety, and health outcomes. Although in-person interpreters are particularly beneficial for these patients, they are underused. We plan to use machine learning predictive analytics to reliably identify patients with language barriers and complex medical needs to prioritize them for in-person interpreters.

MATERIALS AND METHODS

This qualitative study used stakeholder engagement through semi-structured interviews to understand the perceived risks and benefits of artificial intelligence (AI) in this domain. Stakeholders included clinicians, interpreters, and personnel involved in caring for these patients or for organizing interpreters. Data were coded and analyzed using NVIVO software.

RESULTS

We completed 49 interviews. Key perceived risks included concerns about transparency, accuracy, redundancy, privacy, perceived stigmatization among patients, alert fatigue, and supply-demand issues. Key perceived benefits included increased awareness of in-person interpreters, improved standard of care and prioritization for interpreter utilization; a streamlined process for accessing interpreters, empowered clinicians, and potential to overcome clinician bias.

DISCUSSION

This is the first study that elicits stakeholder perspectives on the use of AI with the goal of improved clinical care for patients with language barriers. Perceived benefits and risks related to the use of AI in this domain, overlapped with known hazards and values of AI but some benefits were unique for addressing challenges with providing interpreter services to patients with language barriers.

CONCLUSION

Artificial intelligence to identify and prioritize patients for interpreter services has the potential to improve standard of care and address healthcare disparities among patients with language barriers.

The Challenges of Spirometric Diagnosis of COPD

Chronic obstructive pulmonary disease (COPD) is one of the top causes of morbidity and mortality worldwide. Although for many years its accurate diagnosis has been a focus of intense research, it is still challenging. Due to its simplicity, portability, and low cost, spirometry has been established as the main tool to detect this condition, but its flawed performance makes it an imperfect COPD diagnosis gold standard. This review aims to provide an up-to-date literature overview of recent studies regarding COPD diagnosis; we seek to identify their limitations and establish perspectives for spirometric diagnosis of COPD in the XXI century by combining deep clinical knowledge of the disease with advanced computer analysis techniques.

A review on lung disease recognition by acoustic signal analysis with deep learning networks

Recently, assistive explanations for difficulties in the health check area have been made viable thanks in considerable portion to technologies like deep learning and machine learning. Using auditory analysis and medical imaging, they also increase the predictive accuracy for prompt and early disease detection. Medical professionals are thankful for such technological support since it helps them manage further patients because of the shortage of skilled human resources. In addition to serious illnesses like lung cancer and respiratory diseases, the plurality of breathing difficulties is gradually rising and endangering society. Because early prediction and immediate treatment are crucial for respiratory disorders, chest X-rays and respiratory sound audio are proving to be quite helpful together. Compared to related review studies on lung disease classification/detection using deep learning algorithms, only two review studies based on signal analysis for lung disease diagnosis have been conducted in 2011 and 2018. This work provides a review of lung disease recognition with acoustic signal analysis with deep learning networks. We anticipate that physicians and researchers working with sound-signal-based machine learning will find this material beneficial.

Deep learning diagnostic and severity-stratification for interstitial lung diseases and chronic obstructive pulmonary disease in digital lung auscultations and ultrasonography: clinical protocol for an observational case-control study

BACKGROUND

Interstitial lung diseases (ILD), such as idiopathic pulmonary fibrosis (IPF) and non-specific interstitial pneumonia (NSIP), and chronic obstructive pulmonary disease (COPD) are severe, progressive pulmonary disorders with a poor prognosis. Prompt and accurate diagnosis is important to enable patients to receive appropriate care at the earliest possible stage to delay disease progression and prolong survival. Artificial intelligence-assisted lung auscultation and ultrasound (LUS) could constitute an alternative to conventional, subjective, operator-related methods for the accurate and earlier diagnosis of these diseases. This protocol describes the standardised collection of digitally-acquired lung sounds and LUS images of adult outpatients with IPF, NSIP or COPD and a deep learning diagnostic and severity-stratification approach.

METHODS

A total of 120 consecutive patients (≥ 18 years) meeting international criteria for IPF, NSIP or COPD and 40 age-matched controls will be recruited in a Swiss pulmonology outpatient clinic, starting from August 2022. At inclusion, demographic and clinical data will be collected. Lung auscultation will be recorded with a digital stethoscope at 10 thoracic sites in each patient and LUS images using a standard point-of-care device will be acquired at the same sites. A deep learning algorithm (DeepBreath) using convolutional neural networks, long short-term memory models, and transformer architectures will be trained on these audio recordings and LUS images to derive an automated diagnostic tool. The primary outcome is the diagnosis of ILD versus control subjects or COPD. Secondary outcomes are the clinical, functional and radiological characteristics of IPF, NSIP and COPD diagnosis. Quality of life will be measured with dedicated questionnaires. Based on previous work to distinguish normal and pathological lung sounds, we estimate to achieve convergence with an area under the receiver operating characteristic curve of > 80% using 40 patients in each category, yielding a sample size calculation of 80 ILD (40 IPF, 40 NSIP), 40 COPD, and 40 controls.

DISCUSSION

This approach has a broad potential to better guide care management by exploring the synergistic value of several point-of-care-tests for the automated detection and differential diagnosis of ILD and COPD and to estimate severity. Trial registration Registration: August 8, 2022.

CLINICALTRIALS

gov Identifier: NCT05318599.

Acoustic-Based Deep Learning Architectures for Lung Disease Diagnosis: A Comprehensive Overview

Lung auscultation has long been used as a valuable medical tool to assess respiratory health and has gotten a lot of attention in recent years, notably following the coronavirus epidemic. Lung auscultation is used to assess a patient's respiratory role. Modern technological progress has guided the growth of computer-based respiratory speech investigation, a valuable tool for detecting lung abnormalities and diseases. Several recent studies have reviewed this important area, but none are specific to lung sound-based analysis with deep-learning architectures from one side and the provided information was not sufficient for a good understanding of these techniques. This paper gives a complete review of prior deep-learning-based architecture lung sound analysis. Deep-learning-based respiratory sound analysis articles are found in different databases including the Plos, ACM Digital Libraries, Elsevier, PubMed, MDPI, Springer, and IEEE. More than 160 publications were extracted and submitted for assessment. This paper discusses different trends in pathology/lung sound, the common features for classifying lung sounds, several considered datasets, classification methods, signal processing techniques, and some statistical information based on previous study findings. Finally, the assessment concludes with a discussion of potential future improvements and recommendations.

A Novel Approach for Prediction of Lung Disease Using Chest X-ray Images Based on DenseNet and MobileNet

Covid19 corona virus has caused widespread disruption across the world, in terms of the health, economy, and society problems. X-ray images of the chest can be helpful in making an accurate diagnosis because the corona virus typically first manifests its symptoms in patients' lungs. In this study, a classification method based on deep learning is proposed as a means of identifying lung disease from chest X-ray images. In the proposed study, the detection of covid19 corona virus disease from chest X-ray images was made with MobileNet and Densenet models, which are deep learning methods. Several different use cases can be built with the help of MobileNet model and case modelling approach is utilized to achieve 96% accuracy and an Area Under Curve (AUC) value of 94%. According to the result, the proposed method may be able to more accurately identify the signs of an impurity from dataset of chest X-ray images. This research also compares various performance parameters such as precision, recall and F1-Score.

Coronavirus: a comparative analysis of detection technologies in the wake of emerging variants

An outbreak of the coronavirus disease caused by a novel pathogen created havoc and continues to affect the entire world. As the pandemic progressed, the scientific community was faced by the limitations of existing diagnostic methods. In this review, we have compared the existing diagnostic techniques such as reverse transcription polymerase chain reaction (RT-PCR), antigen and antibody detection, computed tomography scan, etc. and techniques in the research phase like microarray, artificial intelligence, and detection using novel materials; on the prospect of sample preparation, detection procedure (qualitative/quantitative), detection time, screening efficiency, cost-effectiveness, and ability to detect different variants. A detailed comparison of different techniques showed that RT-PCR is still the most widely used and accepted coronavirus detection method despite certain limitations (single gene targeting- in context to mutations). New methods with similar efficiency that could overcome the limitations of RT-PCR may increase the speed, simplicity, and affordability of diagnosis. In addition to existing devices, we have also discussed diagnostic devices in the research phase showing high potential for clinical use. Our approach would be of enormous benefit in selecting a diagnostic device under a given scenario, which would ultimately help in controlling the current pandemic caused by the coronavirus, which is still far from over with new variants emerging.

A convolutional neural network-based system for detecting early gastric cancer in white-light endoscopy

BACKGROUND

White-light endoscopy (WLE) is a main and standard modality for detection of early gastric cancer (EGC). The detection rate of EGC is not satisfactory so far. In this single-center retrospective study we developed a convolutional neural network (CNN)-based system to automatically detect EGC in WLE images.

METHODS

An EGC detecting system was constructed based on the CNN architecture EfficientDet. We trained our system with a data set including 4527 images from 130 cases (cancerous images, 1737; noncancerous images, 2790). Then we tested its performance with a data set including 1243 images from 64 cases (cancerous images, 445; noncancerous images, 798).

RESULTS

For case-based analysis, our system successfully detected EGC in 63 of 64 cases and the sensitivity was 98.4%. For image-based analysis, the accuracy was 88.3%. The sensitivity, specificity, positive predictive value and negative predictive value were 84.5%, 90.5%, 83.2% and 91.3%, respectively. The most common cause for false positives was gastritis (57.9%). The most common cause for false negatives was that the lesion was too small with a diameter of 10 mm or less (44.9%).

CONCLUSION

Our CNN-based EGC detecting system was able to achieve satisfactory sensitivity for detecting EGC in WLE images and shows great potential in assisting endoscopists with the detection of EGC.

A Computer-based Analysis for Identification and Quantification of Small Airway Disease in Lung Computed Tomography Images: A Comprehensive Review for Radiologists

Computed tomography (CT) imaging is being increasingly used in clinical practice for detailed characterization of lung diseases. Respiratory diseases involve various components of the lung, including the small airways. Evaluation of small airway disease on CT images is challenging as the airways cannot be visualized directly by a CT scanner. Small airway disease can manifest as pulmonary air trapping (AT). Although AT may be sometimes seen as mosaic attenuation on expiratory CT images, it is difficult to identify diffuse AT visually. Computer technology advances over the past decades have provided methods for objective quantification of small airway disease on CT images. Quantitative CT (QCT) methods are being rapidly developed to quantify underlying lung diseases with greater precision than subjective visual assessment of CT images. A growing body of evidence suggests that QCT methods can be practical tools in the clinical setting to identify and quantify abnormal regions of the lung accurately and reproducibly. This review aimed to describe the available methods for the identification and quantification of small airway disease on CT images and to discuss the challenges of implementing QCT metrics in clinical care for patients with small airway disease.

AI-Aided Disease Prediction in Visualized Medicine

Artificial intelligence (AI) is playing a vitally important role in promoting the revolution of future technology. Healthcare is one of the promising applications in AI, which covers medical imaging, diagnosis, robotics, disease prediction, pharmacy, health management, and hospital management. Numbers of achievements that made in these fields overturn every aspect in traditional healthcare system. Therefore, to understand the state-of-art AI in healthcare, as well as the chances and obstacles in its development, the applications of AI in disease detection and outlook and the future trends of AI-aided disease prediction were discussed in this chapter.

An Improved Endoscopic Automatic Classification Model for Gastroesophageal Reflux Disease Using Deep Learning Integrated Machine Learning

Gastroesophageal reflux disease (GERD) is a common digestive tract disease, and most physicians use the Los Angeles classification and diagnose the severity of the disease to provide appropriate treatment. With the advancement of artificial intelligence, deep learning models have been used successfully to help physicians with clinical diagnosis. This study combines deep learning and machine learning techniques and proposes a two-stage process for endoscopic classification in GERD, including transfer learning techniques applied to the target dataset to extract more precise image features and machine learning algorithms to build the best classification model. The experimental results demonstrate that the performance of the GerdNet-RF model proposed in this work is better than that of previous studies. Test accuracy can be improved from 78.8% ± 8.5% to 92.5% ± 2.1%. By enhancing the automated diagnostic capabilities of AI models, patient health care will be more assured.

Automated interpretation of the pulmonary function test by a portable spirometer in Chinese adults

Introduction

A portable spirometer is a promising alternative to a traditional pulmonary function test (PFT) spirometer for respiratory function evaluation.

Objectives

This study aimed to investigate the accuracy of automated interpretation of the PFT measured by a portable Yue Cloud spirometer in Chinese adults.

Methods

The PFT was performed to evaluate subjects prospectively enrolled at Ruijin Hospital (n = 220). A Yue Cloud spirometer and a conventional Jaeger MasterScreen device were applied to each patient with a 20‐min quiescent period between each measurement. Pulmonary function parameters, including forced vital capacity (FVC), forced expiratory volume in the first second (FEV1), peak expiratory flow (PEF), maximal expiratory flow at 25%, 50%, and 75% of the FVC (MEF25, MEF50, and MEF75, respectively), and maximal mid‐expiratory flow (MMEF), were compared by correlation analyses and Bland–Altman methods. The Yue Cloud spirometer automatically interpreted the PFT results, and a conventional strategy was performed to interpret the PFT results obtained by the Jaeger machine. Concordance of the categorization of pulmonary dysfunction, small airway dysfunction, and severity was analyzed by the kappa (κ) statistic.

Results

Significantly similar correlations of all variables measured with the two spirometers were observed (all p < 0.001). No significant bias was observed in any of the measured spirometer variables. A satisfactory concordance of pulmonary function and severity classification was observed between the automated interpretation results obtained with the Yue Cloud spirometer vs. a conventional spirometer interpretation strategy (all κ > 0.80).

Conclusion

The portable Yue Cloud spirometer not only yields reliable measurements of pulmonary function but also can automatically interpret the PFT results.

Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis

This literature research had two main objectives. The first objective was to quantify how frequently artificial intelligence (AI) was utilized in dental literature from 2011 until 2021. The second objective was to distinguish the focus of such publications; in particular, dental field and topic. The main inclusion criterium was an original article or review in English focused on dental utilization of AI. All other types of publications or non-dental or non-AI-focused were excluded. The information sources were Web of Science, PubMed, Scopus, and Google Scholar, queried on 19 April 2022. The search string was “artificial intelligence” AND (dental OR dentistry OR tooth OR teeth OR dentofacial OR maxillofacial OR orofacial OR orthodontics OR endodontics OR periodontics OR prosthodontics). Following the removal of duplicates, all remaining publications were returned by searches and were screened by three independent operators to minimize the risk of bias. The analysis of 2011–2021 publications identified 4413 records, from which 1497 were finally selected and calculated according to the year of publication. The results confirmed a historically unprecedented boom in AI dental publications, with an average increase of 21.6% per year over the last decade and a 34.9% increase per year over the last 5 years. In the achievement of the second objective, qualitative assessment of dental AI publications since 2021 identified 1717 records, with 497 papers finally selected. The results of this assessment indicated the relative proportions of focal topics, as follows: radiology 26.36%, orthodontics 18.31%, general scope 17.10%, restorative 12.09%, surgery 11.87% and education 5.63%. The review confirms that the current use of artificial intelligence in dentistry is concentrated mainly around the evaluation of digital diagnostic methods, especially radiology; however, its implementation is expected to gradually penetrate all parts of the profession.

Pilot study on nocturnal monitoring of crackles in children with pneumonia

BACKGROUND

The clinical diagnosis of pneumonia is usually based on crackles at auscultation, but it is not yet clear what kind of crackles are the characteristic features of pneumonia in children. Lung sound monitoring can be used as a "longtime stethoscope". Therefore, it was the aim of this pilot study to use a lung sound monitor system to detect crackles and to differentiate between fine and coarse crackles in children with acute pneumonia. The change of crackles during the course of the disease shall be investigated in a follow-up study.

PATIENTS AND METHODS

Crackles were recorded overnight from 22:00 to 06:00 h in 30 children with radiographically confirmed pneumonia. The data for a total of 28 800 recorded 30-s epochs were audiovisually analysed for fine and coarse crackles.

RESULTS

Fine crackles and coarse crackles were recognised in every patient with pneumonia, but the number of epochs with and without crackles varied widely among the different patients: fine crackles were detected in 40±22% (mean±sd), coarse crackles in 76±20%. The predominant localisation of crackles as recorded during overnight monitoring was in accordance with the radiographic infiltrates and the classical auscultation in most patients. The distribution of crackles was fairly equal throughout the night. However, there were time periods without any crackle in the single patients so that the diagnosis of pneumonia might be missed at sporadic auscultation.

CONCLUSION

Nocturnal monitoring can be beneficial to reliably detect fine and coarse crackles in children with pneumonia.

Deep Learning Based Centerline-Aggregated Aortic Hemodynamics: An Efficient Alternative to Numerical Modelling of Hemodynamics

Image-based patient-specific modelling of hemodynamics are gaining increased popularity as a diagnosis and outcome prediction solution for a variety of cardiovascular diseases. While their potential to improve diagnostic capabilities and thereby clinical outcome is widely recognized, these methods require considerable computational resources since they are mostly based on conventional numerical methods such as computational fluid dynamics (CFD). As an alternative to the numerical methods, we propose a machine learning (ML) based approach to calculate patient-specific hemodynamic parameters. Compared to CFD based methods, our approach holds the benefit of being able to calculate a patient-specific hemodynamic outcome instantly with little need for computational power. In this proof-of-concept study, we present a deep artificial neural network (ANN) capable of computing hemodynamics for patients with aortic coarctation in a centerline aggregated (i.e. locally averaged) form. Considering the complex relation between vessels shape and hemodynamics on the one hand and the limited availability of suitable clinical data on the other, a sufficient accuracy of the ANN may however not be achieved with available data only. Another key aspect of this study is therefore the successful augmentation of available clinical data. Using a statistical shape model, additional training data was generated which substantially increased the ANNs accuracy, showcasing the ability of ML based methods to perform in-silico modelling tasks previously requiring resource intensive CFD simulations.

Identification of diagnostic markers and lipid dysregulation in oesophageal squamous cell carcinoma through lipidomic analysis and machine learning

BACKGROUND

Oesophageal cancer (EC) ranks high in both morbidity and mortality. A non-invasive and high-sensitivity diagnostic approach is necessary to improve the prognosis of EC patients.

METHODS

A total of 525 serum samples were subjected to lipidomic analysis. We combined serum lipidomics and machine-learning algorithms to select important metabolite features for the detection of oesophageal squamous cell carcinoma (ESCC), the major subtype of EC in developing countries. A diagnostic model using a panel of selected features was developed and evaluated. Integrative analyses of tissue transcriptome and serum lipidome were conducted to reveal the underlying mechanism of lipid dysregulation.

RESULTS

Our optimised diagnostic model with a panel of 12 lipid biomarkers together with age and gender reaches a sensitivity of 90.7%, 91.3% and 90.7% and an area under receiver-operating characteristic curve of 0.958, 0.966 and 0.818 in detecting ESCC for the training cohort, validation cohort and independent validation cohort, respectively. Integrative analysis revealed matched variation trend of genes encoding key enzymes in lipid metabolism.

CONCLUSIONS

We have identified a panel of 12 lipid biomarkers for diagnostic modelling and potential mechanisms of lipid dysregulation in the serum of ESCC patients. This is a reliable, rapid and non-invasive tumour-diagnostic approach for clinical application.

Lung parenchyma parameters measure of rats from pulmonary window computed tomography images based on ResU-Net model for medical respiratory researches

Our paper proposes a method to measure lung parenchyma parameters from pulmonary window computed tomography images based on ResU-Net model including the CT value, the density, the lung volume, and the surface area of the lungs of healthy rats, to help promote the quantitative analysis of lung parenchyma parameters of rats in medical respiratory researches. Through the analysis of the lung parenchyma parameters of the control group and the treatment group, the law of change among the lung parenchyma parameters is given in our paper. After comparing and analyzing the lung parenchyma parameter CT value and the density of the two groups, it is discovered that the lung parenchyma parameter CT value and the density significantly increase in the treatment group which is after continuously inhaling the nebulization of contrast agents. The change of the lung volume with the surface area in both two groups conforms to the law of lung changes during breathing. The relationship between the lung volume and the CT value or the density is analyzed and it is concluded that the lung volume is negatively correlated with the CT value or the density.

Employment of Artificial Intelligence Based on Routine Laboratory Results for the Early Diagnosis of Multiple Myeloma

Objective

In order to enhance the detection rate of multiple myeloma and execute an early and more precise disease management, an artificial intelligence assistant diagnosis system is developed.

Methods

4,187 routine blood and biochemical examination records were collected from Shengjing Hospital affiliated to China Medical University from January 2010 to January 2020, which include 1,741 records of multiple myeloma (MM) and 2,446 records of non-myeloma (infectious diseases, rheumatic immune system diseases, hepatic diseases and renal diseases). The data set was split into training and test subsets with the ratio of 4:1 while connecting hemoglobin, serum creatinine, serum calcium, immunoglobulin (A, G and M), albumin, total protein, and the ratio of albumin to globulin data. An early assistant diagnostic model of MM was established by Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), Deep Neural Networks (DNN), and Random Forest (RF). Out team calculated the precision and recall of the system. The performance of the diagnostic model was evaluated by using the receiver operating characteristic (ROC) curve.

Results

By designing the features properly, the typical machine learning algorithms SVM, DNN, RF and GBDT all performed well. GBDT had the highest precision (92.9%), recall (90.0%) and F1 score (0.915) for the myeloma group. The maximized area under the ROC (AUROC) was calculated, and the results of GBDT (AUC: 0.975; 95% confidence interval (CI): 0.963–0.986) outperformed that of SVM, DNN and RF.

Conclusion

The model established by artificial intelligence derived from routine laboratory results can accurately diagnose MM, which can boost the rate of early diagnosis.

Deep learning diagnostic and risk-stratification pattern detection for COVID-19 in digital lung auscultations: clinical protocol for a case-control and prospective cohort study

Background

Lung auscultation is fundamental to the clinical diagnosis of respiratory disease. However, auscultation is a subjective practice and interpretations vary widely between users. The digitization of auscultation acquisition and interpretation is a particularly promising strategy for diagnosing and monitoring infectious diseases such as Coronavirus-19 disease (COVID-19) where automated analyses could help decentralise care and better inform decision-making in telemedicine. This protocol describes the standardised collection of lung auscultations in COVID-19 triage sites and a deep learning approach to diagnostic and prognostic modelling for future incorporation into an intelligent autonomous stethoscope benchmarked against human expert interpretation.

Methods

A total of 1000 consecutive, patients aged ≥ 16 years and meeting COVID-19 testing criteria will be recruited at screening sites and amongst inpatients of the internal medicine department at the Geneva University Hospitals, starting from October 2020. COVID-19 is diagnosed by RT-PCR on a nasopharyngeal swab and COVID-positive patients are followed up until outcome (i.e., discharge, hospitalisation, intubation and/or death). At inclusion, demographic and clinical data are collected, such as age, sex, medical history, and signs and symptoms of the current episode. Additionally, lung auscultation will be recorded with a digital stethoscope at 6 thoracic sites in each patient. A deep learning algorithm (DeepBreath) using a Convolutional Neural Network (CNN) and Support Vector Machine classifier will be trained on these audio recordings to derive an automated prediction of diagnostic (COVID positive vs negative) and risk stratification categories (mild to severe). The performance of this model will be compared to a human prediction baseline on a random subset of lung sounds, where blinded physicians are asked to classify the audios into the same categories.

Discussion

This approach has broad potential to standardise the evaluation of lung auscultation in COVID-19 at various levels of healthcare, especially in the context of decentralised triage and monitoring.

Trial registration: PB_2016-00500, SwissEthics. Registered on 6 April 2020.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-021-01467-w.

Using machine learning to identify clotted specimens in coagulation testing

OBJECTIVES

A sample with a blood clot may produce an inaccurate outcome in coagulation testing, which may mislead clinicians into making improper clinical decisions. Currently, there is no efficient method to automatically detect clots. This study demonstrates the feasibility of utilizing machine learning (ML) to identify clotted specimens.

METHODS

The results of coagulation testing with 192 clotted samples and 2,889 no-clot-detected (NCD) samples were retrospectively retrieved from a laboratory information system to form the training dataset and testing dataset. Standard and momentum backpropagation neural networks (BPNNs) were trained and validated using the training dataset with a five-fold cross-validation method. The predictive performances of the models were then assessed based on the testing dataset.

RESULTS

Our results demonstrated that there were intrinsic distinctions between the clotted and NCD specimens regarding differences in the testing results and the separation of the groups (clotted and NCD) in the t-SNE analysis. The standard and momentum BPNNs could identify the sample status (clotted and NCD) with areas under the ROC curves of 0.966 (95% CI, 0.958-0.974) and 0.971 (95% CI, 0.9641-0.9784), respectively.

CONCLUSIONS

Here, we have described the application of ML algorithms in identifying the sample status based on the results of coagulation testing. This approach provides a proof-of-concept application of ML algorithms to evaluate the sample quality, and it has the potential to facilitate clinical laboratory automation.

A Deep Learning Model for Classification of Endoscopic Gastroesophageal Reflux Disease

Gastroesophageal reflux disease (GERD) is a common disease with high prevalence, and its endoscopic severity can be evaluated using the Los Angeles classification (LA grade). This paper proposes a deep learning model (i.e., GERD-VGGNet) that employs convolutional neural networks for automatic classification and interpretation of routine GERD LA grade. The proposed model employs a data augmentation technique, a two-stage no-freezing fine-tuning policy, and an early stopping criterion. As a result, the proposed model exhibits high generalizability. A dataset of images from 464 patients was used for model training and validation. An additional 32 patients served as a test set to evaluate the accuracy of both the model and our trainees. Experimental results demonstrate that the best model for the development set exhibited an overall accuracy of 99.2% (grade A-B), 100% (grade C-D), and 100% (normal group) using narrow-band image (NBI) endoscopy. On the test set, the proposed model resulted in an accuracy of 87.9%, which was significantly higher than the results of the trainees (75.0% and 65.6%). The proposed GERD-VGGNet model can assist automatic classification of GERD in conventional and NBI environments and thereby increase the accuracy of interpretation of the results by inexperienced endoscopists.

Artificial neural network and logistic regression modelling to characterize COVID-19 infected patients in local areas of Iran

Background

COVID-19 is an infectious disease that started spreading globally at the end of 2019. Due to differences in patient characteristics and symptoms in different regions, in this research, a comparative study was performed on COVID-19 patients in 6 provinces of Iran. Also, multilayer perceptron (MLP) neural network and Logistic Regression (LR) models were applied for the diagnosis of COVID-19.

Methods

A total of 1043 patients with suspected COVID-19 infection in Iran participated in this study. 29 characteristics, symptoms and underlying disease were obtained from hospitalized patients. Afterwards, we compared the obtained data between confirmed cases. Furthermore, the data was applied for building the ANN and LR models to diagnosis the infected patients by COVID-19.

Results

In 750 confirmed patients, Common symptoms were: fever (%) >37.5 °C, cough, shortness of breath, fatigue, chills and headache. The most common underlying diseases were: hypertension, diabetes, chronic obstructive pulmonary disease and coronary heart disease. Finally, the accuracy of the ANN model to the diagnosis of COVID-19 infection was higher than the LR model.

Conclusion

The prevalent symptoms and underlying diseases of COVID-19 patients were similar in different provinces, but the incidence of symptoms was significantly different from each other. Also, the study demonstrated that ANN and LR models have a high ability in the diagnosis of COVID-19 infection.

Artificial Intelligence/Machine Learning in Respiratory Medicine and Potential Role in Asthma and COPD Diagnosis

Artificial intelligence (AI) and machine learning, a subset of AI, are increasingly used in medicine. AI excels at performing well-defined tasks, such as image recognition; for example, classifying skin biopsy lesions, determining diabetic retinopathy severity, and detecting brain tumors. This article provides an overview of the use of AI in medicine and particularly in respiratory medicine, where it is used to evaluate lung cancer images, diagnose fibrotic lung disease, and more recently is being developed to aid the interpretation of pulmonary function tests and the diagnosis of a range of obstructive and restrictive lung diseases. The development and validation of AI algorithms requires large volumes of well-structured data, and the algorithms must work with variable levels of data quality. It is important that clinicians understand how AI can function in the context of heterogeneous conditions such as asthma and chronic obstructive pulmonary disease where diagnostic criteria overlap, how AI use fits into everyday clinical practice, and how issues of patient safety should be addressed. AI has a clear role in providing support for doctors in the clinical workplace, but its relatively recent introduction means that confidence in its use still has to be fully established. Overall, AI is expected to play a key role in aiding clinicians in the diagnosis and management of respiratory diseases in the future, and it will be exciting to see the benefits that arise for patients and doctors from its use in everyday clinical practice.

Machine Learning and Deep Neural Network Applications in the Thorax: Pulmonary Embolism, Chronic Thromboembolic Pulmonary Hypertension, Aorta, and Chronic Obstructive Pulmonary Disease

The radiologic community is rapidly integrating a revolution that has not fully entered daily practice. It necessitates a close collaboration between computer scientists and radiologists to move from concepts to practical applications. This article reviews the current littérature on machine learning and deep neural network applications in the field of pulmonary embolism, chronic thromboembolic pulmonary hypertension, aorta, and chronic obstructive pulmonary disease.

Artificial intelligence in pulmonary medicine: computer vision, predictive model and COVID-19

Artificial intelligence (AI) is transforming healthcare delivery. The digital revolution in medicine and healthcare information is prompting a staggering growth of data intertwined with elements from many digital sources such as genomics, medical imaging and electronic health records. Such massive growth has sparked the development of an increasing number of AI-based applications that can be deployed in clinical practice. Pulmonary specialists who are familiar with the principles of AI and its applications will be empowered and prepared to seize future practice and research opportunities. The goal of this review is to provide pulmonary specialists and other readers with information pertinent to the use of AI in pulmonary medicine. First, we describe the concept of AI and some of the requisites of machine learning and deep learning. Next, we review some of the literature relevant to the use of computer vision in medical imaging, predictive modelling with machine learning, and the use of AI for battling the novel severe acute respiratory syndrome-coronavirus-2 pandemic. We close our review with a discussion of limitations and challenges pertaining to the further incorporation of AI into clinical pulmonary practice.

Artificial intelligence (AI) is changing the landscape in medicine. AI-based applications will empower pulmonary specialists to seize modern practice and research opportunities. Data-driven precision medicine is already here.https://bit.ly/324tl2m

An Innovative Artificial Intelligence-Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study

Background

Gestational diabetes mellitus (GDM) can cause adverse consequences to both mothers and their newborns. However, pregnant women living in low- and middle-income areas or countries often fail to receive early clinical interventions at local medical facilities due to restricted availability of GDM diagnosis. The outstanding performance of artificial intelligence (AI) in disease diagnosis in previous studies demonstrates its promising applications in GDM diagnosis.

Objective

This study aims to investigate the implementation of a well-performing AI algorithm in GDM diagnosis in a setting, which requires fewer medical equipment and staff and to establish an app based on the AI algorithm. This study also explores possible progress if our app is widely used.

Methods

An AI model that included 9 algorithms was trained on 12,304 pregnant outpatients with their consent who received a test for GDM in the obstetrics and gynecology department of the First Affiliated Hospital of Jinan University, a local hospital in South China, between November 2010 and October 2017. GDM was diagnosed according to American Diabetes Association (ADA) 2011 diagnostic criteria. Age and fasting blood glucose were chosen as critical parameters. For validation, we performed k-fold cross-validation (k=5) for the internal dataset and an external validation dataset that included 1655 cases from the Prince of Wales Hospital, the affiliated teaching hospital of the Chinese University of Hong Kong, a non-local hospital. Accuracy, sensitivity, and other criteria were calculated for each algorithm.

Results

The areas under the receiver operating characteristic curve (AUROC) of external validation dataset for support vector machine (SVM), random forest, AdaBoost, k-nearest neighbors (kNN), naive Bayes (NB), decision tree, logistic regression (LR), eXtreme gradient boosting (XGBoost), and gradient boosting decision tree (GBDT) were 0.780, 0.657, 0.736, 0.669, 0.774, 0.614, 0.769, 0.742, and 0.757, respectively. SVM also retained high performance in other criteria. The specificity for SVM retained 100% in the external validation set with an accuracy of 88.7%.

Conclusions

Our prospective and multicenter study is the first clinical study that supports the GDM diagnosis for pregnant women in resource-limited areas, using only fasting blood glucose value, patients’ age, and a smartphone connected to the internet. Our study proved that SVM can achieve accurate diagnosis with less operation cost and higher efficacy. Our study (referred to as GDM-AI study, ie, the study of AI-based diagnosis of GDM) also shows our app has a promising future in improving the quality of maternal health for pregnant women, precision medicine, and long-distance medical care. We recommend future work should expand the dataset scope and replicate the process to validate the performance of the AI algorithms.

The best person (or machine) for the job: Rethinking task shifting in healthcare

Globally, health systems are faced with the difficult challenge of how to get the best results with the often limited number of health workers available to them. Exacerbating this challenge is the task of meeting ever-changing needs of service users and managing unprecedented technological advances. The process of matching skills to changing needs and opportunities is termed task shifting. It involves questioning health service goals, what health workers do, asking if it can be done in a better way, and implementing change. Task shifting in healthcare is often conceptualised as a process of transferring responsibility for 'simple' tasks from high-skilled but scarce health workers to those with less expertise and lower pay, and predominantly viewed as a means to reduce costs and promote efficiency. Here we present a position paper based on the work and expertise of the European Commission Expert Panel on Effective ways of Investing in Health. It contends that this is over simplistic, and aims to provide a new task shifting framework, informed by relevant evidence, and a series of recommendations. While far from comprehensive, there is a growing body of evidence that certain tasks traditionally undertaken by one type of health worker can be undertaken by others (or machines), in some cases to a higher standard, thus challenging the persistence of rigid professional boundaries. Task shifting has the potential to contribute to health systems strengthening when accompanied by adequate planning, resources, education, training and transparency.

Differential diagnosis of asthma and restrictive respiratory diseases by combining forced oscillation measurements, machine learning and neuro-fuzzy classifiers

To design machine learning classifiers to facilitate the clinical use and increase the accuracy of the forced oscillation technique (FOT) in the differential diagnosis of patients with asthma and restrictive respiratory diseases. FOT and spirometric exams were performed in 97 individuals, including controls (n = 20), asthmatic patients (n = 38), and restrictive (n = 39) patients. The first experiment of this study showed that the best FOT parameter was the resonance frequency, providing moderate accuracy (AUC = 0.87). In the second experiment, a neuro-fuzzy classifier and different supervised machine learning techniques were investigated, including k-nearest neighbors, random forests, AdaBoost with decision trees, and support vector machines with a radial basis kernel. All classifiers achieved high accuracy (AUC ≥ 0.9) in the differentiation between patient groups. In the third and fourth experiments, the use of different feature selection techniques allowed us to achieve high accuracy with only three FOT parameters. In addition, the neuro-fuzzy classifier also provided rules to explain the classification. Neuro-fuzzy and machine learning classifiers can aid in the differential diagnosis of patients with asthma and restrictive respiratory diseases. They can assist clinicians as a support system providing accurate diagnostic options.

COPD phenotypes and machine learning cluster analysis: A systematic review and future research agenda

Chronic Obstructive Pulmonary Disease (COPD) is a highly heterogeneous condition projected to become the third leading cause of death worldwide by 2030. To better characterize this condition, clinicians have classified patients sharing certain symptomatic characteristics, such as symptom intensity and history of exacerbations, into distinct phenotypes. In recent years, the growing use of machine learning algorithms, and cluster analysis in particular, has promised to advance this classification through the integration of additional patient characteristics, including comorbidities, biomarkers, and genomic information. This combination would allow researchers to more reliably identify new COPD phenotypes, as well as better characterize existing ones, with the aim of improving diagnosis and developing novel treatments. Here, we systematically review the last decade of research progress, which uses cluster analysis to identify COPD phenotypes. Collectively, we provide a systematized account of the extant evidence, describe the strengths and weaknesses of the main methods used, identify gaps in the literature, and suggest recommendations for future research.

DCT-MIL: Deep CNN transferred multiple instance learning for COPD identification using CT images

While many pre-defined computed tomographic (CT) measures have been utilized to characterize chronic obstructive pulmonary disease (COPD), it is still challenging to represent pathological alternations of multiple dimensions and highly spatial heterogeneity. Deep CNN transferred multiple instance learning (DCT-MIL) is proposed to identify COPD via CT images. After the lung is divided into eight sections along the axial direction, one random axial CT image is taken out from each section as one instance. With one instance as the input, the activations of neural layers of AlexNet trained by natural images are extracted as features. After dimension reduction through principle component analysis, features of all instances are input into three MIL methods: Citation k-Nearest-Neighbor (Citation-KNN), multiple instance support vector machine, and expectation-maximization diverse density. Moreover, the performance dependence of the resulted models on the depth of the neural layer where activations are extracted and the number of features is investigated. The proposed DCT-MIL achieves an exceptional performance with an accuracy of 99.29% and area under curve of 0.9826 while using 100 principle components of features extracted from the fourth convolutional layer and Citation-KNN. It outperforms not only DCT-MIL models using other settings and the pre-trained AlexNet with fine-tuning by montages of eight lung CT images, but also other state-of-art methods. Deep CNN transferred multiple instance learning is suited for identification of COPD using CT images. It can help finding subgroups with high risk of COPD from large populations through CT scans ordered doing lung cancer screening.

Application of Artificial Intelligence in Diabetes Education and Management: Present Status and Promising Prospect

Despite the rapid development of science and technology in healthcare, diabetes remains an incurable lifelong illness. Diabetes education aiming to improve the self-management skills is an essential way to help patients enhance their metabolic control and quality of life. Artificial intelligence (AI) technologies have made significant progress in transforming available genetic data and clinical information into valuable knowledge. The application of AI tech in disease education would be extremely beneficial considering their advantages in promoting individualization and full-course education intervention according to the unique pictures of different individuals. This paper reviews and discusses the most recent applications of AI techniques to various aspects of diabetes education. With the information and evidence collected, this review attempts to provide insight and guidance for the development of prospective, data-driven decision support platforms for diabetes management, with a focus on individualized patient management and lifelong educational interventions.

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.

Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis

This paper reviews applications of machine learning (ML) predictive models in the diagnosis of chronic diseases. Chronic diseases (CDs) are responsible for a major portion of global health costs. Patients who suffer from these diseases need lifelong treatment. Nowadays, predictive models are frequently applied in the diagnosis and forecasting of these diseases. In this study, we reviewed the state-of-the-art approaches that encompass ML models in the primary diagnosis of CD. This analysis covers 453 papers published between 2015 and 2019, and our document search was conducted from PubMed (Medline), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) libraries. Ultimately, 22 studies were selected to present all modeling methods in a precise way that explains CD diagnosis and usage models of individual pathologies with associated strengths and limitations. Our outcomes suggest that there are no standard methods to determine the best approach in real-time clinical practice since each method has its advantages and disadvantages. Among the methods considered, support vector machines (SVM), logistic regression (LR), clustering were the most commonly used. These models are highly applicable in classification, and diagnosis of CD and are expected to become more important in medical practice in the near future.

Convolutional neural network for the diagnosis of early gastric cancer based on magnifying narrow band imaging

BACKGROUND

Magnifying endoscopy with narrow band imaging (M-NBI) has been applied to examine early gastric cancer by observing microvascular architecture and microsurface structure of gastric mucosal lesions. However, the diagnostic efficacy of non-experts in differentiating early gastric cancer from non-cancerous lesions by M-NBI remained far from satisfactory. In this study, we developed a new system based on convolutional neural network (CNN) to analyze gastric mucosal lesions observed by M-NBI.

METHODS

A total of 386 images of non-cancerous lesions and 1702 images of early gastric cancer were collected to train and establish a CNN model (Inception-v3). Then a total of 341 endoscopic images (171 non-cancerous lesions and 170 early gastric cancer) were selected to evaluate the diagnostic capabilities of CNN and endoscopists. Primary outcome measures included diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value.

RESULTS

The sensitivity, specificity, and accuracy of CNN system in the diagnosis of early gastric cancer were 91.18%, 90.64%, and 90.91%, respectively. No significant difference was spotted in the specificity and accuracy of diagnosis between CNN and experts. However, the diagnostic sensitivity of CNN was significantly higher than that of the experts. Furthermore, the diagnostic sensitivity, specificity and accuracy of CNN were significantly higher than those of the non-experts.

CONCLUSIONS

Our CNN system showed high accuracy, sensitivity and specificity in the diagnosis of early gastric cancer. It is anticipated that more progress will be made in optimization of the CNN diagnostic system and further development of artificial intelligence in the medical field.

CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence

The clinical team attending to a patient upon a diagnosis is faced with two main questions: what treatment, and at what dose? Clinical trials' results provide the basis for guidance and support for official protocols that clinicians use to base their decisions upon. However, individuals rarely demonstrate the reported response from relevant clinical trials, often the average from a group representing a population or subpopulation. The decision complexity increases with combination treatments where drugs administered together can interact with each other, which is often the case. Additionally, the individual's response to the treatment varies over time with the changes in his or her condition, whether via the indication or physiology. In practice, the drug and the dose selection depend greatly on the medical protocol of the healthcare provider and the medical team's experience. As such, the results are inherently varied and often suboptimal. Big data approaches have emerged as an excellent decision-making support tool, but their application is limited by multiple challenges, the main one being the availability of sufficiently big datasets with good quality, representative information. An alternative approach-phenotypic personalized medicine (PPM)-finds an appropriate drug combination (quadratic phenotypic optimization platform [QPOP]) and an appropriate dosing strategy over time (CURATE.AI) based on small data collected exclusively from the treated individual. PPM-based approaches have demonstrated superior results over the current standard of care. The side effects are limited while the desired output is maximized, which directly translates into improving the length and quality of individuals' lives.

The second information revolution: digitalization brings opportunities and concerns for public health

The spread of the written word, facilitated by the introduction of the printing press, was an information revolution with profound implications for European society. Now, a second information revolution is underway, a digital transformation that is shaping the way Europeans live and interact with each other and the world around them. We are confronted with an unprecedented expansion in ways to share and access information and experiences, to express ourselves and communicate. Yet while these changes have undoubtedly provided many benefits for health, from information sharing to improved surveillance and diagnostics, they also open up many potential threats. These come in many forms. Here we review some the pressing issues of concern; discrimination; breaches of privacy; iatrogenesis; disinformation and misinformation or 'fake news' and cyber-attacks. These have the potential to impact negatively on the health and wellbeing of individuals as well as entire communities and nations. We call for a concerted European response to maximize the benefits of the digital revolution while minimizing the harms, arguably one of the greatest challenges facing the public health community today.

Spirometric indices of early airflow impairment in individuals at risk of developing COPD: Spirometry beyond FEV1/FVC

Spirometry is the current gold standard for diagnosing and monitoring the progression of Chronic Obstructive Pulmonary Disease (COPD). However, many current and former smokers who do not meet established spirometric criteria for the diagnosis of this disease have symptoms and clinical courses similar to those with diagnosed COPD. Large longitudinal observational studies following individuals at risk of developing COPD offer us additional insight into spirometric patterns of disease development and progression. Analysis of forced expiratory maneuver changes over time may allow us to better understand early changes predictive of progressive disease. This review discusses the theoretical ability of spirometry to capture fine pathophysiologic changes in early airway disease, highlights the shortcomings of current diagnostic criteria, and reviews existing evidence for spirometric measures which may be used to better detect early airflow impairment.