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One of the leading causes of deaths around the globe is heart disease. Heart is an organ that is responsible for the supply of blood to each part of the body. Coronary artery disease (CAD) and chronic heart failure (CHF) often lead to heart attack. Traditional medical procedures (angiography) for the diagnosis of heart disease have higher cost as well as serious health concerns. Therefore, researchers have developed various automated diagnostic systems based on machine learning (ML) and data mining techniques. ML-based automated diagnostic systems provide an affordable, efficient, and reliable solutions for heart disease detection. Various ML, data mining methods, and data modalities have been utilized in the past. Many previous review papers have presented systematic reviews based on one type of data modality. This study, therefore, targets systematic review of automated diagnosis for heart disease prediction based on different types of modalities, i.e., clinical feature-based data modality, images, and ECG. Moreover, this paper critically evaluates the previous methods and presents the limitations in these methods. Finally, the article provides some future research directions in the domain of automated heart disease detection based on machine learning and multiple of data modalities., Competing Interests: The authors declare that they have no competing interests., (Copyright © 2022 Ashir Javeed et al.)

Deep learning (DL) is a branch of machine learning and artificial intelligence that has been applied to many areas in different domains such as health care and drug design. Cancer prognosis estimates the ultimate fate of a cancer subject and provides survival estimation of the subjects. An accurate and timely diagnostic and prognostic decision will greatly benefit cancer subjects. DL has emerged as a technology of choice due to the availability of high computational resources. The main components in a standard computer-aided design (CAD) system are preprocessing, feature recognition, extraction and selection, categorization, and performance assessment. Reduction of costs associated with sequencing systems offers a myriad of opportunities for building precise models for cancer diagnosis and prognosis prediction. In this survey, we provided a summary of current works where DL has helped to determine the best models for the cancer diagnosis and prognosis prediction tasks. DL is a generic model requiring minimal data manipulations and achieves better results while working with enormous volumes of data. Aims are to scrutinize the influence of DL systems using histopathology images, present a summary of state-of-the-art DL methods, and give directions to future researchers to refine the existing methods., Competing Interests: No conflicts of interest exist between authors for the present study., (Copyright © 2021 Ahsan Bin Tufail et al.)

One in five hospitalized patients suffers acute kidney injury (AKI). Depending on its severity, AKI is associated with an up to 15-fold increased risk of mortality and constitutes a major risk factor for subsequent cardiovascular events and for the development of chronic kidney disease. This concise review summarizes recently published studies, focusing on 1.) automated AKI detection using electronic health records-based AKI alert systems, 2.) renal replacement therapy and its optimal timing and anticoagulation regimen, and 3.) coronavirus disease-2019 (COVID-19) associated AKI., Competing Interests: Die Autorinnen/Autoren geben an, dass kein Interessenkonflikt besteht., (Thieme. All rights reserved.)

Most oncological cases can be detected by imaging techniques, but diagnosis is based on pathological assessment of tissue samples. In recent years, the pathology field has evolved to a digital era where tissue samples are digitised and evaluated on screen. As a result, digital pathology opened up many research opportunities, allowing the development of more advanced image processing techniques, as well as artificial intelligence (AI) methodologies. Nevertheless, despite colorectal cancer (CRC) being the second deadliest cancer type worldwide, with increasing incidence rates, the application of AI for CRC diagnosis, particularly on whole-slide images (WSI), is still a young field. In this review, we analyse some relevant works published on this particular task and highlight the limitations that hinder the application of these works in clinical practice. We also empirically investigate the feasibility of using weakly annotated datasets to support the development of computer-aided diagnosis systems for CRC from WSI. Our study underscores the need for large datasets in this field and the use of an appropriate learning methodology to gain the most benefit from partially annotated datasets. The CRC WSI dataset used in this study, containing 1,133 colorectal biopsy and polypectomy samples, is available upon reasonable request., (© 2021. The Author(s).)

Precision medicine is a new therapeutic concept and method emerging in recent years. The rapid development of precision medicine is driven by the development of omics related technology, biological information and big data science. Precision medicine is provided to implement precise and personalized treatment for diseases and specific patients. Precision medicine is commonly used in the diagnosis, treatment and prevention of various diseases. This review introduces the application of precision medicine in eight systematic diseases of the human body, and systematically presenting the current situation of precision medicine. At the same time, the shortcomings and limitations of precision medicine are pointed out. Finally, we prospect the development of precision medicine., (Copyright © 2020 The Author(s). Published by Elsevier Masson SAS.. All rights reserved.)

Despite substantial advances in the study, treatment, and prevention of cardiovascular disease, numerous challenges relating to optimally screening, diagnosing, and managing patients remain. Simultaneous improvements in computing power, data storage, and data analytics have led to the development of new techniques to address these challenges. One powerful tool to this end is machine learning (ML), which aims to algorithmically identify and represent structure within data. Machine learning's ability to efficiently analyze large and highly complex data sets make it a desirable investigative approach in modern biomedical research. Despite this potential and enormous public and private sector investment, few prospective studies have demonstrated improved clinical outcomes from this technology. This is particularly true in cardiology, despite its emphasis on objective, data-driven results. This threatens to stifle ML's growth and use in mainstream medicine. We outline the current state of ML in cardiology and outline methods through which impactful and sustainable ML research can occur. Following these steps can ensure ML reaches its potential as a transformative technology in medicine.

Biosignals such as electrocardiogram or photoplethysmogram are widely used for determining and monitoring the medical condition of patients. It was recently discovered that more information could be gathered from biosignals by applying artificial intelligence (AI). At present, one of the most impactful advancements in AI is deep learning. Deep learning-based models can extract important features from raw data without feature engineering by humans, provided the amount of data is sufficient. This AI-enabled feature presents opportunities to obtain latent information that may be used as a digital biomarker for detecting or predicting a clinical outcome or event without further invasive evaluation. However, the black box model of deep learning is difficult to understand for clinicians familiar with a conventional method of analysis of biosignals. A basic knowledge of AI and machine learning is required for the clinicians to properly interpret the extracted information and to adopt it in clinical practice. This review covers the basics of AI and machine learning, and the feasibility of their application to real-life situations by clinicians in the near future.

Objective: Although highly successful, the medical R&D model is failing at improving people's health due to a series of flaws and defects inherent to the model itself. A new collective intelligence, incorporating human and artificial intelligence (AI) could overcome these obstacles. Because AI will play a key role in this new collective intelligence, it is necessary that those involved in healthcare have a general knowledge of how these technologies work. With this comprehensive review, we intend to provide it., Materials and Methods: A broad-ranging search has been undertaken on institutional and non-institutional websites in order to identify relevant papers, comments and reports., Results: We firstly describe the flaws and defects of the current R&D biomedical model and how the generation of a new collective intelligence will result in a better and wiser medicine through a truly personalized and holistic approach. We, then, discuss the new forms of data collection and data processing and the different types of artificial learning and their specific algorithms. Finally, we review the current uses and applications of AI in the biomedical field and how these can be expanded, as well as the limitations and challenges of applying these new technologies in the medical field., Conclusions: This colossal common effort based on a new collective intelligence will exponentially improve the quality of medical research, resulting in a radical change for the better in the healthcare model. AI, without replacing us, is here to help us achieve the ambitious goal set by the WHO in the Alma Ata declaration of 1978: "Health for All".

The latest state of the art technological innovations have led to a palpable progression in endoscopic imaging and may facilitate standardisation of practice. One of the most rapidly evolving modalities is artificial intelligence with recent studies providing real-time diagnoses and encouraging results in the first randomised trials to conventional endoscopic imaging. Advances in functional hypoxia imaging offer novel opportunities to be used to detect neoplasia and the assessment of colitis. Three-dimensional volumetric imaging provides spatial information and has shown promise in the increased detection of small polyps. Studies to date of self-propelling colonoscopes demonstrate an increased caecal intubation rate and possibly offer patients a more comfortable procedure. Further development in robotic technology has introduced ex vivo automated locomotor upper gastrointestinal and small bowel capsule devices. Eye-tracking has the potential to revolutionise endoscopic training through the identification of differences in experts and non-expert endoscopist as trainable parameters. In this review, we discuss the latest innovations of all these technologies and provide perspective into the exciting future of diagnostic luminal endoscopy., (© 2019 Japan Gastroenterological Endoscopy Society.)

Recently, artificial intelligence (AI) has been widely applied in the diagnosis and treatment of urinary diseases with the development of data storage, image processing, pattern recognition and machine learning technologies. Based on the massive biomedical big data of imaging and histopathology, many urinary system diseases (such as urinary tumor, urological calculi, urinary infection, voiding dysfunction and erectile dysfunction) will be diagnosed more accurately and will be treated more individualizedly. However, most of the current AI diagnosis and treatment are in the pre-clinical research stage, and there are still some difficulties in the wide application of AI. This review mainly summarizes the recent advances of AI in the diagnosis of prostate cancer, bladder cancer, kidney cancer, urological calculi, frequent micturition and erectile dysfunction, and discusses the future potential and existing problems.

Competing Interests: Die Autorinnen/Autoren geben an, dass kein Interessenkonflikt besteht.

Computer-aided diagnosis (CAD) has been a popular area of research and development in the past few decades. In CAD, machine learning methods and multidisciplinary knowledge and techniques are used to analyze the patient information and the results can be used to assist clinicians in their decision making process. CAD may analyze imaging information alone or in combination with other clinical data. It may provide the analyzed information directly to the clinician or correlate the analyzed results with the likelihood of certain diseases based on statistical modeling of the past cases in the population. CAD systems can be developed to provide decision support for many applications in the patient care processes, such as lesion detection, characterization, cancer staging, treatment planning and response assessment, recurrence and prognosis prediction. The new state-of-the-art machine learning technique, known as deep learning (DL), has revolutionized speech and text recognition as well as computer vision. The potential of major breakthrough by DL in medical image analysis and other CAD applications for patient care has brought about unprecedented excitement of applying CAD, or artificial intelligence (AI), to medicine in general and to radiology in particular. In this paper, we will provide an overview of the recent developments of CAD using DL in breast imaging and discuss some challenges and practical issues that may impact the advancement of artificial intelligence and its integration into clinical workflow.

Intracranial aneurysms with subarachnoid hemorrhage lead to high morbidity and mortality. It is of critical importance to detect aneurysms, identify risk factors of rupture, and predict treatment response of aneurysms to guide clinical interventions. Artificial intelligence has received worldwide attention for its impressive performance in image-based tasks. Artificial intelligence serves as an adjunct to physicians in a series of clinical settings, which substantially improves diagnostic accuracy while reducing physicians' workload. Computer-assisted diagnosis systems of aneurysms based on MRA and CTA using deep learning have been evaluated, and excellent performances have been reported. Artificial intelligence has also been used in automated morphologic calculation, rupture risk stratification, and outcomes prediction with the implementation of machine learning methods, which have exhibited incremental value. This review summarizes current advances of artificial intelligence in the management of aneurysms, including detection and prediction. The challenges and future directions of clinical implementations of artificial intelligence are briefly discussed., (© 2020 by American Journal of Neuroradiology.)

Although computer-aided diagnosis (CAD) is widely used in mammography, conventional CAD programs that use prompts to indicate potential cancers on the mammograms have not led to an improvement in diagnostic accuracy. Because of the advances in machine learning, especially with use of deep (multilayered) convolutional neural networks, artificial intelligence has undergone a transformation that has improved the quality of the predictions of the models. Recently, such deep learning algorithms have been applied to mammography and digital breast tomosynthesis (DBT). In this review, the authors explain how deep learning works in the context of mammography and DBT and define the important technical challenges. Subsequently, they discuss the current status and future perspectives of artificial intelligence-based clinical applications for mammography, DBT, and radiomics. Available algorithms are advanced and approach the performance of radiologists-especially for cancer detection and risk prediction at mammography. However, clinical validation is largely lacking, and it is not clear how the power of deep learning should be used to optimize practice. Further development of deep learning models is necessary for DBT, and this requires collection of larger databases. It is expected that deep learning will eventually have an important role in DBT, including the generation of synthetic images., (© RSNA, 2019.)

The use of artificial intelligence will transform clinical practice over the next decade and the early impact of this will likely be the integration of image analysis and machine learning into routine histopathology. In the UK and around the world, a digital revolution is transforming the reporting practice of diagnostic histopathology and this has sparked a proliferation of image analysis software tools. While this is an exciting development that could discover novel predictive clinical information and potentially address international pathology workforce shortages, there is a clear need for a robust and evidence-based framework in which to develop these new tools in a collaborative manner that meets regulatory approval. With these issues in mind, the NCRI Cellular Molecular Pathology (CM-Path) initiative and the British In Vitro Diagnostics Association (BIVDA) have set out a roadmap to help academia, industry, and clinicians develop new software tools to the point of approved clinical use. © 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd., (© 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.)

Echocardiography has a central role in the diagnosis and management of cardiovascular disease. Precise and reliable echocardiographic assessment is required for clinical decision-making. Even if the development of new technologies (3-dimentional echocardiography, speckle-tracking, semi-automated analysis, etc.), the final decision on analysis is strongly dependent on operator experience. Diagnostic errors are a major unresolved problem. Moreover, not only can cardiologists differ from one another in image interpretation, but also the same observer may come to different findings when a reading is repeated. Daily high workloads in clinical practice may lead to this error, and all cardiologists require precise perception in this field. Artificial intelligence (AI) has the potential to improve analysis and interpretation of medical images to a new stage compared with previous algorithms. From our comprehensive review, we believe AI has the potential to improve accuracy of diagnosis, clinical management, and patient care. Although there are several concerns about the required large dataset and "black box" algorithm, AI can provide satisfactory results in this field. In the future, it will be necessary for cardiologists to adapt their daily practice to incorporate AI in this new stage of echocardiography.

An ultrasound (US) examination is a common noninvasive technique widely applied for diagnosis of a variety of diseases. Based on the rapid development of US equipment, many US images have been accumulated and are now available and ready for the preparation of a database for the development of computer-aided US diagnosis with deep learning technology. On the contrary, because of the unique characteristics of the US image, there could be some issues that need to be resolved for the establishment of computer-aided diagnosis (CAD) system in this field. For example, compared to the other modalities, the quality of a US image is, currently, highly operator dependent; the conditions of examination should also directly affect the quality of US images. So far, these factors have hampered the application of deep learning-based technology in the field of US diagnosis. However, the development of CAD and US technologies will contribute to an increase in diagnostic quality, facilitate the development of remote medicine, and reduce the costs in the national health care through the early diagnosis of diseases. From this point of view, it may have a large enough potential to induce a paradigm shift in the field of US imaging and diagnosis of liver diseases.

Due to recent advances in artificial intelligence, there is renewed interest in automating interpretation of imaging tests. Chest radiographs are particularly interesting due to many factors: relatively inexpensive equipment, importance to public health, commonly performed throughout the world, and deceptively complex taking years to master. This article presents a brief introduction to artificial intelligence, reviews the progress to date in chest radiograph interpretation, and provides a snapshot of the available datasets and algorithms available to chest radiograph researchers. Finally, the limitations of artificial intelligence with respect to interpretation of imaging studies are discussed., (Copyright © 2019 The Royal College of Radiologists. All rights reserved.)

In the current approach to male fertility testing, basic semen analysis has limitations as a predictor of fertility status, and the technology is fraught with variability. Nonetheless, it remains the cornerstone of the evaluation of the male infertility, and we recommend adherence to most recent World Health Organization guidelines. Although the current sperm function tests (bioassays) have important drawbacks, they are still valuable as research tools. Sperm quality assays with analysis of sperm DNA fragmentation need further investigation before they can be recommended for routine clinical use. The answer to the many current challenging questions relies on identifying spermatogenesis pathologies and the resulting sperm dysfunctions at the cellular and molecular levels. New discoveries may bring answers or new avenues to explore., (Copyright © 2019 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.)

Background: Autism spectrum disorder (ASD) is a heterogeneous group of neurodevelopmental disorders which core symptoms are impairments in socio-communication and repetitive symptoms and stereotypies. Although not cardinal symptoms per se, motor impairments are fundamental aspects of ASD. These impairments are associated with postural and motor control disabilities that we investigated using computational modeling and developmental robotics through human-machine interaction paradigms., Method: First, in a set of studies involving a human-robot posture imitation, we explored the impact of 3 different groups of partners (including a group of children with ASD) on robot learning by imitation. Second, using an ecological task, i.e. a real-time motor imitation with a tightrope walker (TW) avatar, we investigated interpersonal synchronization, motor coordination and motor control during the task in children with ASD (n=29), TD children (n=39) and children with developmental coordination disorder (n=17, DCD)., Results: From the human-robot experiments, we evidenced that motor signature at both groups' and individuals' levels had a key influence on imitation learning, posture recognition and identity recognition. From the more dynamic motor imitation paradigm with a TW avatar, we found that interpersonal synchronization, motor coordination and motor control were more impaired in children with ASD compared to both TD children and children with DCD. Taken together these results confirm the motor peculiarities of children with ASD despite imitation tasks were adequately performed., Discussion: Studies from human-machine interaction support the idea of a behavioral signature in children with ASD. However, several issues need to be addressed. Is this behavioral signature motoric in essence? Is it possible to ascertain that these peculiarities occur during all motor tasks (e.g. posture, voluntary movement)? Could this motor signature be considered as specific to autism, notably in comparison to DCD that also display poor motor coordination skills? We suggest that more work comparing the two conditions should be implemented, including analysis of kinematics and movement smoothness with sufficient measurement quality to allow spectral analysis., (Copyright © 2018 L'Encéphale, Paris. Published by Elsevier Masson SAS. All rights reserved.)

'Theory of Mind' (ToM) is the ability to attribute mental states to others to make sense of their behaviour. ToM research has informed understanding of (a)typical social behaviour, including the symptoms of autism spectrum disorder (ASD). This began with research on ToM in autistic children and there has been a noticeable increase in the study of ToM in autistic adults. However, methodological limitations in adult ToM research may be limiting its explanatory power of ASD symptoms and their management, therefore we discuss recent advances in measuring ToM aimed at addressing these issues. We also examine previously overlooked approaches and propose several new directions that have potential to improve the sensitivity, accuracy, and clinical utility of ToM measurement in autistic adulthood.

In the field of cancer genomics, the broad availability of genetic information offered by next-generation sequencing technologies and rapid growth in biomedical publication has led to the advent of the big-data era. Integration of artificial intelligence (AI) approaches such as machine learning, deep learning, and natural language processing (NLP) to tackle the challenges of scalability and high dimensionality of data and to transform big data into clinically actionable knowledge is expanding and becoming the foundation of precision medicine. In this paper, we review the current status and future directions of AI application in cancer genomics within the context of workflows to integrate genomic analysis for precision cancer care. The existing solutions of AI and their limitations in cancer genetic testing and diagnostics such as variant calling and interpretation are critically analyzed. Publicly available tools or algorithms for key NLP technologies in the literature mining for evidence-based clinical recommendations are reviewed and compared. In addition, the present paper highlights the challenges to AI adoption in digital healthcare with regard to data requirements, algorithmic transparency, reproducibility, and real-world assessment, and discusses the importance of preparing patients and physicians for modern digitized healthcare. We believe that AI will remain the main driver to healthcare transformation toward precision medicine, yet the unprecedented challenges posed should be addressed to ensure safety and beneficial impact to healthcare.

Deepened periodontal pockets exert a significant pathological burden on the host and its immune system, particularly in a patient with generalized moderate to severe periodontitis. This burden is extensive and longitudinal, occurring over decades of disease development. Considerable diagnostic and prognostic successes in this regard have come from efforts to measure the depths of the pockets and their contents, including level of inflammatory mediators, cellular exudates and microbes; however, the current standard of care for measuring these pockets, periodontal probing, is an analog technology in a digital age. Measurements obtained by probing are variable, operator dependent and influenced by site-specific factors. Despite these limitations, manual probing is still the standard of care for periodontal diagnostics globally. However, it is becoming increasingly clear that this technology needs to be updated to be compatible with the digital technologies currently being used to image other orofacial structures, such as maxillary sinuses, alveolar bone, nerve foramina and endodontic canals in 3 dimensions. This review aims to summarize the existing technology, as well as new imaging strategies that could be utilized for accurate evaluation of periodontal pocket dimensions., (© 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Dramatic changes in the use of prostate magnetic resonance imaging (MRI) have occurred in the last decade. The recognition that MRI detects and localizes cancers with reasonable accuracy led to the development of directed biopsies. These image-guided biopsies have a higher sensitivity for clinically significant cancers and a lower sensitivity for indolent disease. Prospective trials provide level 1 evidence supporting the use of prostate MRI. For local staging, while the specificity of prostate MRI is high, its sensitivity is lacking for microscopic extraprostatic extension. Computer-aided diagnosis of prostate MRI promises to bring the diagnostic power of MRI to nonexpert readers and thus further integrate MRI into the diagnostic workup.

Recently, more and more attention has been paid to the utility of artificial intelligence in medicine. Radiology differs from other medical specialties with its high digitalization, so most software developers operationalize this area of medicine. The primary condition for machine learning is met because medical diagnostic images have high reproducibility. Today, the most common anatomic area for computed tomography is the thorax, particularly with the widespread lung cancer screening programs using low-dose computed tomography. In this regard, the amount of information that needs to be processed by a radiologist is snowballing. Thus, automatic image analysis will allow more studies to be interpreted. This review is aimed at highlighting the possibilities of machine learning in the chest computed tomography.

Repeated measurements could be helpful to identify patients with early cognitive decline. We compare the variation of cognitive performance over one year in patients with mild cognitive impairment (MCI) and healthy individuals using the Brain on Track self-applied computerized test (BoT). The study was initiated 30 patients with probable MCI and 377 controls from a population-based cohort, who performed the BoT test from home every three months for one year. The scores were compared using a linear mixed-effects model. All participants increased their scores in the first tests, after 120 days MCI patients started to decline, with a statistically significant higher rate. The area under the curve to detect MCI was 0.94. We identified a significant decline in cognitive performance over one year in patients with MCI using BoT and the test presented a high discriminative ability.

Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: KY receives a research grant from Japan Radiological Society. OA has no competing interest regarding this work.

The new digital health innovations have opened up several opportunities to help the clinicians, patients and other caregivers of rheumatology healthcare system in maximizing efficiencies resulting in better patient outcomes. In the global context, digital health technology has the potential to bridge the distance gap between all the key stakeholders involved in rheumatology health care. In this review, we update on the recent advances in the field of digital health and highlight unique features of these technologies which would help in routine care. Application of technology in any form to enable, facilitate or enhance the quality of care is the foundation of digitised care. The components could be smartphone apps, sensors, video, social media platforms or messenger platforms, wearables or a combination of these enabling healthcare delivery and overcoming the constraints of distance, location and time. Digital therapeutics have started evolving and an important step in this direction is the involvement of FDA in the approval process. Speciality specific apps, personalised patient education as per disease status, remote specialist consultations or virtual health coach to guide on lifestyle modifications are some of the developments which have been facilitated by increased digitization in all walks of life. Assisted care with the help of robots rendering care in the hospitals or an intelligent robot guiding a patient by voice and visual sense at home are already at the threshold of entering the mainstream of patient care. Wearable devices equipped with powerful sensors are coming handy in keeping a watch on patient symptoms all the time and providing useful insights on disease progression, clinical response or complications. In chronic care such as rheumatology the implications, possibilities and benefits seem unprecedented. Real time data analytics and artificial intelligence are helping the clinicians, healthcare systems and policy makers optimise the resources and improve patient outcomes. Digitization of healthcare has gained momentum in the recent years and it is envisaged that it could be a catalyst to change, bridge the quality of care and most important democratise the healthcare access across the globe. However, more data, efficacy and objective results are needed which would be fulfilled by ongoing observational studies, clinical trials, systematic review and meta-analysis to further establish the role of digital health in the realms of patient care.

OBJECTIVEPseudarthrosis can occur following adult spinal deformity (ASD) surgery and can lead to instrumentation failure, recurrent pain, and ultimately revision surgery. In addition, it is one of the most expensive complications of ASD surgery. Risk factors contributing to pseudarthrosis in ASD have been described; however, a preoperative model predicting the development of pseudarthrosis does not exist. The goal of this study was to create a preoperative predictive model for pseudarthrosis based on demographic, radiographic, and surgical factors.METHODSA retrospective review of a prospectively maintained, multicenter ASD database was conducted. Study inclusion criteria consisted of adult patients (age ≥ 18 years) with spinal deformity and surgery for the ASD. From among 82 variables assessed, 21 were used for model building after applying collinearity testing, redundancy, and univariable predictor importance ≥ 0.90. Variables included demographic data along with comorbidities, modifiable surgical variables, baseline coronal and sagittal radiographic parameters, and baseline scores for health-related quality of life measures. Patients groups were determined according to their Lenke radiographic fusion type at the 2-year follow-up: bilateral or unilateral fusion (union) or pseudarthrosis (nonunion). A decision tree was constructed, and internal validation was accomplished via bootstrapped training and testing data sets. Accuracy and the area under the receiver operating characteristic curve (AUC) were calculated to evaluate the model.RESULTSA total of 336 patients were included in the study (nonunion: 105, union: 231). The model was 91.3% accurate with an AUC of 0.94. From 82 initial variables, the top 21 covered a wide range of areas including preoperative alignment, comorbidities, patient demographics, and surgical use of graft material.CONCLUSIONSA model for predicting the development of pseudarthrosis at the 2-year follow-up was successfully created. This model is the first of its kind for complex predictive analytics in the development of pseudarthrosis for patients with ASD undergoing surgical correction and can aid in clinical decision-making for potential preventative strategies.

Cancer is one of most fatal forms of disease with rapid, abnormal and uncontrolled division of cells which spreads into different organs in the body. The primary aim of this review is to showcase the current and emerging diagnostic techniques that are used in lung cancer detection. Lung cancer is a leading cause of death among smokers and it has been emerging in non-smokers due to passive smoke inhalation by non-smokers. The mortality rate of patients with lung cancer is very high due to the change in lifestyle and environmental factors. It is often misdiagnosed as tuberculosis in India as tuberculosis is prevalent in India. On the contrary tuberculosis is not prevalent in the western countries Like U.S.A., U.K., Canada, etc. The major setback in lung cancer is that the symptoms of lung cancer occur at very later stages when the tumor has spread profusely. Hence, highly advanced techniques are employed for detection, accurate staging and treatment of lung cancer. The review focuses on the various novel and emerging diagnostic tools like biomarkers and biosensors, radiogenomics and artificial intelligence. This review also gives an insight of the various conventional techniques like CT-imaging, sputum cytology, biopsy and bronchoscopy which have been modified over the years for better sensitivity and accuracy. It also encompasses the regulatory provisions like IDE, CLIA-certification, etc. for manufacturing and sale of diagnostics in India, U.S.A., Japan and Australia., (Copyright © 2018. Published by Elsevier Masson SAS.)

One of the most widely used measures of psychosis-related symptoms and characteristics is the 74-item Schizotypal Personality Questionnaire (SPQ). Using multidimensional Item Response Theory (bifactor 2-parameter model), we calibrated SPQ items in a sample of 375 youths aged 9-24 years and constructed a fully functional computerized adaptive form of the SPQ on an open-source platform for public use. To assess validity, we used the above parameters to simulate CAT sessions in a separate validation sample (N = 100) using three test-length-based stopping rules: 8 items, 16 items, and 32 items. Those scores were then compared to full-form and SPQ-Brief scores on their abilities to predict psychosis or clinical risk status. Areas under the receiver operating characteristic curves indicated mediocre predictive ability, but did not differ among any of the forms, even when only eight adaptive items were administered. The Youden index for the 16-item adaptive version was higher than that for the 22-item SPQ-Brief. Classification accuracy for the full SPQ was 73% compared to 66% for the both the SPQ-Brief and adaptive versions (average of three stopping rules). The SPQ-CAT shows promise as a much shorter but valid assessment of schizotypy which can save time with minimal loss of information., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Objectives: The search for objective clinical signs is a constant practitioners' and researchers' concern in psychiatry. New technologies (embedded sensors, artificial intelligence) give an easier access to untapped information such as passive data (i.e. that do not require patient intervention). The concept of "digital phenotype" is emerging in psychiatry: a psychomotor alteration translated by accelerometer's modifications contrasting with the usual functioning of the subject, or the graphorrhea of patients presenting a manic episode which is replaced by an increase of SMS sent. Our main objective is to highlight the digital phenotype of mood disorders by means of a selective review of the literature., Method: We conducted a selective review of the literature by querying the PubMed database until February 2017 with the terms [Computer] [Computerized] [Machine] [Automatic] [Automated] [Heart rate variability] [HRV] [actigraphy] [actimetry] [digital] [motion] [temperature] [Mood] [Bipolar] [Depression] [Depressive]. Eight hundred and forty-nine articles were submitted for evaluation, 37 articles were included., Results: For unipolar disorders, smartphones can diagnose depression with excellent accuracy by combining GPS and call log data. Actigraphic measurements showing daytime alteration in basal function while ECG sensors assessing variation in heart rate variability (HRV) and body temperature appear to be useful tools to diagnose a depressive episode. For bipolar disorders, systems which combine several sensors are described: MONARCA, PRIORI, SIMBA and PSYCHE. All these systems combine passive and active data on smartphones. From a synthesis of these data, a digital phenotype of the disorders is proposed based on the accelerometer and the GPS, the ECG, the body temperature, the use of the smartphone and the voice. This digital phenotype thus brings into question certain clinical paradigms in which psychiatrists evolve., Conclusion: All these systems can be used to computerize the clinical characteristics of the various mental states studied, sometimes with greater precision than a clinician could do. Most authors recommend the use of passive data rather than active data in the context of bipolar disorders because automatically generated data reduce biases and limit the feeling of intrusion that self-questionnaires may cause. The impact of these technologies questions the psychiatrist's professional culture, defined as a specific language and a set of common values. We address issues related to these changes. Impact on psychiatrists could be important because their unity seems to be questioned due to technologies that profoundly modify the collect and process of clinical data., (Copyright © 2017. Published by Elsevier Masson SAS.)

This document clarifies the strategic context of digital pathology adoption, defines the different use cases a healthcare provider may wish to consider as part of a digital adoption and summarises existing reasons for digital adoption and its potential benefits. The reader is provided with references to the relevant literature, and illustrative case studies. The authors hope this report will be of interest to healthcare providers, pathology managers, departmental heads, pathologists and biomedical scientists that are considering digital pathology, deployments or preparing business cases for digital pathology adoption in clinical settings. The information contained in this document can be shared and used in any documentation the reader wishes to present for their own institutional case for adoption report or business case., Competing Interests: Competing interests: None declared., (© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.)

Objectives: Clinical practice guidelines (CPG) provide evidence-based recommendations for patient care but may not be optimally applied in clinical settings. As a pilot study, we evaluated the impact of a computerized, point-of-care decision support system (CDSS) on guideline knowledge and adherence in our diabetes clinic., Methods: iSCREEN, a CDSS, integrated with a province-wide electronic health record, was designed based on the Canadian Diabetes Association 2013 Clinical Practice Guidelines for the Prevention and Management of Diabetes in Canada. Evaluation data were gathered by retrospective chart review and clinician questionnaire prior to and after implementation of iSCREEN. Records of patients with type 1 diabetes, 14 to 18 years of age, were assessed for appropriate screening for complications and comorbidities., Results: To assess guideline adherence, 50 charts were reviewed at 2 time periods (25 before and 25 after launch of iSCREEN). Results revealed improved frequency of appropriate screening for diabetic nephropathy (p=0.03) and retinopathy (p=0.04), accompanied by a decrease in under- and overscreening for these outcomes. To assess guideline knowledge, 58 surveys were collected (31 prior to and 27 after the launch of iSCREEN) from care providers in the field of pediatric diabetes. There was a trend toward improved guideline knowledge in all team members (p=0.06)., Conclusions: Implementation of a de novo CDSS was associated with improved rates of appropriate screening for diabetes-related complications. A trend toward improvement in health professionals' knowledge of the guidelines was also observed. Evaluation of this point-of-care computerized decision support tool suggests that it may facilitate diabetes care by optimizing complication screening and CPG knowledge, with the potential for broader implementation., (Copyright © 2017 Diabetes Canada. Published by Elsevier Inc. All rights reserved.)

Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. We survey the current status of AI applications in healthcare and discuss its future. AI can be applied to various types of healthcare data (structured and unstructured). Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, and the modern deep learning, as well as natural language processing for unstructured data. Major disease areas that use AI tools include cancer, neurology and cardiology. We then review in more details the AI applications in stroke, in the three major areas of early detection and diagnosis, treatment, as well as outcome prediction and prognosis evaluation. We conclude with discussion about pioneer AI systems, such as IBM Watson, and hurdles for real-life deployment of AI., Competing Interests: Competing interests: None declared.