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ObjectiveIn MOBILITY (NCT01061736), sarilumab significantly reduced disease activity, improved physical function and inhibited radiographic progression at week 52 versus placebo in patients with rheumatoid arthritis (RA) and an inadequate response to methotrexate. We report 5-year safety, efficacy and radiographic outcomes of sarilumab from NCT01061736 and the open-label extension (EXTEND; NCT01146652), in which patients received sarilumab 200 mg every 2 weeks (q2w) + methotrexate.MethodsPatients (n=1197) with moderately to severely active RA were initially randomised to placebo, sarilumab 150 mg or sarilumab 200 mg subcutaneously q2w plus weekly methotrexate for 52 weeks. Completers were eligible to enrol in the open-label extension and receive sarilumab 200 mg q2w + methotrexate.ResultsOverall, 901 patients entered the open-label extension. The safety profile remained stable over 5-year follow-up and consistent with interleukin-6 receptor blockade. Absolute neutrophil count <1000 cells/mm3 was observed but not associated with increased infection rate. Initial treatment with sarilumab 200 mg + methotrexate was associated with reduced radiographic progression over 5 years versus sarilumab 150 mg + methotrexate or placebo + methotrexate (mean±SE change from baseline in van der Heijde-modified Total Sharp Score: 1.46±0.27, 2.35±0.28 and 3.68±0.27, respectively (p<0.001 for each sarilumab dose versus placebo)). Clinical efficacy was sustained through 5 years according to Disease Activity Score (28-joint count) using C reactive protein, Clinical Disease Activity Index (CDAI) and Health Assessment Questionnaire-Disability Index. The number of patients achieving CDAI ≤2.8 at 5 years was similar among initial randomisation groups (placebo, 76/398 (19%); sarilumab 150 mg, 68/400 (17%); sarilumab 200 mg, 84/399 (21%)).ConclusionClinical efficacy, including inhibition of radiographic progression, reduction in disease activity and improvement in physical function, was sustained with sarilumab + methotrexate over 5 years. Safety appeared stable over the 5-year period.

Medical imaging can assess the tumor and its environment in their entirety, which makes it suitable for monitoring the temporal and spatial characteristics of the tumor. Progress in computational methods, especially in artificial intelligence for medical image process and analysis, has converted these images into quantitative and minable data associated with clinical events in oncology management. This concept was first described as radiomics in 2012. Since then, computer scientists, radiologists, and oncologists have gravitated towards this new tool and exploited advanced methodologies to mine the information behind medical images. On the basis of a great quantity of radiographic images and novel computational technologies, researchers developed and validated radiomic models that may improve the accuracy of diagnoses and therapy response assessments. Here, we review the recent methodological developments in radiomics, including data acquisition, tumor segmentation, feature extraction, and modelling, as well as the rapidly developing deep learning technology. Moreover, we outline the main applications of radiomics in diagnosis, treatment planning and evaluations in the field of oncology with the aim of developing quantitative and personalized medicine. Finally, we discuss the challenges in the field of radiomics and the scope and clinical applicability of these methods.

Although the acute respiratory distress syndrome (ARDS) is well defined by the development of acute hypoxemia, bilateral infiltrates and non-cardiogenic pulmonary edema, ARDS is heterogeneous in terms of clinical risk factors, physiology of lung injury, microbiology, and biology, potentially explaining why pharmacologic therapies have been mostly unsuccessful in treating ARDS. Identifying phenotypes of ARDS and integrating this information into patient selection for clinical trials may increase the chance for efficacy with new treatments. In this review, we focus on classifying ARDS by the associated clinical disorders, physiological data, and radiographic imaging. We consider biologic phenotypes, including plasma protein biomarkers, gene expression, and common causative microbiologic pathogens. We will also discuss the issue of focusing clinical trials on the patient’s phase of lung injury, including prevention, administration of therapy during early acute lung injury, and treatment of established ARDS. A more in depth understanding of the interplay of these variables in ARDS should provide more success in designing and conducting clinical trials and achieving the goal of personalized medicine.

ObjectiveThe objective was to identify barriers and facilitators to the implementation of artificial intelligence (AI) applications in clinical radiology in The Netherlands.Materials and methodsUsing an embedded multiple case study, an exploratory, qualitative research design was followed. Data collection consisted of 24 semi-structured interviews from seven Dutch hospitals. The analysis of barriers and facilitators was guided by the recently published Non-adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework for new medical technologies in healthcare organizations.ResultsAmong the most important facilitating factors for implementation were the following: (i) pressure for cost containment in the Dutch healthcare system, (ii) high expectations of AI’s potential added value, (iii) presence of hospital-wide innovation strategies, and (iv) presence of a “local champion.” Among the most prominent hindering factors were the following: (i) inconsistent technical performance of AI applications, (ii) unstructured implementation processes, (iii) uncertain added value for clinical practice of AI applications, and (iv) large variance in acceptance and trust of direct (the radiologists) and indirect (the referring clinicians) adopters.ConclusionIn order for AI applications to contribute to the improvement of the quality and efficiency of clinical radiology, implementation processes need to be carried out in a structured manner, thereby providing evidence on the clinical added value of AI applications.Key Points• Successful implementation of AI in radiology requires collaboration between radiologists and referring clinicians.• Implementation of AI in radiology is facilitated by the presence of a local champion.• Evidence on the clinical added value of AI in radiology is needed for successful implementation.

This study aims to characterize changes in computed tomography (CT) utilization in the adult emergency department (ED) over a 5-year period. CT scans ordered on adult ED patients from July 2000 to July 2005 were analyzed in five groups: head, cervical spine, chest, abdomen, and miscellaneous. ED patient volume and triage acuity scores were determined. Triage acuity scores are used to determine the severity of a patient's illness or injury and the need for immediate evaluation and treatment. There were 46,553 CT scans performed on 27,625 adult patients in the ED during the study period. During this same period, 194,622 adult patients were evaluated in the ED. From 2000 to 2005, the adult emergency department patient volume increased by 13% while triage acuity remained stable. During this same period, head CT increased by 51%, cervical spine CT by 463%, chest CT by 226%, abdominal CT by 72%, and miscellaneous CT by 132%. Although increases were generally greater for patients over age 40, the increase in those less than 40 years was also substantial. Of the 4,320 individual patients who underwent chest CT, 83 (2%) had chest CT on three or more separate ED visits. Of 10,960 patients undergoing abdominal CT, 406 (4%) had abdominal CT on three or more separate ED visits. ED CT utilization has increased at a rate far exceeding the growth in ED patient volume. This presumably reflects the improved utility of CT in diagnosing serious pathology, its increased availability, and a desire on the part of physicians for diagnostic certainty. Whether this increase in utilization results in improved patient outcomes is at present unclear and deserves additional study.

Artificial intelligence is a rapidly evolving field, with modern technological advances and the growth of electronic health data opening new possibilities in diagnostic radiology. In recent years, the performance of deep learning (DL) algorithms on various medical image tasks have continually improved. DL algorithms have been proposed as a tool to detect various forms of intracranial hemorrhage on non-contrast computed tomography (NCCT) of the head. In subtle, acute cases, the capacity for DL algorithm image interpretation support might improve the diagnostic yield of CT for detection of this time-critical condition, potentially expediting treatment where appropriate and improving patient outcomes. However, there are multiple challenges to DL algorithm implementation, such as the relative scarcity of labeled datasets, the difficulties in developing algorithms capable of volumetric medical image analysis, and the complex practicalities of deployment into clinical practice. This review examines the literature and the approaches taken in the development of DL algorithms for the detection of intracranial hemorrhage on NCCT head studies. Considerations in crafting such algorithms will be discussed, as well as challenges which must be overcome to ensure effective, dependable implementations as automated tools in a clinical setting.

Background Minimally invasive surgery (MIS) represents one of the most innovative surgical treatments of hallux valgus (HV). However, long-term outcomes still remain a matter of discussion within the orthopaedic community. The purpose of this longitudinal prospective study was to evaluate radiographic and functional outcomes in patients with mild-to-severe HV who underwent Reverdin-Isham and Akin percutaneous osteotomy, following exostosectomy and lateral release. Methods Eighty patients with mild-to-severe symptomatic HV were treated by MIS. Clinical evaluation was assessed preoperatively, as well as at 3 and 12 months after surgery and at final follow-up of 48 months, using the American Orthopaedic Foot and Ankle Society (AOFAS) hallux grading system. Patient satisfaction and complications were recorded. Computer-assisted measurement of antero-posterior radiographs was taken preoperatively, as well as at 3 and 12 months after surgery and at 48-month follow-up, analysing the intermetatarsal angle (IMA), the hallux valgus angle (HVA), the distal metatarsal articular angle (DMAA) and the tibial sesamoid position. Also, the bridging bone/callus formation was evaluated at the different radiographic follow-ups, while the articular surface congruency and the metatarsal index were calculated only preoperatively and at the last follow-up. Patient satisfaction was assessed using the visual analogue score (VAS). Statistical analysis was carried out using the paired t test. Statistical significance was set at p  < 0.05. Results The mean AOFAS score was 87.15 points at the final follow-up of 48 months, and the VAS score was 8.35/10. The post-operative radiographic assessments showed a statistically significant improvement compared with preoperative values. The mean corrections of each angular value at the last follow-up were as follows: IMA 3.90°, HVA 12.50°, DMAA 4.72° and a tibial sesamoid position of 1.10. The articular surface was congruent in 77 (96.25%) cases and incongruent only in 3 (3.75%). The complete healing of the osteotomies was achieved in all series at 3-month follow-up. However, the results obtained in the correction of the severe HV deformities were less encouraging. Conclusions Minimally invasive surgery with Reverdin-Isham and Akin percutaneous osteotomy, in combination with previous exostosectomy and subsequent lateral soft-tissue release, is a safe, effective and reliable procedure for correction of mild-to-moderate HV. However, it requires a long learning curve because of the inherent difficulty of the mixed different surgical procedures. Trial registration ClinicalTrials.gov PRS Protocol Registration and Results System: NCT02886221

Objective. The objective of this systematic review was to investigate the quality and outcome of studies into artificial intelligence techniques, analysis, and effect in dentistry. Materials and Methods. Using the MeSH keywords: artificial intelligence (AI), dentistry, AI in dentistry, neural networks and dentistry, machine learning, AI dental imaging, and AI treatment recommendations and dentistry. Two investigators performed an electronic search in 5 databases: PubMed/MEDLINE (National Library of Medicine), Scopus (Elsevier), ScienceDirect databases (Elsevier), Web of Science (Clarivate Analytics), and the Cochrane Collaboration (Wiley). The English language articles reporting on AI in different dental specialties were screened for eligibility. Thirty-two full-text articles were selected and systematically analyzed according to a predefined inclusion criterion. These articles were analyzed as per a specific research question, and the relevant data based on article general characteristics, study and control groups, assessment methods, outcomes, and quality assessment were extracted. Results. The initial search identified 175 articles related to AI in dentistry based on the title and abstracts. The full text of 38 articles was assessed for eligibility to exclude studies not fulfilling the inclusion criteria. Six articles not related to AI in dentistry were excluded. Thirty-two articles were included in the systematic review. It was revealed that AI provides accurate patient management, dental diagnosis, prediction, and decision making. Artificial intelligence appeared as a reliable modality to enhance future implications in the various fields of dentistry, i.e., diagnostic dentistry, patient management, head and neck cancer, restorative dentistry, prosthetic dental sciences, orthodontics, radiology, and periodontics. Conclusion. The included studies describe that AI is a reliable tool to make dental care smooth, better, time-saving, and economical for practitioners. AI benefits them in fulfilling patient demand and expectations. The dentists can use AI to ensure quality treatment, better oral health care outcome, and achieve precision. AI can help to predict failures in clinical scenarios and depict reliable solutions. However, AI is increasing the scope of state-of-the-art models in dentistry but is still under development. Further studies are required to assess the clinical performance of AI techniques in dentistry.

Children with central nervous system (CNS) tumors undergo frequent imaging for diagnosis and follow-up, but few studies have characterized longitudinal imaging patterns. We described medical imaging in children before and after malignant CNS tumor diagnosis. We conducted a retrospective cohort study of children aged 0-20 years diagnosed with CNS tumors between 1996-2016 at six U.S. integrated healthcare systems and Ontario, Canada. We collected computed topography (CT), magnetic resonance imaging (MRI), radiography, ultrasound, nuclear medicine examinations from 12 months before through 10 years after CNS diagnosis censoring six months before death or a subsequent cancer diagnosis, disenrollment from the health system, age 21 years, or December 31, 2016. We calculated imaging rates per child per month stratified by modality, country, diagnosis age, calendar year, time since diagnosis, and tumor grade. We observed 1,879 children with median four years follow-up post-diagnosis in the U.S. and seven years in Ontario, Canada. During the diagnosis period (±15 days of diagnosis), children averaged 1.10 CTs (95% confidence interval [CI] 1.09-1.13) and 2.14 MRIs (95%CI 2.12-2.16) in the U.S., and 1.67 CTs (95%CI 1.65-1.68) and 1.86 MRIs (95%CI 1.85-1.88) in Ontario. Within one year after diagnosis, 19% of children had ≥5 CTs and 45% had ≥5 MRIs. By nine years after diagnosis, children averaged one MRI and one radiograph per year with little use of other imaging modalities. MRI and CT are commonly used for CNS tumor diagnosis, whereas MRI is the primary modality used during surveillance of children with CNS tumors.

Background Open reduction with internal fixation (ORIF) and percutaneous surgery (PS) are the most common surgical procedures for the treatment of displaced intra-articular calcaneal fractures. The purpose of this retrospective study was to compare the clinical and radiological results of these techniques and to verify the prognostic value of the radiographic measurement tools proposed in the literature. Methods A consecutive series of 104 calcaneal fractures was included in this analysis. Essex-Lopresti and Sanders classifications were used to evaluate the injuries, and their prognostic correlation was tested. Böhler’s angle was measured pre- and postoperatively and evaluated as radiological outcome. Clinical outcomes were evaluated using the American Orthopaedic Foot and Ankle Society hindfoot scale (AOFAS), Maryland Foot Scale (MFS), 17-Foot Function Index (FFI), Short Form-36 (PCS), and a 10-point visual analogue scale (VAS). Results A total of 87 fractures (5 bilateral), 54 in males and 28 in females, were evaluated with a mean follow-up of 77.0 months. Overall mean age was 51.6 years old. The most frequent cause of trauma was a fall from a height. According to Essex-Lopresti, there were 58 joint depression fractures, 26 tongue, and 3 comminute. According to Sanders: 37 type II, 31 type III, and 19 type IV. Patients were divided into three groups according to surgical treatment: 19 in the ORIF group , 35 in the PS Screw group , and 33 in PS K-wire group . The ORIF group obtained significantly better results (82 AOFAS, 86 MFS, 19.6 FFI, 46.2 PCS, 8 VAS) with respect to the PS K-wire group (74 AOFAS, 76 MSF, 26.4 FFI, 40.8 PCS, 6 VAS). The PS Screw group obtained intermediate results (79 AOFAS, 82 MFS, 22.4 FFI, 41.6 PCS, 7 VAS). The restoration of the Böhler’s angle was achieved most frequently ( p  = 0.02) in the ORIF group, without better clinical results. Conclusion The results were best in the ORIF group, despite its risk of complications, inferior in the PS Screw group, however without statistical significance ( p  > 0.05), and worse in the PS K-wire group. Finally, our data confirmed the prognostic correlation between the two radiographic classifications used and the clinical outcomes.