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Objective: The primary aim of this study was to assess to what extent 99mTc-HDP Single photon emission computed tomography/computed tomography (SPECT/CT) will lead to change of diagnosis and treatment, in patients with suspected foot and ankle osteoarthritis (OA). Secondary aim was to assess the intraobserver variability.

Methods: Retrospectively 107 patients, with suspected foot and/or ankle OA of which a SPECT/CT was made, were included for analysis. All the clinical and radiological data were randomized and blinded before being scored by one experienced orthopaedic surgeon. Firstly, based on the clinical data and conventional radiographs, a diagnosis and treatment plan was scored. Secondly, the observer accessed the SPECT/CT and could change the diagnosis and treatment plan. Additionally, the intraobserver reliability was determined by data of 18 patients that were added in twofold to the dataset, without awareness of the observer and by calculating the κ values.

Results: The diagnosis changed in 53% (57/107) and treatment plans changed in 26% (28/107) of the patients. Intraobserver reliability for the conventional workup was k = 0.54 (moderate strength of agreement), compared to k = 0.66 (substantial strength of agreement) when SPECT/CT data were added.

Conclusions: This study describes the influence of SPECT/CT on diagnosis and treatment plans in patients with suspected symptomatic OA. Also, it shows SPECT/CT leads to a higher intraobserver variability. We believe SPECT/CT has a promising role in the workup for foot and ankle OA.

Advances in knowledge: In addition to what was found in complex foot and ankle cases, this study shows that in patients with non-complex foot and ankle problems, SPECT/CT has a substantial influence on the diagnosis (and subsequent treatment plan).

Objective: The microscopic analysis of biopsied lung nodules represents the gold-standard for definitive diagnosis of lung cancer. Deep learning has achieved pathologist-level classification of non-small cell lung cancer histopathology images at high resolutions (0.5-2 µm/px), and recent studies have revealed tomography-histology relationships at lower spatial resolutions. Thus, we tested whether patterns for histological classification of lung cancer could be detected at spatial resolutions such as those offered by ultra-high-resolution CT.

Methods: We investigated the performance of a deep convolutional neural network (inception-v3) to classify lung histopathology images at lower spatial resolutions than that of typical pathology. Models were trained on 2167 histopathology slides from The Cancer Genome Atlas to differentiate between lung cancer tissues (adenocarcinoma (LUAD) and squamous-cell carcinoma (LUSC)), and normal dense tissue. Slides were accessed at 2.5 × magnification (4 µm/px) and reduced resolutions of 8, 16, 32, 64, and 128 µm/px were simulated by applying digital low-pass filters.

Results: The classifier achieved area under the curve ≥0.95 for all classes at spatial resolutions of 4-16 µm/px, and area under the curve ≥0.95 for differentiating normal tissue from the two cancer types at 128 µm/px.

Conclusions: Features for tissue classification by deep learning exist at spatial resolutions below what is typically viewed by pathologists.

Advances in knowledge: We demonstrated that a deep convolutional network could differentiate normal and cancerous lung tissue at spatial resolutions as low as 128 µm/px and LUAD, LUSC, and normal tissue as low as 16 µm/px. Our data, and results of tomography-histology studies, indicate that these patterns should also be detectable within tomographic data at these resolutions.

Artificial intelligence (AI) has transitioned from the lab to the bedside, and it is increasingly being used in healthcare. Radiology and Radiography are on the frontline of AI implementation, because of the use of big data for medical imaging and diagnosis for different patient groups. Safe and effective AI implementation requires that responsible and ethical practices are upheld by all key stakeholders, that there is harmonious collaboration between different professional groups, and customised educational provisions for all involved. This paper outlines key principles of ethical and responsible AI, highlights recent educational initiatives for clinical practitioners and discusses the synergies between all medical imaging professionals as they prepare for the digital future in Europe. Responsible and ethical AI is vital to enhance a culture of safety and trust for healthcare professionals and patients alike. Educational and training provisions for medical imaging professionals on AI is central to the understanding of basic AI principles and applications and there are many offerings currently in Europe. Education can facilitate the transparency of AI tools, but more formalised, university-led training is needed to ensure the academic scrutiny, appropriate pedagogy, multidisciplinarity and customisation to the learners' unique needs are being adhered to. As radiographers and radiologists work together and with other professionals to understand and harness the benefits of AI in medical imaging, it becomes clear that they are faced with the same challenges and that they have the same needs. The digital future belongs to multidisciplinary teams that work seamlessly together, learn together, manage risk collectively and collaborate for the benefit of the patients they serve.

Objective: This study aimed to describe the methodologies used to develop and evaluate models that use artificial intelligence (AI) to analyse lung images in order to detect, segment (outline borders of), or classify pulmonary nodules as benign or malignant.

Methods: In October 2019, we systematically searched the literature for original studies published between 2018 and 2019 that described prediction models using AI to evaluate human pulmonary nodules on diagnostic chest images. Two evaluators independently extracted information from studies, such as study aims, sample size, AI type, patient characteristics, and performance. We summarised data descriptively.

Results: The review included 153 studies: 136 (89%) development-only studies, 12 (8%) development and validation, and 5 (3%) validation-only. CT scans were the most common type of image type used (83%), often acquired from public databases (58%). Eight studies (5%) compared model outputs with biopsy results. 41 studies (26.8%) reported patient characteristics. The models were based on different units of analysis, such as patients, images, nodules, or image slices or patches.

Conclusion: The methods used to develop and evaluate prediction models using AI to detect, segment, or classify pulmonary nodules in medical imaging vary, are poorly reported, and therefore difficult to evaluate. Transparent and complete reporting of methods, results and code would fill the gaps in information we observed in the study publications.

Advances in knowledge: We reviewed the methodology of AI models detecting nodules on lung images and found that the models were poorly reported and had no description of patient characteristics, with just a few comparing models' outputs with biopsies results. When lung biopsy is not available, lung-RADS could help standardise the comparisons between the human radiologist and the machine. The field of radiology should not give up principles from the diagnostic accuracy studies, such as the choice for the correct ground truth, just because AI is used. Clear and complete reporting of the reference standard used would help radiologists trust in the performance that AI models claim to have. This review presents clear recommendations about the essential methodological aspects of diagnostic models that should be incorporated in studies using AI to help detect or segmentate lung nodules. The manuscript also reinforces the need for more complete and transparent reporting, which can be helped using the recommended reporting guidelines.

In this review, we summarize state-of-the-art artificial intelligence applications for non-invasive cardiovascular imaging modalities including CT, MRI, echocardiography, and nuclear myocardial perfusion imaging.

Objective: Evaluate whether biomarkers measured by automated artificial intelligence (AI)-based algorithms are suggestive of future fall risk.

Methods: In this retrospective age- and sex-matched case-control study, 9029 total patients underwent initial abdominal CT for a variety of indications over a 20-year interval at one institution. 3535 case patients (mean age at initial CT, 66.5 ± 9.6 years; 63.4% female) who went on to fall (mean interval to fall, 6.5 years) and 5494 controls (mean age at initial CT, 66.7 ± 9.8 years; 63.4% females; mean follow-up interval, 6.6 years) were included. Falls were identified by electronic health record review. Validated and fully automated quantitative CT algorithms for skeletal muscle, adipose tissue, and trabecular bone attenuation at the level of L1 were applied to all scans. Uni- and multivariate assessment included hazard ratios (HRs) and area under the receiver operating characteristic (AUROC) curve.

Results: Fall HRs (with 95% CI) for low muscle Hounsfield unit, high total adipose area, and low bone Hounsfield unit were 1.82 (1.65-2.00), 1.31 (1.19-1.44) and 1.91 (1.74-2.11), respectively, and the 10-year AUROC values for predicting falls were 0.619, 0.556, and 0.639, respectively. Combining all these CT biomarkers further improved the predictive value, including 10-year AUROC of 0.657.

Conclusion: Automated abdominal CT-based opportunistic measures of muscle, fat, and bone offer a novel approach to risk stratification for future falls, potentially by identifying patients with osteosarcopenic obesity.

Advances in knowledge: There are few well-established clinical tools to predict falls. We use novel AI-based body composition algorithms to leverage incidental CT data to help determine a patient's future fall risk.

Novel and developing artificial intelligence (AI) systems can be integrated into healthcare settings in numerous ways. For example, in the case of automated image classification and natural language processing, AI systems are beginning to demonstrate near expert level performance in detecting abnormalities such as seizure activity. This paper, however, focuses on AI integration into clinical trials. During the clinical trial recruitment process, considerable labor and time is spent sifting through electronic health record and interviewing patients. With the advancement of deep learning techniques such as natural language processing, intricate electronic health record data can be efficiently processed. This provides utility to workflows such as recruitment for clinical trials. Studies are starting to show promise in shortening the time to recruitment and reducing workload for those involved in clinical trial design. Additionally, numerous guidelines are being constructed to encourage integration of AI into the healthcare setting with meaningful impact. The goal would be to improve the clinical trial process by reducing bias in patient composition, improving retention of participants, and lowering costs and labor.

Objective: Interstitial lung disease (ILD) is relatively common in patients with lung cancer with an incidence of 7.5%. Historically pre-existing ILD was a contraindication to radical radiotherapy owing to increased radiation pneumonitis rates, worsened fibrosis and poorer survival compared with non-ILD cohorts. Herein, the clinical and radiological toxicity outcomes of a contemporaneous cohort are described.

Methods: Patients with ILD treated with radical radiotherapy for lung cancer at a regional cancer centre were collected prospectively. Radiotherapy planning, tumour characteristics, and pre- and post-treatment functional and radiological parameters were recorded. Cross-sectional images were independently assessed by two Consultant Thoracic Radiologists.

Results: Twenty-seven patients with co-existing ILD received radical radiotherapy from February 2009 to April 2019, with predominance of usual interstitial pneumonia subtype (52%). According to ILD-GAP scores, most patients were Stage I. After radiotherapy, localised (41%) or extensive (41%) progressive interstitial changes were noted for most patients yet dyspnoea scores (n = 15 available) and spirometry (n = 10 available) were stable. One-third of patients with ILD went on to receive long-term oxygen therapy, which was significantly more than the non-ILD cohort. Median survival trended towards being worse compared with non-ILD cases (17.8 vs 24.0 months, p = 0.834).

Conclusion: Radiological progression of ILD and reduced survival were observed post-radiotherapy in this small cohort receiving lung cancer radiotherapy, although a matched functional decline was frequently absent. Although there is an excess of early deaths, long-term disease control is achievable.

Advances in knowledge: For selected patients with ILD, long-term lung cancer control without severely impacting respiratory function may be possible with radical radiotherapy, albeit with a slightly higher risk of death.

The exposure to alcohol in utero has been known to damage the developing foetus. Foetal alcohol spectrum disorders is an umbrella term that highlights a range of adverse effects linked to alcohol exposure in utero. Multiple studies have shown specific brain abnormalities, including a reduction in brain size, specifically in the deep nuclei and cerebellum, and parietal and temporal lobe white matter changes. While studies ascertained that other prenatal risk factors, such as maternal use of illicit drugs or lack of pre-natal care, and post-natal risk factors, such as physical or sexual abuse and low socioeconomic status, may be involved in the pathology of variances in foetal neurological abnormalities, prenatal alcohol exposure remained the strongest factor for effects on brain structure and function. Particularly, the number of days of alcohol consumption per week and drinking during all three trimesters of the pregnancy indicating the strongest relationship with brain abnormalities. Further studies are needed to explain pre-natal risk factors in isolation as well as in combination for neurodevelopmental outcomes. The diverse phenotypic presentations described indicate that the diagnostic criteria of foetal alcohol spectrum disorder must be refined to better represent the range of neurologic anomalies.