Opening the black box of machine learning in radiology: can the proximity of annotated cases be a way?

Machine learning (ML) and deep learning (DL) systems, currently employed in medical image analysis, are data-driven models often considered as black boxes. However, improved transparency is needed to translate automated decision-making to clinical practice. To this aim, we propose a strategy to open the black box by presenting to the radiologist the annotated cases (ACs) proximal to the current case (CC), making decision rationale and uncertainty more explicit. The ACs, used for training, validation, and testing in supervised methods and for validation and testing in the unsupervised ones, could be provided as support of the ML/DL tool. If the CC is localised in a classification space and proximal ACs are selected by proper metrics, the latter ones could be shown in their original form of images, enriched with annotation to radiologists, thus allowing immediate interpretation of the CC classification. Moreover, the density of ACs in the CC neighbourhood, their image saliency maps, classification confidence, demographics, and clinical information would be available to radiologists. Thus, encrypted information could be transmitted to radiologists, who will know model output (what) and salient image regions (where) enriched by ACs, providing classification rationale (why). Summarising, if a classifier is data-driven, let us make its interpretation data-driven too

Artificial intelligence in medical imaging: threat or opportunity? : radiologists again at the forefront of innovation in medicine

One of the most promising areas of health innovation is the application of artificial intelligence (AI), primarily in medical imaging. This article provides basic definitions of terms such as "machine/deep learning" and analyses the integration of AI into radiology. Publications on AI have drastically increased from about 100-150 per year in 2007-2008 to 700-800 per year in 2016-2017. Magnetic resonance imaging and computed tomography collectively account for more than 50% of current articles. Neuroradiology appears in about one-third of the papers, followed by musculoskeletal, cardiovascular, breast, urogenital, lung/thorax, and abdomen, each representing 6-9% of articles. With an irreversible increase in the amount of data and the possibility to use AI to identify findings either detectable or not by the human eye, radiology is now moving from a subjective perceptual skill to a more objective science. Radiologists, who were on the forefront of the digital era in medicine, can guide the introduction of AI into healthcare. Yet, they will not be replaced because radiology includes communication of diagnosis, consideration of patient's values and preferences, medical judgment, quality assurance, education, policy-making, and interventional procedures. The higher efficiency provided by AI will allow radiologists to perform more value-added tasks, becoming more visible to patients and playing a vital role in multidisciplinary clinical teams

Breast arterial calcifications as a biomarker of cardiovascular risk: radiologists' awareness, reporting, and action : a survey among the EUSOBI members

Objectives: To investigate the knowledge of radiologists on breast arterial calcifications (BAC) and attitude about BAC reporting, communication to women, and subsequent action. Methods: An online survey was offered to EUSOBI members, with 17 questions focused on demographics, level of experience, clinical setting, awareness of BAC association with cardiovascular risk, mammographic reporting, modality of BAC assessment, and action habits. Descriptive statistics were used. Results: Among 1084 EUSOBI members, 378 (34.9%) responded to the survey, 361/378 (95.5%) radiologists, 263 females (69.6%), 112 males (29.6%), and 3 (0.8%) who did not specify their gender. Of 378 respondents, 305 (80.7%) declared to be aware of BAC meaning in terms of cardiovascular risk and 234 (61.9%) to routinely include BAC in mammogram reports, when detected. Excluding one inconsistent answer, simple annotation of BAC presence was declared by 151/233 (64.8%), distinction between low versus extensive BAC burden by 59/233 (25.3%), and usage of an ordinal scale by 22/233 (9.5%) and of a cardinal scale by 1/233 (0.4%). Among these 233 radiologists reporting BAC, 106 (45.5%) declared to orally inform the woman and, in case of severe BAC burden, 103 (44.2%) to investigate cardiovascular history, and 92 (39.5%) to refer the woman to a cardiologist. Conclusion: Among EUSOBI respondents, over 80% declared to be aware of BAC cardiovascular meaning and over 60% to include BAC in the report. Qualitative BAC assessment predominates. About 40% of respondents who report on BAC, in the case of severe BAC burden, investigate cardiovascular history and/or refer the woman to a cardiologist

Image quality of late gadolinium enhancement in cardiac magnetic resonance with different doses of contrast material in patients with chronic myocardial infarction

Background: Contrast-enhanced cardiac magnetic resonance (CMR) is pivotal for evaluating chronic myocardial infarction (CMI). Concerns about safety of gadolinium-based contrast agents favour dose reduction. We assessed image quality of scar tissue in CMRs performed with different doses of gadobutrol in CMI patients. Methods: Informed consent was waived for this Ethics Committee-approved single-centre retrospective study. Consecutive contrast-enhanced CMRs from CMI patients were retrospectively analysed according to the administered gadobutrol dose (group A, 0.10 mmol/kg; group B, 0.15 mmol/kg; group C, 0.20 mmol/kg). We calculated the signal-to-noise ratio for scar tissue (SNRscar) and contrast-to-noise ratio between scar and either remote myocardium (CNRscar-rem) or blood (CNRscar-blood). Results: Of 79 CMRs from 79 patients, 22 belonged to group A, 26 to group B, and 31 to group C. The groups were homogeneous for age, sex, left ventricular morpho-functional parameters, and percentage of scar tissue over whole myocardium (p 65 0.300). SNRscar was lower in group A (46.4; 40.3\u201365.1) than in group B (70.1; 52.2\u2013111.5) (p = 0.013) and group C (72.1; 59.4\u2013100.0) (p = 0.002), CNRscar-rem was lower in group A (62.9; 52.2\u201387.4) than in group B (96.5; 73.1\u2013152.8) (p = 0.008) and in group C (103.9; 83.9\u2013132.0) (p = 0.001). No other significant differences were found (p 65 0.335). Conclusions: Gadobutrol at 0.10 mmol/kg provides inferior scar image quality of CMI than 0.15 and 0.20 mmol/kg; the last two dosages seem to provide similar LGE. Thus, for CMR of CMI, 0.15 mmol/kg of gadobutrol can be suggested instead of 0.20 mmol/kg, with no hindrance to scar visualisation. Dose reduction would not impact on diagnostic utility of CMR examinations

Impact of artificial intelligence on radiology: a EuroAIM survey among members of the European Society of Radiology

We report the results of a survey conducted among ESR members in November and December 2018, asking for expectations about artificial intelligence (AI) in 5-10 years. Of 24,000 ESR members contacted, 675 (2.8%) completed the survey, 454 males (67%), 555 (82%) working at academic/public hospitals. AI impact was mostly expected (>= 30% of responders) on breast, oncologic, thoracic, and neuro imaging, mainly involving mammography, computed tomography, and magnetic resonance. Responders foresee AI impact on: job opportunities (375/675, 56%), 218/375 (58%) expecting increase, 157/375 (42%) reduction; reporting workload (504/675, 75%), 256/504 (51%) expecting reduction, 248/504 (49%) increase; radiologist's profile, becoming more clinical (364/675, 54%) and more subspecialised (283/675, 42%). For 374/675 responders (55%) AI-only reports would be not accepted by patients, for 79/675 (12%) accepted, for 222/675 (33%) it is too early to answer. For 275/675 responders (41%) AI will make the radiologist-patient relation more interactive, for 140/675 (21%) more impersonal, for 259/675 (38%) unchanged. If AI allows time saving, radiologists should interact more with clinicians (437/675, 65%) and/or patients (322/675, 48%). For all responders, involvement in AI-projects is welcome, with different roles: supervision (434/675, 64%), task definition (359/675, 53%), image labelling (197/675, 29%). Of 675 responders, 321 (48%) do not currently use AI, 138 (20%) use AI, 205 (30%) are planning to do it. According to 277/675 responders (41%), radiologists will take responsibility for AI outcome, while 277/675 (41%) suggest shared responsibility with other professionals. To summarise, responders showed a general favourable attitude towards AI

Novel imaging biomarkers: epicardial adipose tissue evaluation

Epicardial adipose tissue (EAT) is a metabolically activated beige adipose tissue, non-homogeneously surrounding the myocardium. Physiologically, EAT regulates toxic fatty acids, protects the coronary arteries against mechanical strain, regulates proinflammatory cytokines, stimulates the production of nitric oxide, reduces oxidative stress, and works as a thermogenic source against hypothermia. Conversely, EAT has pathologic paracrine interactions with the surrounded vessels, and might favour the onset of atrial fibrillation. In addition, initial atherosclerotic lesions can promote inflammation and trigger the EAT production of cytokines increasing vascular inflammation, which, in turn, may help the development of collateral vessels but also of self-stimulating, dysregulated inflammatory process, increasing coronary artery disease severity. Variations in EAT were also linked to metabolic syndrome. Echocardiography first estimated EAT measuring its thickness on the free wall of the right ventricle but does not allow accurate volumetric EAT estimates. Cardiac CT (CCT) and cardiac MR (CMR) allow for three-dimensional EAT estimates, the former showing higher spatial resolution and reproducibility but being limited by radiation exposure and long segmentation times, the latter being radiation-free but limited by lower spatial resolution and reproducibility, higher cost, and difficulties for obese patients. EAT radiodensity at CCT could to be related to underlying metabolic processes. The correlation between EAT and response to certain pharmacological therapies has also been investigated, showing promising results. In the future, semi-automatic or fully automatic techniques, machine/deep-learning methods, if validated, will facilitate research for various EAT measures and may find a place in CCT/CMR reporting

The face of Glut1-DS patients : A 3D craniofacial morphometric analysis

Introduction - Glut1 deficiency syndrome (Glut1-DS) is a neurological and metabolic disorder caused by impaired transport of glucose across the blood brain barrier (BBB). Mutations on the SCL2A1 gene encoding the glucose transporter protein in the BBB cause the syndrome, which encompasses epilepsy, movement disorders and mental delay. Such variability of symptoms presents an obstacle to early diagnosis. The patients seem to share some craniofacial features, and identification and quantification of these could help in prompt diagnosis and clinical management. Materials and method - We performed a three-dimensional morphometric analysis of the faces of 11 female Glut1-DS patients using a stereophotogrammetric system. Data were analyzed using both inter-landmark distances and Principal Component Analysis (PCA). Results - Compared to data collected from age-, sex- and ethnicity-matched control subjects, common and homogenous facial features were identified among patients, which were mainly located in the mandible and the eyes. Glut1-DS patients had a more anterior chin; their mandibular body was longer but the rami were shorter, with a reduced gonial angle; they had smaller and down-slanted eyes with a reduced intercanthal distance. Conclusions - This study highlights the importance of morphometric analysis for defining the facial anatomical characteristics of the syndrome better, potentially helping clinicians to diagnose Glut1-DS. Imnproved knowledge of the facial anatomy of these patients can provide insights into their facial and cerebral embryological development, perhaps further clarifying the molecular basis of the syndrome

Longitudinal analysis of palatal volume in unilateral cleft lip and palate children

Cleft lip and/or palate are among the most frequent congenital craniofacial defects, which every year affect one in 500-700 newborn worldwide (1,2). The aim of this study was to analyze the effect of orthopaedic and surgical treatments on the palatal size and shape of patients with unilateral cleft lip and palate (UCLP). Ninetysix palatal casts from 32 neonatal patients, attending the Fundacion Clinica de Noel (Colombia) were analyzed through a stereophotogrammetric system. The analysis was carried out before (mean age 10.5 days, SD 4.8) and after (mean age 83.3 days, SD 6.6) orthopaedic treatment (performed with acrylic plates) and after cheiloplasty (mean age 317.1 days, SD 44.2). Volumes of the greater and the minor alveolar segments were evaluated through a new measurement protocol. Intra and inter operator repeatability was evaluated using paired Student\u2019s t test. In order to investigate differences between alveolar segments and time, volume measurements were compared with a repeated two-way analysis of variance (ANOVA). No significant differences between repetitions, both intra and inter operator, were found (p>0.05). Random errors explained 3.7% of the sample variance. On the other hand, significant differences in volume measurements were found both in alveolar segment and time (p<0.01). Before orthopaedic treatment, the smaller palatal segment had a mean volume of 0.52 cm3 (SD 0.23), and the greater of 0.9 cm3 (SD 0.40); after orthopaedic treatment, the mean volumes were 0.58 cm3 (SD 0.25), and 1.09 cm3 (SD 0.43). After surgery, mean values of 0.73 cm3 (SD 0.28) and 1.31 cm3 (SD 0.52) were measured. Results suggest that a three-dimensional stereophotogrammetric system is a repeatable and reliable method to evaluate palatal casts of patients with UCLP. Obtained data offer a preliminary quantitative information about the changes occurring in maxillary arches of UCLP patients after orthopaedic treatment and surgery. Further investigation is required to increase the frequency of evaluations and the number of patients

VALIDATION OF AN AUTOMATIC HARD TISSUE SEGMENTATION ALGORITHM FOR CONE BEAM CT DATA

An automatic algorithm for hard tissue segmentation in CBCT data is presented and validated on 30 subjects. Bone segmentation threshold was set after voxel clustering through a sub-set of slices and the elimination of outliers with teeth and metal artifacts. Comparison with manual thresholding by experts gave no significant difference