Machine vs. Radiologist-Based Translations of RadLex: Implications for Multi-language Report Interoperability.

The purpose of this study was to evaluate the feasibility of translation of RadLex lexicon from English to German performed by Google Translate, using the RadLex ontology as ground truth. The same comparison was also performed for German to English translations. We determined the concordance rate of the Google Translate-rendered translations (for both English to German and German to English translations) to the official German RadLex (translations provided by the German Radiological Society) and English RadLex terms via character-by-character concordance analysis (string matching). Specific term characteristics of term character count and word count were compared between concordant and discordant translations using t-tests. Google Translate-rendered translations originally considered incongruent (2482 English terms and 2500 German terms) were then reviewed by German and English-speaking radiologists to further evaluate clinical utility. Overall success rates of both methods were calculated by adding the percentage of terms marked correct by string comparison to the percentage marked correct during manual review extrapolated to the terms that had been initially marked incorrect during string analysis. 64,632 English and 47,425 German RadLex terms were analyzed. 3507 (5.4%) of the Google Translate-rendered English to German translations were concordant with the official German RadLex terms when evaluated via character-by-character concordance. 3288 (6.9%) of the Google Translate-rendered German to English translations matched the corresponding English RadLex terms. Human review of a random sample of non-concordant machine translations revealed that 95.5% of such English to German translations were understandable, whereas 43.9% of such German to English translations were understandable. Combining both string matching and human review resulted in an overall Google Translate success rate of 95.7% for English to German translations and 47.8% for German to English translations. For certain radiologic text translation tasks, Google Translate may be a useful tool for translating multi-language radiology reports into a common language for natural language processing and subsequent labeling of datasets for machine learning. Indeed, string matching analysis alone is an incomplete method for evaluating machine translation. However, when human review of automated translation is also incorporated, measured performance improves. Additional evaluation using longer text samples and full imaging reports is needed. An apparent discordance between English to German versus German to English translation suggests that the direction of translation affects accuracy.

AUR-RRA Review: Logistics of Academic-Industry Partnerships in Artificial Intelligence.

The Radiology Research Alliance (RRA) of the Association of University Radiologists (AUR) convenes Task Forces to address current topics in radiology. In this article, the AUR-RRA Task Force on Academic-Industry Partnerships for Artificial Intelligence, considered issues of importance to academic radiology departments contemplating industry partnerships in artificial intelligence (AI) development, testing and evaluation. Our goal was to create a framework encompassing the domains of clinical, technical, regulatory, legal and financial considerations that impact the arrangement and success of such partnerships.

Initial Experience With Low-Dose 18F-Fluorodeoxyglucose Positron Emission Tomography/Magnetic Resonance Imaging With Deep Learning Enhancement.

OBJECTIVE: To demonstrate the utility of deep learning enhancement (DLE) to achieve diagnostic quality low-dose positron emission tomography (PET)/magnetic resonance (MR) imaging. METHODS: Twenty subjects with known Crohn disease underwent simultaneous PET/MR imaging after intravenous administration of approximately 185 MBq of 18F-fluorodeoxyglucose (FDG). Five image sets were generated: (1) standard-of-care (reference), (2) low-dose (ie, using 20% of PET counts), (3) DLE-enhanced low-dose using PET data as input, (4) DLE-enhanced low-dose using PET and MR data as input, and (5) DLE-enhanced using no PET data input. Image sets were evaluated by both quantitative metrics and qualitatively by expert readers. RESULTS: Although low-dose images (series 2) and images with no PET data input (series 5) were nondiagnostic, DLE of the low-dose images (series 3 and 4) achieved diagnostic quality images that scored more favorably than reference (series 1), both qualitatively and quantitatively. CONCLUSIONS: Deep learning enhancement has the potential to enable a 90% reduction of radiotracer while achieving diagnostic quality images.

Medical Student Perspectives on the Impact of Artificial Intelligence on the Practice of Medicine.

INTRODUCTION: Concerns about radiologists being replaced by artificial intelligence (AI) from the lay media could have a negative impact on medical students' perceptions of radiology as a viable specialty. The purpose of this study was to evaluate United States of America medical students' perceptions about radiology and other medical specialties in relation to AI. METHODS: An anonymous, web-based survey was sent to 32 radiology interest groups at United States medical schools. The survey was comprised of 6 questions assessing medical student perceptions of AI and its potential impact on radiology and other medical specialties. Responses were voluntary and collected over a 6-month period from November 2017 to April 2018. RESULTS: A total of 156 students responded with representation from each year of medical school. Over 75% agreed that AI would have a significant role in the future of medicine. Most (66%) agreed that diagnostic radiology would be the specialty most greatly affected. Nearly half (44%) reported that AI made them less enthusiastic about radiology. The majority of students (57%) obtained their information about AI from online articles. Thematic analysis of free answer comments revealed mostly neutral comments towards AI, however, the negative responses were the strongest and most detailed. CONCLUSIONS: US medical students believe that AI will play a significant role in medicine, particularly in radiology. However, nearly half are less enthusiastic about the field of radiology due to AI. As the majority receive information about AI from online articles, which may have negative sentiments towards AI's impact on radiology, formal AI education and medical student outreach may help combat misinformation and help prevent the dissuading of medical students who might otherwise consider the specialty.

Ovariectomy-Induced Osteoporosis Does Not Impact Fusion Rates in a Recombinant Human Bone Morphogenetic Protein-2-Dependent Rat Posterolateral Arthrodesis Model.

Study Design Randomized, controlled animal study. Objective Recombinant human bone morphogenetic protein-2 (rhBMP-2) is frequently utilized as a bone graft substitute in spinal fusions to overcome the difficult healing environment in patients with osteoporosis. However, the effects of estrogen deficiency and poor bone quality on rhBMP-2 efficacy are unknown. This study sought to determine whether rhBMP-2-induced healing is affected by estrogen deficiency and poor bone quality in a stringent osteoporotic posterolateral spinal fusion model. Methods Aged female Sprague-Dawley rats underwent an ovariectomy (OVX group) or a sham procedure, and the OVX animals were fed a low-calcium, low-phytoestrogen diet. After 12 weeks, the animals underwent a posterolateral spinal fusion with 1 µg rhBMP-2 on an absorbable collagen sponge. Representative animals were sacrificed at 1 week postoperative for alkaline phosphatase (ALP) and osteocalcin serum analyses. The remaining animals underwent radiographs 2 and 4 weeks after surgery and were subsequently euthanized for fusion analysis by manual palpation, micro-computed tomography (CT) imaging, and histologic analysis. Results The ALP and osteocalcin levels were similar between the control and OVX groups. Manual palpation revealed no significant differences in the fusion scores between the control (1.42 ± 0.50) and OVX groups (1.83 ± 0.36; p = 0.07). Fusion rates were 100% in both groups. Micro-CT imaging revealed no significant difference in the quantity of new bone formation, and histologic analysis demonstrated bridging bone across the transverse processes in fused animals from both groups. Conclusions This study demonstrates that estrogen deficiency and compromised bone quality do not negatively influence spinal fusion when utilizing rhBMP-2, and the osteoinductive capacity of the growth factor is not functionally reduced under osteoporotic conditions in the rat. Although osteoporosis is a risk factor for pseudarthrosis/nonunion, rhBMP-2-induced healing was not inhibited in osteoporotic rats.