Eli Konen

Background The early identification of intracranial aneurysms (IAs) enables risk stratification and the timely initiation of optimal management. This study aimed to identify patients with missed aneurysms for follow-up and possible treatment, and to evaluate the effectiveness of a commercial deep learning algorithm in retrospectively detecting missed IAs on CTA. Methods All consecutive head CTA studies of adult patients performed at a single referral center between February 18, 2020, and July 31, 2022, were retrospectively collected. A machine learning algorithm using natural language processing (NLP) classified radiology reports as positive or negative for aneurysms, and a convolutional neural network (CNN) algorithm analyzed the imaging data. Concordant results with the original reports were accepted as ground truth, while discordant cases were reviewed by three neuroradiologists, with majority voting determining the reference standard. Results A total of 2,615 head CTA studies were analyzed. the algorithm flagged 34 suspected missed aneurysms, with 67% (23/34) confirmed as true positives by at least two neuroradiologists. This improved detection by 20.9% (23/110) or 0.88% of all studies. Most missed aneurysms were small (≤ 3 mm). There were 4 false negatives, resulting in a sensitivity of 96.36%, specificity of 99.56%, positive predictive value of 90.6%, and negative predictive value of 99.84%. Conclusion This study highlights the potential of deep learning systems to detect missed intracranial aneurysms. Although the missed aneurysms in this cohort were predominantly small, follow-up or diagnostic digital subtraction angiography may still be warranted, depending on clinical characteristics and risk factors for aneurysm rupture.

Background: The term “fatty kidney” refers to the accumulation of fat within the kidney. There is no clear definition of a fatty kidney. In our previous study, we defined a fatty kidney as one with fat accumulation of more than 4% in the kidney as detected by magnetic resonance imaging (MRI). This condition is associated with renal inflammation and contributes to the development of kidney dysfunction. Fat accumulation in the kidney can be detected using imaging modalities such as computed tomography (CT) or MRI. Given the clinical importance of renal fat deposition, the aim of this review was to investigate how imaging findings in this condition correlate to disease prevalence and metabolic disorders. Methods: A systematic review was conducted in accordance with the PRISMA guidelines. The databases searched included PubMed, Scopus, Web of Science, and Cochrane Library up to August 2024. Studies employing MRI or CT for renal fat quantification were included. Data were extracted, and their quality was assessed using the QUADAS-2 tool. Results: Twenty-eight studies comprising 6994 participants met the inclusion criteria. Most studies used MRI (75%) for fat quantification, with CT limited to renal sinus evaluation. Renal fat fractions (FFs) ranged from 0.4% to 55.3%, with higher values consistently observed in individuals with obesity, diabetes, chronic kidney disease, and hypertension. A consistent positive association was observed between fatty kidney and fatty liver, suggesting shared pathogenic mechanisms. Conclusions: Fatty kidney appears to be a distinct and clinically relevant entity with strong links to metabolic dysfunction. Imaging-based quantification—particularly MRI—offers a promising tool for early detection, yet standardization is needed. The findings underscore the need for further research into fatty kidney as a modifiable risk factor for renal and cardiovascular disease.

Background: Empathy, a fundamental aspect of human interaction, is characterized as the ability to experience another being’s emotions within oneself. In health care, empathy is a fundamental for health care professionals and patients’ interaction. It is a unique quality to humans that large language models (LLMs) are believed to lack. Objective: We aimed to review the literature on the capacity of LLMs in demonstrating empathy. Methods: We conducted a literature search on MEDLINE, Google Scholar, PsyArXiv, medRxiv, and arXiv between December 2022 and February 2024. We included English-language full-length publications that evaluated empathy in LLMs’ outputs. We excluded papers evaluating other topics related to emotional intelligence that were not specifically empathy. The included studies’ results, including the LLMs used, performance in empathy tasks, and limitations of the models, along with studies’ metadata were summarized. Results: A total of 12 studies published in 2023 met the inclusion criteria. ChatGPT-3.5 (OpenAI) was evaluated in all studies, with 6 studies comparing it with other LLMs such GPT-4, LLaMA (Meta), and fine-tuned chatbots. Seven studies focused on empathy within a medical context. The studies reported LLMs to exhibit elements of empathy, including emotions recognition and emotional support in diverse contexts. Evaluation metric included automatic metrics such as Recall-Oriented Understudy for Gisting Evaluation and Bilingual Evaluation Understudy, and human subjective evaluation. Some studies compared performance on empathy with humans, while others compared between different models. In some cases, LLMs were observed to outperform humans in empathy-related tasks. For example, ChatGPT-3.5 was evaluated for its responses to patients’ questions from social media, where ChatGPT’s responses were preferred over those of humans in 78.6% of cases. Other studies used subjective readers’ assigned scores. One study reported a mean empathy score of 1.84-1.9 (scale 0-2) for their fine-tuned LLM, while a different study evaluating ChatGPT-based chatbots reported a mean human rating of 3.43 out of 4 for empathetic responses. Other evaluations were based on the level of the emotional awareness scale, which was reported to be higher for ChatGPT-3.5 than for humans. Another study evaluated ChatGPT and GPT-4 on soft-skills questions in the United States Medical Licensing Examination, where GPT-4 answered 90% of questions correctly. Limitations were noted, including repetitive use of empathic phrases, difficulty following initial instructions, overly lengthy responses, sensitivity to prompts, and overall subjective evaluation metrics influenced by the evaluator’s background. Conclusions: LLMs exhibit elements of cognitive empathy, recognizing emotions and providing emotionally supportive responses in various contexts. Since social skills are an integral part of intelligence, these advancements bring LLMs closer to human-like interactions and expand their potential use in applications requiring emotional intelligence. However, there remains room for improvement in both the performance of these models and the evaluation strategies used for assessing soft skills.

Background: Obesity is associated with metabolic syndrome and fat accumulation in various organs such as the liver and the kidneys. Our goal was to assess, using magnetic resonance imaging (MRI) Dual-Echo phase sequencing, the association between liver and kidney fat deposition and their relation to obesity. Methods: We analyzed MRI scans of individuals who were referred to the Chaim Sheba Medical Center between December 2017 and May 2020 to perform a study for any indication. For each individual, we retrieved from the computerized charts data on sex, and age, weight, height, body mass index (BMI), systolic and diastolic blood pressure (BP), and comorbidities (diabetes mellitus, hypertension, dyslipidemia). Results: We screened MRI studies of 399 subjects with a median age of 51 years, 52.4% of whom were women, and a median BMI 24.6 kg/m². We diagnosed 18% of the participants with fatty liver and 18.6% with fat accumulation in the kidneys (fatty kidneys). Out of the 67 patients with fatty livers, 23 (34.3%) also had fatty kidneys, whereas among the 315 patients without fatty livers, only 48 patients (15.2%) had fatty kidneys (p < 0.01). In comparison to the patients who did not have a fatty liver or fatty kidneys (n = 267), those who had both (n = 23) were more obese, had higher systolic BP, and were more likely to have diabetes mellitus. In comparison to the patients without a fatty liver, those with fatty livers had an adjusted odds ratio of 2.91 (97.5% CI; 1.61–5.25) to have fatty kidneys. In total, 19.6% of the individuals were obese (BMI ≥ 30), and 26.1% had overweight (25 < BMI < 30). The obese and overweight individuals were older and more likely to have diabetes mellitus and hypertension and had higher rates of fatty livers and fatty kidneys. Fat deposition in both the liver and the kidneys was observed in 15.9% of the obese patients, in 8.3% of the overweight patients, and in none of those with normal weight. Obesity was the only risk factor for fatty kidneys and fatty livers, with an adjusted OR of 6.3 (97.5% CI 2.1–18.6). Conclusions: Obesity is a major risk factor for developing a fatty liver and fatty kidneys. Individuals with a fatty liver are more likely to have fatty kidneys. MRI is an accurate modality for diagnosing fatty kidneys. Reviewing MRI scans of any indication should include assessment of fat fractions in the kidneys in addition to that of the liver.

BACKGROUND Empathy, a fundamental aspect of human interaction, is characterized as the ability to experience another being’s emotions within oneself. In health care, empathy is a fundamental for health care professionals and patients’ interaction. It is a unique quality to humans that large language models (LLMs) are believed to lack. OBJECTIVE We aimed to review the literature on the capacity of LLMs in demonstrating empathy. METHODS We conducted a literature search on MEDLINE, Google Scholar, PsyArXiv, medRxiv, and arXiv between December 2022 and February 2024. We included English-language full-length publications that evaluated empathy in LLMs’ outputs. We excluded papers evaluating other topics related to emotional intelligence that were not specifically empathy. The included studies’ results, including the LLMs used, performance in empathy tasks, and limitations of the models, along with studies’ metadata were summarized. RESULTS A total of 12 studies published in 2023 met the inclusion criteria. ChatGPT-3.5 (OpenAI) was evaluated in all studies, with 6 studies comparing it with other LLMs such GPT-4, LLaMA (Meta), and fine-tuned chatbots. Seven studies focused on empathy within a medical context. The studies reported LLMs to exhibit elements of empathy, including emotions recognition and emotional support in diverse contexts. Evaluation metric included automatic metrics such as Recall-Oriented Understudy for Gisting Evaluation and Bilingual Evaluation Understudy, and human subjective evaluation. Some studies compared performance on empathy with humans, while others compared between different models. In some cases, LLMs were observed to outperform humans in empathy-related tasks. For example, ChatGPT-3.5 was evaluated for its responses to patients’ questions from social media, where ChatGPT’s responses were preferred over those of humans in 78.6% of cases. Other studies used subjective readers’ assigned scores. One study reported a mean empathy score of 1.84-1.9 (scale 0-2) for their fine-tuned LLM, while a different study evaluating ChatGPT-based chatbots reported a mean human rating of 3.43 out of 4 for empathetic responses. Other evaluations were based on the level of the emotional awareness scale, which was reported to be higher for ChatGPT-3.5 than for humans. Another study evaluated ChatGPT and GPT-4 on soft-skills questions in the United States Medical Licensing Examination, where GPT-4 answered 90% of questions correctly. Limitations were noted, including repetitive use of empathic phrases, difficulty following initial instructions, overly lengthy responses, sensitivity to prompts, and overall subjective evaluation metrics influenced by the evaluator’s background. CONCLUSIONS LLMs exhibit elements of cognitive empathy, recognizing emotions and providing emotionally supportive responses in various contexts. Since social skills are an integral part of intelligence, these advancements bring LLMs closer to human-like interactions and expand their potential use in applications requiring emotional intelligence. However, there remains room for improvement in both the performance of these models and the evaluation strategies used for assessing soft skills.

Purpose MRI has become the tool of choice for brain imaging, providing unrivalled contrast between soft tissues, as well as a wealth of information about anatomy, function, and neurochemistry. Image quality, in terms of spatial resolution and noise, is strongly dependent on acquisition duration. A typical brain MRI scan may last several minutes, with total protocol duration often exceeding 30 minutes. Long scan duration leads to poor patient experience, long waiting time for appointments, and high costs. Therefore, shortening MRI scans is crucial. In this paper, we investigate the enhancement of low-resolution (LR) brain MRI scanning, to enable shorter acquisition times without compromising the diagnostic value of the images. Methods We propose a novel fully convolutional neural enhancement approach. It is optimized for accelerated LR MRI acquisitions obtained by reducing the acquisition matrix size only along phase encoding direction. The network is trained to transform the LR acquisitions into corresponding high-resolution (HR) counterparts in an end-to-end manner. In contrast to previous neural-based MRI enhancement algorithms, such as DAGAN, the LR images used for training are real acquisitions rather than smoothed, downsampled versions of the HR images. Results The proposed method is validated qualitatively and quantitatively for an acceleration factor of 4. Favourable comparison is demonstrated against the state-of-the-art DeblurGAN and DAGAN algorithms in terms of PSNR and SSIM scores. The result was further confirmed by an image quality rating experiment performed by four senior neuroradiologists. Conclusions The proposed method may become a valuable tool for scan time reduction in brain MRI. In continuation of this research, the validation should be extended to larger datasets acquired for different imaging protocols, and considering several MRI machines produced by different vendors.

Rapid and sensitive screening tools for SARS-CoV-2 infection are essential to limit the spread of COVID-19 and to properly allocate national resources. Here, we developed a new point-of-care, non-contact thermal imaging tool to detect COVID-19, based on image-processing algorithms and machine learning analysis. We captured thermal images of the back of individuals with and without COVID-19 using a portable thermal camera that connects directly to smartphones. Our novel image processing algorithms automatically extracted multiple texture and shape features of the thermal images and achieved an area under the curve (AUC) of 0.85 in detecting COVID-19 with up to 92% sensitivity. Thermal imaging scores were inversely correlated with clinical variables associated with COVID-19 disease progression. We show, for the first time, that a hand-held thermal imaging device can be used to detect COVID-19. Non-invasive thermal imaging could be used to screen for COVID-19 in out-of-hospital settings, especially in low-income regions with limited imaging resources. HIGHLIGHTS Automated processing of thermal images of the back can be used to detect COVID-19 with up to 92% sensitivity. The extracted texture features of the thermal image are associated with COVID-19 disease progression and lung injury. A portable thermal camera that connects directly to smartphones can be used to detect COVID-19. Non-invasive thermal imaging could be used to screen for COVID-19 in out-of-hospital settings and regions with limited imaging resources.