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Mutations in isocitrate dehydrogenase genes IDH1 and IDH2 are frequently found in diffuse and anaplastic astrocytic and oligodendroglial tumours as well as in secondary glioblastomas. As IDH is a very important prognostic, diagnostic and therapeutic biomarker for glioma, it is of paramount importance to determine its mutational status. The haematoxylin and eosin (H&E) staining is a valuable tool in precision oncology as it guides histopathology-based diagnosis and proceeding patient’s treatment. However, H&E staining alone does not determine the IDH mutational status of a tumour. Deep learning methods applied to MRI data have been demonstrated to be a useful tool in IDH status prediction, however the effectiveness of deep learning on H&E slides in the clinical setting has not been investigated so far. Furthermore, the performance of deep learning methods in medical imaging has been practically limited by small sample sizes currently available. Here we propose a data augmentation method based on the Generative Adversarial Networks (GAN) deep learning methodology, to improve the prediction performance of IDH mutational status using H&E slides. The H&E slides were acquired from 266 grade II-IV glioma patients from a mixture of public and private databases, including 130 IDH-wildtype and 136 IDH-mutant patients. A baseline deep learning model without data augmentation achieved an accuracy of 0.794 (AUC = 0.920). With GAN-based data augmentation, the accuracy of the IDH mutational status prediction was improved to 0.853 (AUC = 0.927) when the 3,000 GAN generated training samples were added to the original training set (24,000 samples). By integrating also patients’ age into the model, the accuracy improved further to 0.882 (AUC = 0.931). Our findings show that deep learning methodology, enhanced by GAN data augmentation, can support physicians in gliomas’ IDH status prediction.

The personalization of conversational agents with natural language user interfaces is seeing increasing use in health care applications, shaping the content, structure, or purpose of the dialogue between humans and conversational agents. The goal of this systematic review was to understand the ways in which personalization has been used with conversational agents in health care and characterize the methods of its implementation. We searched on PubMed, Embase, CINAHL, PsycInfo, and ACM Digital Library using a predefined search strategy. The studies were included if they: (1) were primary research studies that focused on consumers, caregivers, or health care professionals; (2) involved a conversational agent with an unconstrained natural language interface; (3) tested the system with human subjects; and (4) implemented personalization features. The search found 1958 publications. After abstract and full-text screening, 13 studies were included in the review. Common examples of personalized content included feedback, daily health reports, alerts, warnings, and recommendations. The personalization features were implemented without a theoretical framework of customization and with limited evaluation of its impact. While conversational agents with personalization features were reported to improve user satisfaction, user engagement and dialogue quality, the role of personalization in improving health outcomes was not assessed directly. Most of the studies in our review implemented the personalization features without theoretical or evidence-based support for them and did not leverage the recent developments in other domains of personalization. Future research could incorporate personalization as a distinct design factor with a more careful consideration of its impact on health outcomes and its implications on patient safety, privacy, and decision-making.

Audio signal classification finds various applications in detecting and monitoring health conditions in healthcare. Convolutional neural networks (CNN) have produced state-of-the-art results in image classification and are being increasingly used in other tasks, including signal classification. However, audio signal classification using CNN presents various challenges. In image classification tasks, raw images of equal dimensions can be used as a direct input to CNN. Raw time-domain signals, on the other hand, can be of varying dimensions. In addition, the temporal signal often has to be transformed to frequency-domain to reveal unique spectral characteristics, therefore requiring signal transformation. In this work, we overview and benchmark various audio signal representation techniques for classification using CNN, including approaches that deal with signals of different lengths and combine multiple representations to improve the classification accuracy. Hence, this work surfaces important empirical evidence that may guide future works deploying CNN for audio signal classification purposes.

A central need for neurodegenerative diseases is to find curative drugs for the many clinical subtypes, the causative gene for most cases being unknown. This requires the classification of disease cases at the genetic and cellular level, an understanding of disease aetiology in the subtypes and the development of phenotypic assays for high throughput screening of large compound libraries. Herein we describe a method that facilitates these requirements based on cell morphology that is being increasingly used as a readout defining cell state. In patient-derived fibroblasts we quantified 124 morphological features in 100,000 cells from 15 people with two genotypes ( SPAST and SPG7 ) of Hereditary Spastic Paraplegia (HSP) and matched controls. Using machine learning analysis, we distinguished between each genotype and separated them from controls. Cell morphologies changed with treatment with noscapine, a tubulin-binding drug, in a genotype-dependent manner, revealing a novel effect on one of the genotypes ( SPG7 ). These findings demonstrate a method for morphological profiling in fibroblasts, an accessible non-neural cell, to classify and distinguish between clinical subtypes of neurodegenerative diseases, for drug discovery, and potentially for biomarkers of disease severity and progression.

Clinicians spend a large amount of time on clinical documentation of patient encounters, often impacting quality of care and clinician satisfaction, and causing physician burnout. Advances in artificial intelligence (AI) and machine learning (ML) open the possibility of automating clinical documentation with digital scribes, using speech recognition to eliminate manual documentation by clinicians or medical scribes. However, developing a digital scribe is fraught with problems due to the complex nature of clinical environments and clinical conversations. This paper identifies and discusses major challenges associated with developing automated speech-based documentation in clinical settings: recording high-quality audio, converting audio to transcripts using speech recognition, inducing topic structure from conversation data, extracting medical concepts, generating clinically meaningful summaries of conversations, and obtaining clinical data for AI and ML algorithms.

We assessed the performance of large language models’ summarizing clinical dialogues using computational metrics and human evaluations. The comparison was done between automatically generated and human-produced summaries. We conducted an exploratory evaluation of five language models: one general summarisation model, one fine-tuned for general dialogues, two fine-tuned with anonymized clinical dialogues, and one Large Language Model (ChatGPT). These models were assessed using ROUGE, UniEval metrics, and expert human evaluation was done by clinicians comparing the generated summaries against a clinician generated summary (gold standard). The fine-tuned transformer model scored the highest when evaluated with ROUGE, while ChatGPT scored the lowest overall. However, using UniEval, ChatGPT scored the highest across all the evaluated domains (coherence 0.957, consistency 0.7583, fluency 0.947, and relevance 0.947 and overall score 0.9891). Similar results were obtained when the systems were evaluated by clinicians, with ChatGPT scoring the highest in four domains (coherency 0.573, consistency 0.908, fluency 0.96 and overall clinical use 0.862). Statistical analyses showed differences between ChatGPT and human summaries vs. all other models. These exploratory results indicate that ChatGPT’s performance in summarizing clinical dialogues approached the quality of human summaries. The study also found that the ROUGE metrics may not be reliable for evaluating clinical summary generation, whereas UniEval correlated well with human ratings. Large language models may provide a successful path for automating clinical dialogue summarization. Privacy concerns and the restricted nature of health records remain challenges for its integration. Further evaluations using diverse clinical dialogues and multiple initialization seeds are needed to verify the reliability and generalizability of automatically generated summaries.

For this, we treat AI as a black box, to focus just on how human clinicians can best harness it. [...]we leave explainablity beyond the scope of discussion because it affects the design of AI. If not designed properly, we can realistically expect clinician interaction with AI to further aggravate this problem. [...]clinical AI needs not just to be accurate, but also usable. [...]better integration of such innovative interaction paradigms into the current clinical protocols is an essential step towards their adoption. 3.

Preventive strategies integrated with digital health and artificial intelligence (AI) have significant potential to mitigate the global burden of atherosclerotic cardiovascular disease (ASCVD). AI-enabled clinical decision support (CDS) systems increasingly provide patient-specific insights beyond traditional risk factors. Despite these advances, their capacity to enhance clinical decision-making in resource-constrained settings remains largely unexplored. We conducted a randomized controlled study to assess the effect of AI-based CDS on 10-year ASCVD risk assessment and management in primary prevention. In a 3-way, within-subject randomized design, doctors completed 9 clinical vignettes representative of primary care presentations in a resource-constrained outpatient setting. For each vignette, participants assessed 10-year ASCVD risk and made management decisions using a conceptual prototype of AI-based CDS, automated CDS, or no decision support. The conceptual prototype represented contemporary risk calculators based on traditional machine learning models (eg, random forest, neural networks, logistic regression) that incorporate additional predictors alongside traditional risk factors. Primary outcomes were correct risk assessment and patient management (prescription of aspirin, statins, and antihypertensives; referral for advanced examinations). Decision-making time and perceptions about AI utility were also measured. In total, 102 doctors from all 7 geographical regions of Indonesia participated. Most (n=85, 83%) participants were 26-35 years of age, and 57 (56%) were male, with a median of 6 (IQR 4.75) years of clinical experience. AI-based CDS improved risk assessment by 27% (χ22 (n=102)=48.875, P<.001) when compared to unassisted risk assessment, equating to 1 additional correct risk classification for every 3.7 patients where doctors used AI (number needed to treat=3.7, 95% CI 2.9-5.2). The prescription of statins also improved by 29% (χ22 (n=102)=36.608, P<.001). In pairwise comparisons, doctors who were assisted by the AI-based CDS correctly assessed significantly more cases (z=-5.602, n=102, adjusted P<.001) and prescribed the appropriate statin more often (z=-4.936, adjusted P<.001, medium effect size r=0.35) when compared with the control. AI-assisted cases required less time (estimated marginal means 63.6 s vs 72.8 s, F2, 772.8=5.710, P=.003). However, improvements in the prescription of aspirin and antihypertensives did not reach statistical significance (P=.08 and P=.30, respectively). No improvement was observed in referral decisions. Participants generally viewed AI-based CDS positively, with 81 (79%) agreeing or strongly agreeing that they would follow its recommendations and 82 (82%) indicating they would use it if given access. They believed CDS could enhance the efficiency of risk assessment, particularly in high-volume primary care settings, while noting the need to verify AI recommendations against clinical guidelines for each patient. Improvements in risk assessment and statin prescription, coupled with reduced decision-making time, highlight the potential utility of AI in ASCVD risk assessment, particularly in resource-constrained settings where efficient use of health care resources and doctors' time is crucial. Further research is needed to ascertain whether improvements observed in this online study translate to real-world low-resource settings.

Immersive virtual reality (iVR) has gained considerable attention recently with increasing affordability and accessibility of the hardware. iVR applications for older adults present tremendous potential for diverse interventions and innovations. The iVR literature, however, provides a limited understanding of guiding design considerations and evaluations pertaining to user experience (UX). To address this gap, we present a state-of-the-art scoping review of literature on iVR applications developed for older adults over 65 years. We performed a search in ACM Digital Library, IEEE Xplore, Scopus, and PubMed (1 January 2010–15 December 2019) and found 36 out of 3874 papers met the inclusion criteria. We identified 10 distinct sets of design considerations that guided target users and physical configuration, hardware use, and software design. Most studies carried episodic UX where only 2 captured anticipated UX and 7 measured longitudinal experiences. We discuss the interplay between our findings and future directions to design effective, safe, and engaging iVR applications for older adults.

Mass community testing is a critical means for monitoring the spread of the COVID-19 pandemic. Polymerase chain reaction (PCR) is the gold standard for detecting the causative coronavirus 2 (SARS-CoV-2) but the test is invasive, test centers may not be readily available, and the wait for laboratory results can take several days. Various machine learning based alternatives to PCR screening for SARS-CoV-2 have been proposed, including cough sound analysis. Cough classification models appear to be a robust means to predict infective status, but collecting reliable PCR confirmed data for their development is challenging and recent work using unverified crowdsourced data is seen as a viable alternative. In this study, we report experiments that assess cough classification models trained (i) using data from PCR-confirmed COVID subjects and (ii) using data of individuals self-reporting their infective status. We compare performance using PCR-confirmed data. Models trained on PCR-confirmed data perform better than those trained on patient-reported data. Models using PCR-confirmed data also exploit more stable predictive features and converge faster. Crowd-sourced cough data is less reliable than PCR-confirmed data for developing predictive models for COVID-19, and raises concerns about the utility of patient reported outcome data in developing other clinical predictive models when better gold-standard data are available.