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Diagnostic and Interventional Imaging Jun 2024Radiology in Canada is advancing through innovations in clinical practices and research methodologies. Recent developments focus on refining evidence-based practice... (Review)
Review
Radiology in Canada is advancing through innovations in clinical practices and research methodologies. Recent developments focus on refining evidence-based practice guidelines, exploring innovative imaging techniques and enhancing diagnostic processes through artificial intelligence. Within the global radiology community, Canadian institutions play an important role by engaging in international collaborations, such as with the American College of Radiology to refine implementation of the Ovarian-Adnexal Reporting and Data System for ultrasound and magnetic resonance imaging. Additionally, researchers have participated in multidisciplinary collaborations to evaluate the performance of artificial intelligence-driven diagnostic tools for chronic liver disease and pediatric brain tumors. Beyond clinical radiology, efforts extend to addressing gender disparities in the field, improving educational practices, and enhancing the environmental sustainability of radiology departments. These advancements highlight Canada's role in the global radiology community, showcasing a commitment to improving patient outcomes and advancing the field through research and innovation. This update underscores the importance of continued collaboration and innovation to address emerging challenges and further enhance the quality and efficacy of radiology practices worldwide.
PubMed: 38942638
DOI: 10.1016/j.diii.2024.06.004 -
Briefings in Bioinformatics May 2024Accurate understanding of the biological functions of enzymes is vital for various tasks in both pathologies and industrial biotechnology. However, the existing methods...
Accurate understanding of the biological functions of enzymes is vital for various tasks in both pathologies and industrial biotechnology. However, the existing methods are usually not fast enough and lack explanations on the prediction results, which severely limits their real-world applications. Following our previous work, DEEPre, we propose a new interpretable and fast version (ifDEEPre) by designing novel self-guided attention and incorporating biological knowledge learned via large protein language models to accurately predict the commission numbers of enzymes and confirm their functions. Novel self-guided attention is designed to optimize the unique contributions of representations, automatically detecting key protein motifs to provide meaningful interpretations. Representations learned from raw protein sequences are strictly screened to improve the running speed of the framework, 50 times faster than DEEPre while requiring 12.89 times smaller storage space. Large language modules are incorporated to learn physical properties from hundreds of millions of proteins, extending biological knowledge of the whole network. Extensive experiments indicate that ifDEEPre outperforms all the current methods, achieving more than 14.22% larger F1-score on the NEW dataset. Furthermore, the trained ifDEEPre models accurately capture multi-level protein biological patterns and infer evolutionary trends of enzymes by taking only raw sequences without label information. Meanwhile, ifDEEPre predicts the evolutionary relationships between different yeast sub-species, which are highly consistent with the ground truth. Case studies indicate that ifDEEPre can detect key amino acid motifs, which have important implications for designing novel enzymes. A web server running ifDEEPre is available at https://proj.cse.cuhk.edu.hk/aihlab/ifdeepre/ to provide convenient services to the public. Meanwhile, ifDEEPre is freely available on GitHub at https://github.com/ml4bio/ifDEEPre/.
Topics: Deep Learning; Enzymes; Computational Biology; Software; Proteins; Databases, Protein; Algorithms
PubMed: 38942594
DOI: 10.1093/bib/bbae225 -
RMD Open Jun 2024Fibromyalgia (FM) is a complex disorder with widespread pain and emotional distress, posing diagnostic challenges. FM patients show altered cognitive and emotional...
BACKGROUND
Fibromyalgia (FM) is a complex disorder with widespread pain and emotional distress, posing diagnostic challenges. FM patients show altered cognitive and emotional processing, with a preferential allocation of attention to pain-related information. This attentional bias towards pain cues can impair cognitive functions such as inhibitory control, affecting patients' ability to manage and express emotions. Sentiment analysis using large language models (LLMs) can provide insights by detecting nuances in pain expression. This study investigated whether open-source LLM-driven sentiment analysis could aid FM diagnosis.
METHODS
40 patients with FM, according to the 2016 American College of Rheumatology Criteria and 40 non-FM chronic pain controls referred to rheumatology clinics, were enrolled. Transcribed responses to questions on pain and sleep were machine translated to English and analysed by the LLM Mistral-7B-Instruct-v0.2 using prompt engineering targeting FM-associated language nuances for pain expression ('prompt-engineered') or an approach without this targeting ('ablated'). Accuracy, precision, recall, specificity and area under the receiver operating characteristic curve (AUROC) were calculated using rheumatologist diagnosis as ground truth.
RESULTS
The prompt-engineered approach demonstrated accuracy of 0.87, precision of 0.92, recall of 0.84, specificity of 0.82 and AUROC of 0.86 for distinguishing FM. In comparison, the ablated approach had an accuracy of 0.76, precision of 0.75, recall of 0.77, specificity of 0.75 and AUROC of 0.76. The accuracy was superior to the ablated approach (McNemar's test p<0.001).
CONCLUSION
This proof-of-concept study suggests LLM-driven sentiment analysis, especially with prompt engineering, may facilitate FM diagnosis by detecting subtle differences in pain expression. Further validation is warranted, particularly the inclusion of secondary FM patients.
Topics: Humans; Fibromyalgia; Female; Middle Aged; Male; Adult; ROC Curve; Natural Language Processing; Language; Emotions; Aged; Chronic Pain
PubMed: 38942593
DOI: 10.1136/rmdopen-2024-004367 -
Progress in Molecular Biology and... 2024In the dynamic landscape of cancer therapeutics, the innovative strategy of drug repurposing emerges as a transformative paradigm, heralding a new era in the fight... (Review)
Review
In the dynamic landscape of cancer therapeutics, the innovative strategy of drug repurposing emerges as a transformative paradigm, heralding a new era in the fight against malignancies. This book chapter aims to embark on the comprehension of the strategic deployment of approved drugs for repurposing and the meticulous journey of drug repurposing from earlier times to the current era. Moreover, the chapter underscores the multifaceted and complex nature of cancer biology, and the evolving field of cancer drug therapeutics while emphasizing the mandate of drug repurposing to advance cancer therapeutics. Importantly, the narrative explores the latest tools, technologies, and cutting-edge methodologies including high-throughput screening, omics technologies, and artificial intelligence-driven approaches, for shaping and accelerating the pace of drug repurposing to uncover novel cancer therapeutic avenues. The chapter critically assesses the breakthroughs, expanding the repertoire of repurposing drug candidates in cancer, and their major categories. Another focal point of this book chapter is that it addresses the emergence of combination therapies involving repurposed drugs, reflecting a shift towards personalized and synergistic treatment approaches. The expert analysis delves into the intricacies of combinatorial regimens, elucidating their potential to target heterogeneous cancer populations and overcome resistance mechanisms, thereby enhancing treatment efficacy. Therefore, this chapter provides in-depth insights into the potential of repurposing towards bringing the much-needed big leap in the field of cancer therapeutics.
Topics: Drug Repositioning; Humans; Neoplasms; Antineoplastic Agents; Animals
PubMed: 38942535
DOI: 10.1016/bs.pmbts.2024.03.032 -
PDA Journal of Pharmaceutical Science... Jun 2024
Topics: Artificial Intelligence; Humans
PubMed: 38942478
DOI: 10.5731/pdajpst.2024.001834 -
Korean Journal of Radiology Jul 2024Evaluating the performance of a binary diagnostic test, including artificial intelligence classification algorithms, involves measuring sensitivity, specificity,... (Review)
Review
Evaluating the performance of a binary diagnostic test, including artificial intelligence classification algorithms, involves measuring sensitivity, specificity, positive predictive value, and negative predictive value. Particularly when comparing the performance of two diagnostic tests applied on the same set of patients, these metrics are crucial for identifying the more accurate test. However, comparing predictive values presents statistical challenges because their denominators depend on the test outcomes, unlike the comparison of sensitivities and specificities. This paper reviews existing methods for comparing predictive values and proposes using the permutation test. The permutation test is an intuitive, non-parametric method suitable for datasets with small sample sizes. We demonstrate each method using a dataset from MRI and combined modality of mammography and ultrasound in diagnosing breast cancer.
Topics: Humans; Breast Neoplasms; Female; Predictive Value of Tests; Magnetic Resonance Imaging; Mammography; Sensitivity and Specificity; Algorithms; Ultrasonography, Mammary
PubMed: 38942459
DOI: 10.3348/kjr.2024.0049 -
Korean Journal of Radiology Jul 2024In Korea, radiology has been positioned towards the early adoption of artificial intelligence-based software as medical devices (AI-SaMDs); however, little is known...
OBJECTIVE
In Korea, radiology has been positioned towards the early adoption of artificial intelligence-based software as medical devices (AI-SaMDs); however, little is known about the current usage, implementation, and future needs of AI-SaMDs. We surveyed the current trends and expectations for AI-SaMDs among members of the Korean Society of Radiology (KSR).
MATERIALS AND METHODS
An anonymous and voluntary online survey was open to all KSR members between April 17 and May 15, 2023. The survey was focused on the experiences of using AI-SaMDs, patterns of usage, levels of satisfaction, and expectations regarding the use of AI-SaMDs, including the roles of the industry, government, and KSR regarding the clinical use of AI-SaMDs.
RESULTS
Among the 370 respondents (response rate: 7.7% [370/4792]; 340 board-certified radiologists; 210 from academic institutions), 60.3% (223/370) had experience using AI-SaMDs. The two most common use-case of AI-SaMDs among the respondents were lesion detection (82.1%, 183/223), lesion diagnosis/classification (55.2%, 123/223), with the target imaging modalities being plain radiography (62.3%, 139/223), CT (42.6%, 95/223), mammography (29.1%, 65/223), and MRI (28.7%, 64/223). Most users were satisfied with AI-SaMDs (67.6% [115/170, for improvement of patient management] to 85.1% [189/222, for performance]). Regarding the expansion of clinical applications, most respondents expressed a preference for AI-SaMDs to assist in detection/diagnosis (77.0%, 285/370) and to perform automated measurement/quantification (63.5%, 235/370). Most respondents indicated that future development of AI-SaMDs should focus on improving practice efficiency (81.9%, 303/370) and quality (71.4%, 264/370). Overall, 91.9% of the respondents (340/370) agreed that there is a need for education or guidelines driven by the KSR regarding the use of AI-SaMDs.
CONCLUSION
The penetration rate of AI-SaMDs in clinical practice and the corresponding satisfaction levels were high among members of the KSR. Most AI-SaMDs have been used for lesion detection, diagnosis, and classification. Most respondents requested KSR-driven education or guidelines on the use of AI-SaMDs.
Topics: Humans; Republic of Korea; Artificial Intelligence; Surveys and Questionnaires; Societies, Medical; Radiology; Software
PubMed: 38942455
DOI: 10.3348/kjr.2023.1246 -
Korean Journal of Radiology Jul 2024Artificial intelligence (AI) is rapidly gaining recognition in the radiology domain as a greater number of radiologists are becoming AI-literate. However, the adoption... (Review)
Review
Position Statements of the Emerging Trends Committee of the Asian Oceanian Society of Radiology on the Adoption and Implementation of Artificial Intelligence for Radiology.
Artificial intelligence (AI) is rapidly gaining recognition in the radiology domain as a greater number of radiologists are becoming AI-literate. However, the adoption and implementation of AI solutions in clinical settings have been slow, with points of contention. A group of AI users comprising mainly clinical radiologists across various Asian countries, including India, Japan, Malaysia, Singapore, Taiwan, Thailand, and Uzbekistan, formed the working group. This study aimed to draft position statements regarding the application and clinical deployment of AI in radiology. The primary aim is to raise awareness among the general public, promote professional interest and discussion, clarify ethical considerations when implementing AI technology, and engage the radiology profession in the ever-changing clinical practice. These position statements highlight pertinent issues that need to be addressed between care providers and care recipients. More importantly, this will help legalize the use of non-human instruments in clinical deployment without compromising ethical considerations, decision-making precision, and clinical professional standards. We base our study on four main principles of medical care-respect for patient autonomy, beneficence, non-maleficence, and justice.
Topics: Artificial Intelligence; Humans; Radiology; Asia; Societies, Medical
PubMed: 38942454
DOI: 10.3348/kjr.2024.0419 -
Annals of Vascular Surgery Jun 2024Endovascular aortic repair requires extensive preoperative, intraoperative, and postoperative imaging for planning, surveillance, and detection of endo-leaks. There have... (Review)
Review
OBJECTIVES
Endovascular aortic repair requires extensive preoperative, intraoperative, and postoperative imaging for planning, surveillance, and detection of endo-leaks. There have been many advancements in imaging modalities to achieve this purpose. This review discussed different imaging modalities used at different stages of treatment of complex endovascular aortic repair.
METHODS
We conducted a literature review of all the imaging modalities utilized in endovascular aortic repair by searching various databases.
RESULTS
Pre-operative techniques include analysis of images obtained via modified central line using analysis software and intravascular ultrasound. Fusion imaging, CO2 angiography, intravascular ultrasound, and Fiber Optic RealShape technology have been crucial in obtaining real-time imaging for the detection of endo-leaks during operative procedures. Conventional imaging modalities like CT Angiography and MR Angiography are still employed for post-operative surveillance along with computational fluid dynamics and contrast-enhanced ultrasound. The advancements in artificial intelligence have been the breakthrough in developing robust imaging applications.
CONCLUSIONS
This review explains the advantages, disadvantages, and side-effect profile of the abovementioned imaging modalities.
PubMed: 38942370
DOI: 10.1016/j.avsg.2024.06.003 -
Gastrointestinal Endoscopy Jun 2024Computer-aided diagnosis (CADx) for optical diagnosis of colorectal polyps is thoroughly investigated. However, studies on human-artificial intelligence (AI) interaction...
BACKGROUND AND AIMS
Computer-aided diagnosis (CADx) for optical diagnosis of colorectal polyps is thoroughly investigated. However, studies on human-artificial intelligence (AI) interaction are lacking. Aim was to investigate endoscopists' trust in CADx by evaluating whether communicating a calibrated algorithm confidence improved trust.
METHODS
Endoscopists optically diagnosed 60 colorectal polyps. Initially, endoscopists diagnosed the polyps without CADx assistance (initial diagnosis). Immediately afterwards, the same polyp was again shown with CADx prediction; either only a prediction (benign or pre-malignant) or a prediction accompanied by a calibrated confidence score (0-100). A confidence score of 0 indicated a benign prediction, 100 a (pre-)malignant prediction. In half of the polyps CADx was mandatory, for the other half CADx was optional. After reviewing the CADx prediction, endoscopists made a final diagnosis. Histopathology was used as gold standard. Endoscopists' trust in CADx was measured as CADx prediction utilization; the willingness to follow CADx predictions when the endoscopists initially disagreed with the CADx prediction.
RESULTS
Twenty-three endoscopists participated. Presenting CADx predictions increased the endoscopists' diagnostic accuracy (69.3% initial vs 76.6% final diagnosis, p<0.001). The CADx prediction was utilized in 36.5% (n=183/501) disagreements. Adding a confidence score led to a lower CADx prediction utilization, except when the confidence score surpassed 60. A mandatory CADx decreased CADx prediction utilization compared to an optional CADx. Appropriate trust, utilizing correct or disregarding incorrect CADx predictions was 48.7% (n=244/501).
CONCLUSIONS
Appropriate trust was common and CADx prediction utilization was highest for the optional CADx without confidence scores. These results express the importance of a better understanding of human-AI interaction.
PubMed: 38942330
DOI: 10.1016/j.gie.2024.06.029