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Zhao J, Long Y, Li S, Li X, Zhang Y, Hu J, Han L, Ren L. Use of artificial intelligence algorithms to analyse systemic sclerosis-interstitial lung disease imaging features. Rheumatol Int 2024; 44:2027-2041. [PMID: 39207588 PMCID: PMC11393027 DOI: 10.1007/s00296-024-05681-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2024] [Accepted: 08/04/2024] [Indexed: 09/04/2024]
Abstract
The use of artificial intelligence (AI) in high-resolution computed tomography (HRCT) for diagnosing systemic sclerosis-associated interstitial lung disease (SSc-ILD) is relatively limited. This study aimed to analyse lung HRCT images of patients with systemic sclerosis with interstitial lung disease (SSc-ILD) using artificial intelligence (AI), conduct correlation analysis with clinical manifestations and prognosis, and explore the features and prognosis of SSc-ILD. Overall, 72 lung HRCT images and clinical data of 58 patients with SSC-ILD were collected. ILD lesion type, location, and volume on HRCT images were identified and evaluated using AI. The imaging characteristics of diffuse SSC (dSSc)-ILD and limited SSc-ILD (lSSc-ILD) were statistically analysed. Furthermore, the correlations between lesion type, clinical indicators, and prognosis were investigated. dSSc and lSSc were more prevalent in patients with a disease duration of < 1 and ≥ 5 years, respectively. SSc-ILD mainly comprises non-specific interstitial pneumonia (NSIP), usual interstitial pneumonia (UIP), and unclassifiable idiopathic interstitial pneumonia. HRCT reveals various lesion types in the early stages of the disease, with an increase in the number of lesion types as the disease progresses. Lesions appearing as grid, ground-glass, and nodular shadows were dispersed throughout both lungs, while those appearing as consolidation shadows and honeycomb were distributed across the lungs. Ground-glass opacity lesion type was absent on HRCT images of patients with SSc-ILD and pulmonary hypertension. This study showed that AI can efficiently analyse imaging characteristics of SSc-ILD, demonstrating its potential to learn from complex images with high generalisation ability.
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Affiliation(s)
- Jing Zhao
- Department of Rheumatology, People's Hospital of Xiangxi Tujia and Miao Autonomous Prefecture (The First Affiliated Hospital of Jishou University), Intersection of Shiji Avenue and Jianxin Road, Jishou, 416000, Hunan, People's Republic of China
| | - Ying Long
- Department of Rheumatology, Xiangya Hospital of Central South University, Changsha, People's Republic of China
- Provincial Clinical Research Center for Rheumatic and Immunologic Diseases, Xiangya Hospital of Central South University, Changsha, People's Republic of China
| | - Shengtao Li
- Department of Urology, People's Hospital of Xiangxi Tujia and Miao Autonomous Prefecture (The First Affiliated Hospital of Jishou University), Jishou, 416000, Hunan, People's Republic of China
| | - Xiaozhen Li
- Department of Rheumatology, People's Hospital of Xiangxi Tujia and Miao Autonomous Prefecture (The First Affiliated Hospital of Jishou University), Intersection of Shiji Avenue and Jianxin Road, Jishou, 416000, Hunan, People's Republic of China
| | - Yi Zhang
- Department of Rheumatology, People's Hospital of Xiangxi Tujia and Miao Autonomous Prefecture (The First Affiliated Hospital of Jishou University), Intersection of Shiji Avenue and Jianxin Road, Jishou, 416000, Hunan, People's Republic of China
| | - Juan Hu
- Department of Rheumatology, People's Hospital of Xiangxi Tujia and Miao Autonomous Prefecture (The First Affiliated Hospital of Jishou University), Intersection of Shiji Avenue and Jianxin Road, Jishou, 416000, Hunan, People's Republic of China
| | - Lin Han
- Department of Imaging, People's Hospital of Xiangxi Tujia and Miao Autonomous Prefecture (The First Affiliated Hospital of Jishou University), Jishou, 416000, Hunan, People's Republic of China
| | - Li Ren
- Department of Rheumatology, People's Hospital of Xiangxi Tujia and Miao Autonomous Prefecture (The First Affiliated Hospital of Jishou University), Intersection of Shiji Avenue and Jianxin Road, Jishou, 416000, Hunan, People's Republic of China.
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2
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Huang W, Li C, Zhou HY, Yang H, Liu J, Liang Y, Zheng H, Zhang S, Wang S. Enhancing representation in radiography-reports foundation model: a granular alignment algorithm using masked contrastive learning. Nat Commun 2024; 15:7620. [PMID: 39223122 PMCID: PMC11369198 DOI: 10.1038/s41467-024-51749-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 08/15/2024] [Indexed: 09/04/2024] Open
Abstract
Recently, multi-modal vision-language foundation models have gained significant attention in the medical field. While these models offer great opportunities, they still face crucial challenges, such as the requirement for fine-grained knowledge understanding in computer-aided diagnosis and the capability of utilizing very limited or even no task-specific labeled data in real-world clinical applications. In this study, we present MaCo, a masked contrastive chest X-ray foundation model that tackles these challenges. MaCo explores masked contrastive learning to simultaneously achieve fine-grained image understanding and zero-shot learning for a variety of medical imaging tasks. It designs a correlation weighting mechanism to adjust the correlation between masked chest X-ray image patches and their corresponding reports, thereby enhancing the model's representation learning capabilities. To evaluate the performance of MaCo, we conducted extensive experiments using 6 well-known open-source X-ray datasets. The experimental results demonstrate the superiority of MaCo over 10 state-of-the-art approaches across tasks such as classification, segmentation, detection, and phrase grounding. These findings highlight the significant potential of MaCo in advancing a wide range of medical image analysis tasks.
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Affiliation(s)
- Weijian Huang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Pengcheng Laboratory, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Cheng Li
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Hong-Yu Zhou
- Department of Biomedical Informatics, Harvard Medical University, Boston, MA, USA
| | - Hao Yang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Pengcheng Laboratory, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jiarun Liu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Pengcheng Laboratory, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | | | - Hairong Zheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Shaoting Zhang
- Qingyuan Research Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Shanshan Wang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
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3
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Spoladore D, Stella F, Tosi M, Lorenzini EC, Bettini C. A knowledge-based decision support system to support family doctors in personalizing type-2 diabetes mellitus medical nutrition therapy. Comput Biol Med 2024; 180:109001. [PMID: 39126791 DOI: 10.1016/j.compbiomed.2024.109001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Revised: 07/12/2024] [Accepted: 08/05/2024] [Indexed: 08/12/2024]
Abstract
BACKGROUND Type-2 Diabetes Mellitus (T2D) is a growing concern worldwide, and family doctors are called to help diabetic patients manage this chronic disease, also with Medical Nutrition Therapy (MNT). However, MNT for Diabetes is usually standardized, while it would be much more effective if tailored to the patient. There is a gap in patient-tailored MNT which, if addressed, could support family doctors in delivering effective recommendations. In this context, decision support systems (DSSs) are valuable tools for physicians to support MNT for T2D patients - as long as DSSs are transparent to humans in their decision-making process. Indeed, the lack of transparency in data-driven DSS might hinder their adoption in clinical practice, thus leaving family physicians to adopt general nutrition guidelines provided by the national healthcare systems. METHOD This work presents a prototypical ontology-based clinical Decision Support System (OnT2D- DSS) aimed at assisting general practice doctors in managing T2D patients, specifically in creating a tailored dietary plan, leveraging clinical expert knowledge. OnT2D-DSS exploits clinical expert knowledge formalized as a domain ontology to identify a patient's phenotype and potential comorbidities, providing personalized MNT recommendations for macro- and micro-nutrient intake. The system can be accessed via a prototypical interface. RESULTS Two preliminary experiments are conducted to assess both the quality and correctness of the inferences provided by the system and the usability and acceptance of the OnT2D-DSS (conducted with nutrition experts and family doctors, respectively). CONCLUSIONS Overall, the system is deemed accurate by the nutrition experts and valuable by the family doctors, with minor suggestions for future improvements collected during the experiments.
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Affiliation(s)
- Daniele Spoladore
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA), National Research Council (Cnr), Lecco, Italy.
| | - Francesco Stella
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA), National Research Council (Cnr), Lecco, Italy; Department of Computer Science, University of Milan, Milan, Italy.
| | - Martina Tosi
- Department of Health Sciences, University of Milan, Milan, Italy.
| | | | - Claudio Bettini
- Department of Computer Science, University of Milan, Milan, Italy.
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Kim KA, Kim H, Ha EJ, Yoon BC, Kim DJ. Artificial Intelligence-Enhanced Neurocritical Care for Traumatic Brain Injury : Past, Present and Future. J Korean Neurosurg Soc 2024; 67:493-509. [PMID: 38186369 PMCID: PMC11375068 DOI: 10.3340/jkns.2023.0195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 01/04/2024] [Indexed: 01/09/2024] Open
Abstract
In neurointensive care units (NICUs), particularly in cases involving traumatic brain injury (TBI), swift and accurate decision-making is critical because of rapidly changing patient conditions and the risk of secondary brain injury. The use of artificial intelligence (AI) in NICU can enhance clinical decision support and provide valuable assistance in these complex scenarios. This article aims to provide a comprehensive review of the current status and future prospects of AI utilization in the NICU, along with the challenges that must be overcome to realize this. Presently, the primary application of AI in NICU is outcome prediction through the analysis of preadmission and high-resolution data during admission. Recent applications include augmented neuromonitoring via signal quality control and real-time event prediction. In addition, AI can integrate data gathered from various measures and support minimally invasive neuromonitoring to increase patient safety. However, despite the recent surge in AI adoption within the NICU, the majority of AI applications have been limited to simple classification tasks, thus leaving the true potential of AI largely untapped. Emerging AI technologies, such as generalist medical AI and digital twins, harbor immense potential for enhancing advanced neurocritical care through broader AI applications. If challenges such as acquiring high-quality data and ethical issues are overcome, these new AI technologies can be clinically utilized in the actual NICU environment. Emphasizing the need for continuous research and development to maximize the potential of AI in the NICU, we anticipate that this will further enhance the efficiency and accuracy of TBI treatment within the NICU.
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Affiliation(s)
- Kyung Ah Kim
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea
| | - Hakseung Kim
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea
| | - Eun Jin Ha
- Department of Critical Care Medicine, Seoul National University Hospital, Seoul, Korea
| | - Byung C Yoon
- Department of Radiology, Stanford University School of Medicine, VA Palo Alto Heath Care System, Palo Alto, CA, USA
| | - Dong-Joo Kim
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea
- Department of Neurology, Korea University College of Medicine, Seoul, Korea
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Reith TP, D'Alessandro DM, D'Alessandro MP. Capability of multimodal large language models to interpret pediatric radiological images. Pediatr Radiol 2024; 54:1729-1737. [PMID: 39133401 DOI: 10.1007/s00247-024-06025-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2024] [Revised: 07/31/2024] [Accepted: 08/01/2024] [Indexed: 08/13/2024]
Abstract
BACKGROUND There is a dearth of artificial intelligence (AI) development and research dedicated to pediatric radiology. The newest iterations of large language models (LLMs) like ChatGPT can process image and video input in addition to text. They are thus theoretically capable of providing impressions of input radiological images. OBJECTIVE To assess the ability of multimodal LLMs to interpret pediatric radiological images. MATERIALS AND METHODS Thirty medically significant cases were collected and submitted to GPT-4 (OpenAI, San Francisco, CA), Gemini 1.5 Pro (Google, Mountain View, CA), and Claude 3 Opus (Anthropic, San Francisco, CA) with a short history for a total of 90 images. AI responses were recorded and independently assessed for accuracy by a resident and attending physician. 95% confidence intervals were determined using the adjusted Wald method. RESULTS Overall, the models correctly diagnosed 27.8% (25/90) of images (95% CI=19.5-37.8%), were partially correct for 13.3% (12/90) of images (95% CI=2.7-26.4%), and were incorrect for 58.9% (53/90) of images (95% CI=48.6-68.5%). CONCLUSION Multimodal LLMs are not yet capable of interpreting pediatric radiological images.
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Affiliation(s)
- Thomas P Reith
- Department of Radiology, University of Iowa Hospitals and Clinics, Iowa City, IA, 52242, USA.
| | - Donna M D'Alessandro
- Department of Pediatrics, University of Iowa Hospitals and Clinics, Iowa City, IA, 52242, USA
| | - Michael P D'Alessandro
- Department of Radiology, University of Iowa Hospitals and Clinics, Iowa City, IA, 52242, USA
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Peters S, Kellermann G, Watkinson J, Gärtner F, Huhndorf M, Stürner K, Jansen O, Larsen N. AI supported detection of cerebral multiple sclerosis lesions decreases radiologic reporting times. Eur J Radiol 2024; 178:111638. [PMID: 39067268 DOI: 10.1016/j.ejrad.2024.111638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Revised: 06/10/2024] [Accepted: 07/16/2024] [Indexed: 07/30/2024]
Abstract
PURPOSE Multiple Sclerosis (MS) is a common autoimmune disease of the central nervous system. MRI plays a crucial role in diagnosing as well as in disease and treatment monitoring. Therefore, evaluation of cerebral MRI of MS patients is part of daily clinical routine. A growing number of companies offer commercial software to support the reporting with automated lesion detection. Aim of this study was to evaluate the effect of such a software with AI supported lesion detection to the radiologic reporting. METHOD Four radiologist each counted MS-lesions in MRI examinations of 50 patients separated by the locations periventricular, cortical/juxtacortical, infrantentorial and unspecific white matter. After at least six weeks they repeated the evaluation, this time using the AI based software mdbrain for lesion detection. In both settings the required time was documented. Further the radiologists evaluated follow-up MRI of 50 MS-patients concerning new and enlarging lesions in the same manner. RESULTS To determine the lesion-load the average reporting time decreased from 286.85 sec to 196.34 sec (p > 0.001). For the evaluation of the follow-up images the reporting time dropped from 196.17 sec to 120.87 sec (p < 0.001). The interrater reliabilities showed no significant differences for the determination of lesion-load (0.83 without vs. 0.8 with software support) and for the detection of new/enlarged lesions (0.92 without vs. 0.82 with software support). CONCLUSION For the evaluation of MR images of MS patients, an AI-based support for image-interpretation can significantly decreases reporting times.
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Affiliation(s)
- Sönke Peters
- Department of Radiology and Neuroradiology, University Hospital of Schleswig-Holstein, Campus Kiel, Germany.
| | - Gesa Kellermann
- Department of Radiology, Bundeswehr Hospital Hamburg, Germany
| | - Joe Watkinson
- Department of Radiology and Neuroradiology, University Hospital of Schleswig-Holstein, Campus Kiel, Germany
| | - Friederike Gärtner
- Department of Radiology and Neuroradiology, University Hospital of Schleswig-Holstein, Campus Kiel, Germany
| | - Monika Huhndorf
- Department of Radiology and Neuroradiology, University Hospital of Schleswig-Holstein, Campus Kiel, Germany
| | - Klarissa Stürner
- Department of Neurology, University Hospital of Schleswig-Holstein, Campus Kiel, Germany
| | - Olav Jansen
- Department of Radiology and Neuroradiology, University Hospital of Schleswig-Holstein, Campus Kiel, Germany
| | - Naomi Larsen
- Department of Radiology and Neuroradiology, University Hospital of Schleswig-Holstein, Campus Kiel, Germany
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Yan TD, Jalal S, Harris A. Value-Based Radiology in Canada: Reducing Low-Value Care and Improving System Efficiency. Can Assoc Radiol J 2024:8465371241277110. [PMID: 39219178 DOI: 10.1177/08465371241277110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/04/2024] Open
Abstract
Radiology departments are increasingly tasked with managing growing demands on services including long waitlists for scanning and interventional procedures, human health resource shortages, equipment needs, and challenges incorporating advanced imaging solutions. The burden of system inefficiencies and the overuse of "low-value" imaging causes downstream impact on patients at the individual level, the economy and healthcare system at the societal level, and planetary health at an overarching level. Low value imaging includes those performed for an inappropriate clinical indication, with little to no value to the management of the patient, and resulting in healthcare resource waste; it is estimated that up to a quarter of advanced imaging studies in Canada meet this criterion. Strategies to reduce low-value imaging include the development and use of referral guidelines, use of appropriateness criteria, optimization of existing protocols, and integration of clinical decision support tools into the ordering provider's workflow. Additional means of optimizing system efficiency such as centralized intake models, improved access to electronic medical records and outside imaging, enhanced communication with patients and referrers, and the utilization of artificial intelligence will further increase the value of radiology provided to patients and care providers.
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Affiliation(s)
- Tyler D Yan
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Sabeena Jalal
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Alison Harris
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
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Salvagno M, Cassai AD, Zorzi S, Zaccarelli M, Pasetto M, Sterchele ED, Chumachenko D, Gerli AG, Azamfirei R, Taccone FS. The state of artificial intelligence in medical research: A survey of corresponding authors from top medical journals. PLoS One 2024; 19:e0309208. [PMID: 39178224 PMCID: PMC11343420 DOI: 10.1371/journal.pone.0309208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 08/08/2024] [Indexed: 08/25/2024] Open
Abstract
Natural Language Processing (NLP) is a subset of artificial intelligence that enables machines to understand and respond to human language through Large Language Models (LLMs)‥ These models have diverse applications in fields such as medical research, scientific writing, and publishing, but concerns such as hallucination, ethical issues, bias, and cybersecurity need to be addressed. To understand the scientific community's understanding and perspective on the role of Artificial Intelligence (AI) in research and authorship, a survey was designed for corresponding authors in top medical journals. An online survey was conducted from July 13th, 2023, to September 1st, 2023, using the SurveyMonkey web instrument, and the population of interest were corresponding authors who published in 2022 in the 15 highest-impact medical journals, as ranked by the Journal Citation Report. The survey link has been sent to all the identified corresponding authors by mail. A total of 266 authors answered, and 236 entered the final analysis. Most of the researchers (40.6%) reported having moderate familiarity with artificial intelligence, while a minority (4.4%) had no associated knowledge. Furthermore, the vast majority (79.0%) believe that artificial intelligence will play a major role in the future of research. Of note, no correlation between academic metrics and artificial intelligence knowledge or confidence was found. The results indicate that although researchers have varying degrees of familiarity with artificial intelligence, its use in scientific research is still in its early phases. Despite lacking formal AI training, many scholars publishing in high-impact journals have started integrating such technologies into their projects, including rephrasing, translation, and proofreading tasks. Efforts should focus on providing training for their effective use, establishing guidelines by journal editors, and creating software applications that bundle multiple integrated tools into a single platform.
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Affiliation(s)
- Michele Salvagno
- Department of Intensive Care, Hôpital Universitaire de Bruxelles (HUB), Brussels, Belgium
| | - Alessandro De Cassai
- Sant’Antonio Anesthesia and Intensive Care Unit, University Hospital of Padua, Padua, Italy
| | - Stefano Zorzi
- Department of Intensive Care, Hôpital Universitaire de Bruxelles (HUB), Brussels, Belgium
| | - Mario Zaccarelli
- Department of Intensive Care, Hôpital Universitaire de Bruxelles (HUB), Brussels, Belgium
| | - Marco Pasetto
- Department of Intensive Care, Hôpital Universitaire de Bruxelles (HUB), Brussels, Belgium
| | - Elda Diletta Sterchele
- Department of Intensive Care, Hôpital Universitaire de Bruxelles (HUB), Brussels, Belgium
| | - Dmytro Chumachenko
- Department of Mathematical Modelling and Artificial Intelligence, National Aerospace University “Kharkiv Aviation Institute”, Kharkiv, Ukraine
- Ubiquitous Health Technologies Lab, University of Waterloo, Waterloo, Canada
| | - Alberto Giovanni Gerli
- Department of Clinical Sciences and Community Health, Università degli Studi di Milano, Milan, Italy
| | - Razvan Azamfirei
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
| | - Fabio Silvio Taccone
- Department of Intensive Care, Hôpital Universitaire de Bruxelles (HUB), Brussels, Belgium
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Lee S, Kim EK, Han K, Ryu L, Lee EH, Shin HJ. Factors for increasing positive predictive value of pneumothorax detection on chest radiographs using artificial intelligence. Sci Rep 2024; 14:19624. [PMID: 39179744 PMCID: PMC11343866 DOI: 10.1038/s41598-024-70780-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 08/21/2024] [Indexed: 08/26/2024] Open
Abstract
This study evaluated the positive predictive value (PPV) of artificial intelligence (AI) in detecting pneumothorax on chest radiographs (CXRs) and its affecting factors. Patients determined to have pneumothorax on CXR by a commercial AI software from March to December 2021 were included retrospectively. The PPV was evaluated according to the true-positive (TP) and false-positive (FP) diagnosis determined by radiologists. To know the factors that might influence the results, logistic regression with generalized estimating equation was used. Among a total of 87,658 CXRs, 308 CXRs with 331 pneumothoraces from 283 patients were finally included. The overall PPV of AI about pneumothorax was 41.1% (TF:FP = 136:195). The PA view (odds ratio [OR], 29.837; 95% confidence interval [CI], 15.062-59.107), high abnormality score (OR, 1.081; 95% CI, 1.066-1.097), large amount of pneumothorax (OR, 1.005; 95% CI, 1.003-1.007), presence of ipsilateral atelectasis (OR, 3.508; 95% CI, 1.509-8.156) and a small amount of ipsilateral pleural effusion (OR, 5.277; 95% CI, 2.55-10.919) had significant effects on the increasing PPV. Therefore, PPV for pneumothorax diagnosis using AI can vary based on patients' factors, image-acquisition protocols, and the presence of concurrent lesions on CXR.
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Affiliation(s)
- Seungsoo Lee
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yongin Severance Hospital, Yonsei University College of Medicine, 363, Dongbaekjukjeon-Daero, Giheung-Gu, Yongin-Si, Gyeonggi-Do, 16995, South Korea
| | - Eun-Kyung Kim
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yongin Severance Hospital, Yonsei University College of Medicine, 363, Dongbaekjukjeon-Daero, Giheung-Gu, Yongin-Si, Gyeonggi-Do, 16995, South Korea
| | - Kyunghwa Han
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-Gu, Seoul, 03722, South Korea
| | - Leeha Ryu
- Department of Biostatistics and Computing, Yonsei University Graduate School, 50-1 Yonsei-Ro, Seodaemun-Gu, Seoul, 03722, South Korea
| | - Eun Hye Lee
- Division of Pulmonology, Allergy and Critical Care Medicine, Department of Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, 363, Dongbaekjukjeon-Daero, Giheung-Gu, Yongin-Si, Gyeonggi-Do, 16995, South Korea
| | - Hyun Joo Shin
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yongin Severance Hospital, Yonsei University College of Medicine, 363, Dongbaekjukjeon-Daero, Giheung-Gu, Yongin-Si, Gyeonggi-Do, 16995, South Korea.
- Center for Digital Health, Yongin Severance Hospital, Yonsei University College of Medicine, 363, Dongbaekjukjeon-Daero, Giheung-Gu, Yongin-Si, Gyeonggi-Do, 16995, South Korea.
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Yalcinkaya DM, Youssef K, Heydari B, Wei J, Merz NB, Judd R, Dharmakumar R, Simonetti OP, Weinsaft JW, Raman SV, Sharif B. Improved Robustness for Deep Learning-based Segmentation of Multi-Center Myocardial Perfusion MRI Datasets Using Data Adaptive Uncertainty-guided Space-time Analysis. J Cardiovasc Magn Reson 2024:101082. [PMID: 39142567 DOI: 10.1016/j.jocmr.2024.101082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 06/14/2024] [Accepted: 08/07/2024] [Indexed: 08/16/2024] Open
Abstract
BACKGROUND Fully automatic analysis of myocardial perfusion MRI datasets enables rapid and objective reporting of stress/rest studies in patients with suspected ischemic heart disease. Developing deep learning techniques that can analyze multi-center datasets despite limited training data and variations in software (pulse sequence) and hardware (scanner vendor) is an ongoing challenge. METHODS Datasets from 3 medical centers acquired at 3T (n = 150 subjects; 21,150 first-pass images) were included: an internal dataset (inD; n = 95) and two external datasets (exDs; n = 55) used for evaluating the robustness of the trained deep neural network (DNN) models against differences in pulse sequence (exD-1) and scanner vendor (exD-2). A subset of inD (n = 85) was used for training/validation of a pool of DNNs for segmentation, all using the same spatiotemporal U-Net architecture and hyperparameters but with different parameter initializations. We employed a space-time sliding-patch analysis approach that automatically yields a pixel-wise "uncertainty map" as a byproduct of the segmentation process. In our approach, dubbed Data Adaptive Uncertainty-Guided Space-time (DAUGS) analysis, a given test case is segmented by all members of the DNN pool and the resulting uncertainty maps are leveraged to automatically select the "best" one among the pool of solutions. For comparison, we also trained a DNN using the established approach with the same settings (hyperparameters, data augmentation, etc.). RESULTS The proposed DAUGS analysis approach performed similarly to the established approach on the internal dataset (Dice score for the testing subset of inD: 0.896 ± 0.050 vs. 0.890 ± 0.049; p = n.s.) whereas it significantly outperformed on the external datasets (Dice for exD-1: 0.885 ± 0.040 vs. 0.849 ± 0.065, p < 0.005; Dice for exD-2: 0.811 ± 0.070 vs. 0.728 ± 0.149, p < 0.005). Moreover, the number of image series with "failed" segmentation (defined as having myocardial contours that include bloodpool or are noncontiguous in ≥1 segment) was significantly lower for the proposed vs. the established approach (4.3% vs. 17.1%, p < 0.0005). CONCLUSIONS The proposed DAUGS analysis approach has the potential to improve the robustness of deep learning methods for segmentation of multi-center stress perfusion datasets with variations in the choice of pulse sequence, site location or scanner vendor.
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Affiliation(s)
- Dilek M Yalcinkaya
- Laboratory for Translational Imaging of Microcirculation, Indiana University School of Medicine, Indianapolis, IN, USA; Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA
| | - Khalid Youssef
- Laboratory for Translational Imaging of Microcirculation, Indiana University School of Medicine, Indianapolis, IN, USA; Krannert Cardiovascular Research Center, Dept. of Medicine, Indiana Univ. School of Medicine, Indianapolis, IN, USA
| | - Bobak Heydari
- Stephenson Cardiac Imaging Centre, Department of Cardiac Sciences, University of Calgary, Alberta, Canada
| | - Janet Wei
- Barbra Streisand Women's Heart Center, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Noel Bairey Merz
- Barbra Streisand Women's Heart Center, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Robert Judd
- Division of Cardiology, Department of Medicine, Duke University, Durham, NC, USA
| | - Rohan Dharmakumar
- Krannert Cardiovascular Research Center, Dept. of Medicine, Indiana Univ. School of Medicine, Indianapolis, IN, USA; OhioHealth, Columbus, OH, USA
| | - Orlando P Simonetti
- Department of Medicine, Davis Heart and Lung Research Institute, The Ohio State University, Columbus, OH, USA
| | - Jonathan W Weinsaft
- Division of Cardiology at NY Presbyterian Hospital, Weill Cornell Medical Center, New York, NY, USA
| | - Subha V Raman
- Krannert Cardiovascular Research Center, Dept. of Medicine, Indiana Univ. School of Medicine, Indianapolis, IN, USA; OhioHealth, Columbus, OH, USA
| | - Behzad Sharif
- Laboratory for Translational Imaging of Microcirculation, Indiana University School of Medicine, Indianapolis, IN, USA; Krannert Cardiovascular Research Center, Dept. of Medicine, Indiana Univ. School of Medicine, Indianapolis, IN, USA; OhioHealth, Columbus, OH, USA.
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Plesner LL, Müller FC, Brejnebøl MW, Krag CH, Laustrup LC, Rasmussen F, Nielsen OW, Boesen M, Andersen MB. Using AI to Identify Unremarkable Chest Radiographs for Automatic Reporting. Radiology 2024; 312:e240272. [PMID: 39162628 DOI: 10.1148/radiol.240272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/21/2024]
Abstract
Background Radiology practices have a high volume of unremarkable chest radiographs and artificial intelligence (AI) could possibly improve workflow by providing an automatic report. Purpose To estimate the proportion of unremarkable chest radiographs, where AI can correctly exclude pathology (ie, specificity) without increasing diagnostic errors. Materials and Methods In this retrospective study, consecutive chest radiographs in unique adult patients (≥18 years of age) were obtained January 1-12, 2020, at four Danish hospitals. Exclusion criteria included insufficient radiology reports or AI output error. Two thoracic radiologists, who were blinded to AI output, labeled chest radiographs as "remarkable" or "unremarkable" based on predefined unremarkable findings (reference standard). Radiology reports were classified similarly. A commercial AI tool was adapted to output a chest radiograph "remarkableness" probability, which was used to calculate specificity at different AI sensitivities. Chest radiographs with missed findings by AI and/or the radiology report were graded by one thoracic radiologist as critical, clinically significant, or clinically insignificant. Paired proportions were compared using the McNemar test. Results A total of 1961 patients were included (median age, 72 years [IQR, 58-81 years]; 993 female), with one chest radiograph per patient. The reference standard labeled 1231 of 1961 chest radiographs (62.8%) as remarkable and 730 of 1961 (37.2%) as unremarkable. At 99.9%, 99.0%, and 98.0% sensitivity, the AI had a specificity of 24.5% (179 of 730 radiographs [95% CI: 21, 28]), 47.1% (344 of 730 radiographs [95% CI: 43, 51]), and 52.7% (385 of 730 radiographs [95% CI: 49, 56]), respectively. With the AI fixed to have a similar sensitivity as radiology reports (87.2%), the missed findings of AI and reports had 2.2% (27 of 1231 radiographs) and 1.1% (14 of 1231 radiographs) classified as critical (P = .01), 4.1% (51 of 1231 radiographs) and 3.6% (44 of 1231 radiographs) classified as clinically significant (P = .46), and 6.5% (80 of 1231) and 8.1% (100 of 1231) classified as clinically insignificant (P = .11), respectively. At sensitivities greater than or equal to 95.4%, the AI tool exhibited less than or equal to 1.1% critical misses. Conclusion A commercial AI tool used off-label could correctly exclude pathology in 24.5%-52.7% of all unremarkable chest radiographs at greater than or equal to 98% sensitivity. The AI had equal or lower rates of critical misses than radiology reports at sensitivities greater than or equal to 95.4%. These results should be confirmed in a prospective study. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Yoon and Hwang in this issue.
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Affiliation(s)
- Louis Lind Plesner
- From the Department of Radiology, Herlev and Gentofte Hospital, Borgmester Ib, Juuls vej 1 Herlev, Copenhagen 2730, Denmark (L.L.P., F.C.M., M.W.B., C.H.K., L.C.L., M.B.A.); Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark (L.L.P., M.W.B., C.H.K., M.B., M.B.A.); Radiological Artificial Intelligence Testcenter, RAIT.dk, Herlev, Denmark (L.L.P., F.C.M., M.W.B., C.H.K., M.B., M.B.A.); Department of Radiology, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark (M.W.B., M.B.); Department of Radiology, Aarhus University Hospital, Aarhus, Denmark (F.R.); and Department of Cardiology, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark (O.W.N.)
| | - Felix C Müller
- From the Department of Radiology, Herlev and Gentofte Hospital, Borgmester Ib, Juuls vej 1 Herlev, Copenhagen 2730, Denmark (L.L.P., F.C.M., M.W.B., C.H.K., L.C.L., M.B.A.); Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark (L.L.P., M.W.B., C.H.K., M.B., M.B.A.); Radiological Artificial Intelligence Testcenter, RAIT.dk, Herlev, Denmark (L.L.P., F.C.M., M.W.B., C.H.K., M.B., M.B.A.); Department of Radiology, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark (M.W.B., M.B.); Department of Radiology, Aarhus University Hospital, Aarhus, Denmark (F.R.); and Department of Cardiology, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark (O.W.N.)
| | - Mathias W Brejnebøl
- From the Department of Radiology, Herlev and Gentofte Hospital, Borgmester Ib, Juuls vej 1 Herlev, Copenhagen 2730, Denmark (L.L.P., F.C.M., M.W.B., C.H.K., L.C.L., M.B.A.); Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark (L.L.P., M.W.B., C.H.K., M.B., M.B.A.); Radiological Artificial Intelligence Testcenter, RAIT.dk, Herlev, Denmark (L.L.P., F.C.M., M.W.B., C.H.K., M.B., M.B.A.); Department of Radiology, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark (M.W.B., M.B.); Department of Radiology, Aarhus University Hospital, Aarhus, Denmark (F.R.); and Department of Cardiology, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark (O.W.N.)
| | - Christian Hedeager Krag
- From the Department of Radiology, Herlev and Gentofte Hospital, Borgmester Ib, Juuls vej 1 Herlev, Copenhagen 2730, Denmark (L.L.P., F.C.M., M.W.B., C.H.K., L.C.L., M.B.A.); Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark (L.L.P., M.W.B., C.H.K., M.B., M.B.A.); Radiological Artificial Intelligence Testcenter, RAIT.dk, Herlev, Denmark (L.L.P., F.C.M., M.W.B., C.H.K., M.B., M.B.A.); Department of Radiology, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark (M.W.B., M.B.); Department of Radiology, Aarhus University Hospital, Aarhus, Denmark (F.R.); and Department of Cardiology, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark (O.W.N.)
| | - Lene C Laustrup
- From the Department of Radiology, Herlev and Gentofte Hospital, Borgmester Ib, Juuls vej 1 Herlev, Copenhagen 2730, Denmark (L.L.P., F.C.M., M.W.B., C.H.K., L.C.L., M.B.A.); Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark (L.L.P., M.W.B., C.H.K., M.B., M.B.A.); Radiological Artificial Intelligence Testcenter, RAIT.dk, Herlev, Denmark (L.L.P., F.C.M., M.W.B., C.H.K., M.B., M.B.A.); Department of Radiology, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark (M.W.B., M.B.); Department of Radiology, Aarhus University Hospital, Aarhus, Denmark (F.R.); and Department of Cardiology, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark (O.W.N.)
| | - Finn Rasmussen
- From the Department of Radiology, Herlev and Gentofte Hospital, Borgmester Ib, Juuls vej 1 Herlev, Copenhagen 2730, Denmark (L.L.P., F.C.M., M.W.B., C.H.K., L.C.L., M.B.A.); Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark (L.L.P., M.W.B., C.H.K., M.B., M.B.A.); Radiological Artificial Intelligence Testcenter, RAIT.dk, Herlev, Denmark (L.L.P., F.C.M., M.W.B., C.H.K., M.B., M.B.A.); Department of Radiology, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark (M.W.B., M.B.); Department of Radiology, Aarhus University Hospital, Aarhus, Denmark (F.R.); and Department of Cardiology, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark (O.W.N.)
| | - Olav Wendelboe Nielsen
- From the Department of Radiology, Herlev and Gentofte Hospital, Borgmester Ib, Juuls vej 1 Herlev, Copenhagen 2730, Denmark (L.L.P., F.C.M., M.W.B., C.H.K., L.C.L., M.B.A.); Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark (L.L.P., M.W.B., C.H.K., M.B., M.B.A.); Radiological Artificial Intelligence Testcenter, RAIT.dk, Herlev, Denmark (L.L.P., F.C.M., M.W.B., C.H.K., M.B., M.B.A.); Department of Radiology, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark (M.W.B., M.B.); Department of Radiology, Aarhus University Hospital, Aarhus, Denmark (F.R.); and Department of Cardiology, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark (O.W.N.)
| | - Mikael Boesen
- From the Department of Radiology, Herlev and Gentofte Hospital, Borgmester Ib, Juuls vej 1 Herlev, Copenhagen 2730, Denmark (L.L.P., F.C.M., M.W.B., C.H.K., L.C.L., M.B.A.); Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark (L.L.P., M.W.B., C.H.K., M.B., M.B.A.); Radiological Artificial Intelligence Testcenter, RAIT.dk, Herlev, Denmark (L.L.P., F.C.M., M.W.B., C.H.K., M.B., M.B.A.); Department of Radiology, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark (M.W.B., M.B.); Department of Radiology, Aarhus University Hospital, Aarhus, Denmark (F.R.); and Department of Cardiology, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark (O.W.N.)
| | - Michael B Andersen
- From the Department of Radiology, Herlev and Gentofte Hospital, Borgmester Ib, Juuls vej 1 Herlev, Copenhagen 2730, Denmark (L.L.P., F.C.M., M.W.B., C.H.K., L.C.L., M.B.A.); Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark (L.L.P., M.W.B., C.H.K., M.B., M.B.A.); Radiological Artificial Intelligence Testcenter, RAIT.dk, Herlev, Denmark (L.L.P., F.C.M., M.W.B., C.H.K., M.B., M.B.A.); Department of Radiology, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark (M.W.B., M.B.); Department of Radiology, Aarhus University Hospital, Aarhus, Denmark (F.R.); and Department of Cardiology, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark (O.W.N.)
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Clausdorff Fiedler H, Prager R, Smith D, Wu D, Dave C, Tschirhart J, Wu B, Van Berlo B, Malthaner R, Arntfield R. Automated Real-Time Detection of Lung Sliding Using Artificial Intelligence: A Prospective Diagnostic Accuracy Study. Chest 2024; 166:362-370. [PMID: 38365174 DOI: 10.1016/j.chest.2024.02.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 02/04/2024] [Accepted: 02/09/2024] [Indexed: 02/18/2024] Open
Abstract
BACKGROUND Rapid evaluation for pneumothorax is a common clinical priority. Although lung ultrasound (LUS) often is used to assess for pneumothorax, its diagnostic accuracy varies based on patient and provider factors. To enhance the performance of LUS for pulmonary pathologic features, artificial intelligence (AI)-assisted imaging has been adopted; however, the diagnostic accuracy of AI-assisted LUS (AI-LUS) deployed in real time to diagnose pneumothorax remains unknown. RESEARCH QUESTION In patients with suspected pneumothorax, what is the real-time diagnostic accuracy of AI-LUS to recognize the absence of lung sliding? STUDY DESIGN AND METHODS We performed a prospective AI-assisted diagnostic accuracy study of AI-LUS to recognize the absence of lung sliding in a convenience sample of patients with suspected pneumothorax. After calibrating the model parameters and imaging settings for bedside deployment, we prospectively evaluated its diagnostic accuracy for lung sliding compared with a reference standard of expert consensus. RESULTS Two hundred forty-one lung sliding evaluations were derived from 62 patients. AI-LUS showed a sensitivity of 0.921 (95% CI, 0.792-0.973), specificity of 0.802 (95% CI, 0.735-0.856), area under the receiver operating characteristic curve of 0.885 (95% CI, 0.828-0.956), and accuracy of 0.824 (95% CI, 0.766-0.870) for the diagnosis of absent lung sliding. INTERPRETATION In this study, real-time AI-LUS showed high sensitivity and moderate specificity to identify the absence of lung sliding. Further research to improve model performance and optimize the integration of AI-LUS into existing diagnostic pathways is warranted.
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Affiliation(s)
| | - Ross Prager
- Division of Critical Care Medicine, Western University, London, ON, Canada
| | - Delaney Smith
- Lawson Health Research Institute, London, ON, Canada
| | - Derek Wu
- Lawson Health Research Institute, London, ON, Canada
| | - Chintan Dave
- Lawson Health Research Institute, London, ON, Canada
| | | | - Ben Wu
- Lawson Health Research Institute, London, ON, Canada
| | - Blake Van Berlo
- Faculty of Mathematics, University of Waterloo, Waterloo, ON, Canada
| | - Richard Malthaner
- Division of Thoracic Surgery, Western University, London, ON, Canada
| | - Robert Arntfield
- Division of Critical Care Medicine, Western University, London, ON, Canada
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Faierstein K, Fiman M, Loutati R, Rubin N, Manor U, Am-Shalom A, Cohen-Shelly M, Blank N, Lotan D, Zhao Q, Schwammenthal E, Klempfner R, Zimlichman E, Raanani E, Maor E. Artificial Intelligence Assessment of Biological Age From Transthoracic Echocardiography: Discrepancies with Chronologic Age Predict Significant Excess Mortality. J Am Soc Echocardiogr 2024; 37:725-735. [PMID: 38740271 DOI: 10.1016/j.echo.2024.04.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 04/29/2024] [Accepted: 04/30/2024] [Indexed: 05/16/2024]
Abstract
BACKGROUND Age and sex can be estimated using artificial intelligence on the basis of various sources. The aims of this study were to test whether convolutional neural networks could be trained to estimate age and predict sex using standard transthoracic echocardiography and to evaluate the prognostic implications. METHODS The algorithm was trained on 76,342 patients, validated in 22,825 patients, and tested in 20,960 patients. It was then externally validated using data from a different hospital (n = 556). Finally, a prospective cohort of handheld point-of-care ultrasound devices (n = 319; ClinicalTrials.gov identifier NCT05455541) was used to confirm the findings. A multivariate Cox regression model was used to investigate the association between age estimation and chronologic age with overall survival. RESULTS The mean absolute error in age estimation was 4.9 years, with a Pearson correlation coefficient of 0.922. The probabilistic value of sex had an overall accuracy of 96.1% and an area under the curve of 0.993. External validation and prospective study cohorts yielded consistent results. Finally, survival analysis demonstrated that age prediction ≥5 years vs chronologic age was associated with an independent 34% increased risk for death during follow-up (P < .001). CONCLUSIONS Applying artificial intelligence to standard transthoracic echocardiography allows the prediction of sex and the estimation of age. Machine-based estimation is an independent predictor of overall survival and, with further evaluation, can be used for risk stratification and estimation of biological age.
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Affiliation(s)
- Kobi Faierstein
- Leviev Cardiovascular Institute, Sheba Medical Center, Ramat Gan, Israel; Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel.
| | | | - Ranel Loutati
- Leviev Cardiovascular Institute, Sheba Medical Center, Ramat Gan, Israel
| | | | - Uri Manor
- Leviev Cardiovascular Institute, Sheba Medical Center, Ramat Gan, Israel; Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel
| | | | | | - Nimrod Blank
- Echocardiography Unit, Division of Cardiovascular Medicine, Baruch-Padeh Medical Center, Poria, Israel
| | - Dor Lotan
- Division of Cardiology, Department of Medicine, NewYork-Presbyterian Hospital, Columbia University Irving Medical Center, New York, New York
| | - Qiong Zhao
- Inova Heart and Vascular Institute, Inova Fairfax Hospital, Falls Church, Virginia
| | - Ehud Schwammenthal
- Leviev Cardiovascular Institute, Sheba Medical Center, Ramat Gan, Israel; Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel; Aisap.ai, Ramat Gan, Israel
| | - Robert Klempfner
- Leviev Cardiovascular Institute, Sheba Medical Center, Ramat Gan, Israel; Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel; Aisap.ai, Ramat Gan, Israel
| | - Eyal Zimlichman
- Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Ehud Raanani
- Leviev Cardiovascular Institute, Sheba Medical Center, Ramat Gan, Israel; Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel; Aisap.ai, Ramat Gan, Israel
| | - Elad Maor
- Leviev Cardiovascular Institute, Sheba Medical Center, Ramat Gan, Israel; Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel; Aisap.ai, Ramat Gan, Israel
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Hong N, Whittier DE, Glüer CC, Leslie WD. The potential role for artificial intelligence in fracture risk prediction. Lancet Diabetes Endocrinol 2024; 12:596-600. [PMID: 38942044 DOI: 10.1016/s2213-8587(24)00153-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Revised: 05/16/2024] [Accepted: 05/16/2024] [Indexed: 06/30/2024]
Abstract
Osteoporotic fractures are a major health challenge in older adults. Despite the availability of safe and effective therapies for osteoporosis, these therapies are underused in individuals at high risk for fracture, calling for better case-finding and fracture risk assessment strategies. Artificial intelligence (AI) and machine learning (ML) hold promise for enhancing identification of individuals at high risk for fracture by distilling useful features from high-dimensional data derived from medical records, imaging, and wearable devices. AI-ML could enable automated opportunistic screening for vertebral fractures and osteoporosis, home-based monitoring and intervention targeting lifestyle factors, and integration of multimodal features to leverage fracture prediction, ultimately aiding improved fracture risk assessment and individualised treatment. Optimism must be balanced with consideration for the explainability of AI-ML models, biases (including information inequity in numerically under-represented populations), model limitations, and net clinical benefit and workload impact. Clinical integration of AI-ML algorithms has the potential to transform osteoporosis management, offering a more personalised approach to reduce the burden of osteoporotic fractures.
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Affiliation(s)
- Namki Hong
- Department of Internal Medicine, Endocrine Research Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea; Institute for Innovation in Digital Healthcare, Yonsei University Health System, Seoul, Korea.
| | - Danielle E Whittier
- McCaig Institute for Bone and Joint Health and Alberta Children's Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Claus-C Glüer
- Section Biomedical Imaging, Department of Radiology and Neuroradiology, University Medical Center Schleswig-Holstein, Kiel University, Kiel, Germany
| | - William D Leslie
- Department of Internal Medicine, University of Manitoba, Winnipeg, MB, Canada
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15
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T P, Mitra D, Nagaraju PK, Sirohi GK, Periyadan Kandinhapally S. Enhancing dermatological diagnosis with artificial intelligence: a comparative study of ChatGPT-4 and Google Lens. Int J Dermatol 2024. [PMID: 39039696 DOI: 10.1111/ijd.17392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 06/24/2024] [Accepted: 07/09/2024] [Indexed: 07/24/2024]
Affiliation(s)
- Praveenraj T
- Department of Dermatology, Command Hospital Air Force, Bengaluru, India
| | - Debdeep Mitra
- Department of Dermatology, Command Hospital Air Force, Bengaluru, India
| | | | - Gulshan K Sirohi
- Department of Dermatology, Command Hospital Air Force, Bengaluru, India
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16
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Bernardini A, Bindini L, Antonucci E, Berteotti M, Giusti B, Testa S, Palareti G, Poli D, Frasconi P, Marcucci R. Machine learning approach for prediction of outcomes in anticoagulated patients with atrial fibrillation. Int J Cardiol 2024; 407:132088. [PMID: 38657869 DOI: 10.1016/j.ijcard.2024.132088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 04/15/2024] [Accepted: 04/19/2024] [Indexed: 04/26/2024]
Abstract
BACKGROUND The accuracy of available prediction tools for clinical outcomes in patients with atrial fibrillation (AF) remains modest. Machine Learning (ML) has been used to predict outcomes in the AF population, but not in a population entirely on anticoagulant therapy. METHODS AND AIMS Different supervised ML models were applied to predict all-cause death, cardiovascular (CV) death, major bleeding and stroke in anticoagulated patients with AF, processing data from the multicenter START-2 Register. RESULTS 11078 AF patients (male n = 6029, 54.3%) were enrolled with a median follow-up period of 1.5 years [IQR 1.0-2.6]. Patients on Vitamin K Antagonists (VKA) were 5135 (46.4%) and 5943 (53.6%) were on Direct Oral Anticoagulants (DOAC). Using Multi-Gate Mixture of Experts, a cross-validated AUC of 0.779 ± 0.016 and 0.745 ± 0.022 were obtained, respectively, for the prediction of all-cause death and CV-death in the overall population. The best ML model outperformed CHA2DSVA2SC and HAS-BLED for all-cause death prediction (p < 0.001 for both). When compared to HAS-BLED, Gradient Boosting improved major bleeding prediction in DOACs patients (0.711 vs. 0.586, p < 0.001). A very low number of events during follow-up (52) resulted in a suboptimal ischemic stroke prediction (best AUC of 0.606 ± 0.117 in overall population). Body mass index, age, renal function, platelet count and hemoglobin levels resulted the most important variables for ML prediction. CONCLUSIONS In AF patients, ML models showed good discriminative ability to predict all-cause death, regardless of the type of anticoagulation strategy, and major bleeding on DOAC therapy, outperforming CHA2DS2VASC and the HAS-BLED scores for risk prediction in these populations.
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Affiliation(s)
- Andrea Bernardini
- Cardiology and Electrophysiology Unit, Santa Maria Nuova Hospital, Florence, Italy; Department of Experimental and Clinical Medicine, University of Florence, Italy.
| | - Luca Bindini
- Department of Information Engineering, University of Florence, 50139 Florence, Italy
| | | | - Martina Berteotti
- Department of Experimental and Clinical Medicine, University of Florence, Italy
| | - Betti Giusti
- Department of Experimental and Clinical Medicine, University of Florence, Italy
| | - Sophie Testa
- Hemostasis and Thrombosis Center, Laboratory Medicine Department, Azienda Socio-Sanitaria Territoriale, Cremona, Italy
| | | | - Daniela Poli
- Department of Experimental and Clinical Medicine, University of Florence, Italy
| | - Paolo Frasconi
- Department of Information Engineering, University of Florence, 50139 Florence, Italy
| | - Rossella Marcucci
- Department of Experimental and Clinical Medicine, University of Florence, Italy
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Smoke S. Artificial intelligence in pharmacy: A guide for clinicians. Am J Health Syst Pharm 2024; 81:641-646. [PMID: 38394361 DOI: 10.1093/ajhp/zxae051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Indexed: 02/25/2024] Open
Affiliation(s)
- Steven Smoke
- Newark Beth Israel Medical Center, Newark, NJ, USA
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Berger JH. It Is Time to Consider the Ethical Implications of Artificial Intelligence Use in Generating Manuscripts for Peer-Reviewed Journals. J Endourol 2024; 38:707-708. [PMID: 38623791 DOI: 10.1089/end.2024.0020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/17/2024] Open
Affiliation(s)
- Jonathan H Berger
- Department of Urology, Naval Medical Center San Diego, San Diego, California, USA
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Haberfehlner H, Roth Z, Vanmechelen I, Buizer AI, Jeroen Vermeulen R, Koy A, Aerts JM, Hallez H, Monbaliu E. A Novel Video-Based Methodology for Automated Classification of Dystonia and Choreoathetosis in Dyskinetic Cerebral Palsy During a Lower Extremity Task. Neurorehabil Neural Repair 2024; 38:479-492. [PMID: 38842031 DOI: 10.1177/15459683241257522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2024]
Abstract
BACKGROUND Movement disorders in children and adolescents with dyskinetic cerebral palsy (CP) are commonly assessed from video recordings, however scoring is time-consuming and expert knowledge is required for an appropriate assessment. OBJECTIVE To explore a machine learning approach for automated classification of amplitude and duration of distal leg dystonia and choreoathetosis within short video sequences. METHODS Available videos of a heel-toe tapping task were preprocessed to optimize key point extraction using markerless motion analysis. Postprocessed key point data were passed to a time series classification ensemble algorithm to classify dystonia and choreoathetosis duration and amplitude classes (scores 0, 1, 2, 3, and 4), respectively. As ground truth clinical scoring of dystonia and choreoathetosis by the Dyskinesia Impairment Scale was used. Multiclass performance metrics as well as metrics for summarized scores: absence (score 0) and presence (score 1-4) were determined. RESULTS Thirty-three participants were included: 29 with dyskinetic CP and 4 typically developing, age 14 years:6 months ± 5 years:15 months. The multiclass accuracy results for dystonia were 77% for duration and 68% for amplitude; for choreoathetosis 30% for duration and 38% for amplitude. The metrics for score 0 versus score 1 to 4 revealed an accuracy of 81% for dystonia duration, 77% for dystonia amplitude, 53% for choreoathetosis duration and amplitude. CONCLUSIONS This methodology study yielded encouraging results in distinguishing between presence and absence of dystonia, but not for choreoathetosis. A larger dataset is required for models to accurately represent distinct classes/scores. This study presents a novel methodology of automated assessment of movement disorders solely from video data.
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Affiliation(s)
- Helga Haberfehlner
- Department of Rehabilitation Sciences, KU Leuven Bruges, Bruges, Belgium
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Rehabilitation Medicine, Amsterdam, The Netherlands
- Amsterdam Movement Sciences, Rehabilitation & Development, Amsterdam, The Netherlands
| | - Zachary Roth
- Department of Rehabilitation Sciences, KU Leuven Bruges, Bruges, Belgium
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
| | - Inti Vanmechelen
- Department of Rehabilitation Sciences, KU Leuven Bruges, Bruges, Belgium
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
| | - Annemieke I Buizer
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Rehabilitation Medicine, Amsterdam, The Netherlands
- Amsterdam Movement Sciences, Rehabilitation & Development, Amsterdam, The Netherlands
- Amsterdam UMC, Emma Children's Hospital, Amsterdam, The Netherlands
| | | | - Anne Koy
- Department of Pediatrics, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Jean-Marie Aerts
- Department of Computer Science, Mechatronics Research Group (M-Group), KU Leuven Bruges, Distrinet, Bruges, Belgium
| | - Hans Hallez
- Department of Biosystems, Division of Animal and Human Health Engineering, Measure, Model and Manage Bioresponse (M3-BIORES), KU Leuven, Leuven, Belgium
| | - Elegast Monbaliu
- Department of Rehabilitation Sciences, KU Leuven Bruges, Bruges, Belgium
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
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20
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Lungren MP, Fishman EK, Chu LC, Rizk RC, Rowe SP. More Is Different: Large Language Models in Health Care. J Am Coll Radiol 2024; 21:1151-1154. [PMID: 38043632 DOI: 10.1016/j.jacr.2023.11.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 11/12/2023] [Indexed: 12/05/2023]
Affiliation(s)
- Matthew P Lungren
- Chief Data Science Officer for Microsoft Health and Life Sciences, Microsoft, Inc., Redmond, Washington; and is from the Department of Radiology, University of California, San Francisco, California
| | - Elliot K Fishman
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Linda C Chu
- Associate Director of Diagnostic Imaging, The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Ryan C Rizk
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Steven P Rowe
- Director of Molecular Imaging and Therapeutics, Department of Radiology, University of North Carolina School of Medicine, Chapel Hill, North Carolina.
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21
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VanDecker WA. The Integrative Sport of Cardiac Imaging and Clinical Cardiology: Machine Augmentation and an Evolving Odyssey. JACC Cardiovasc Imaging 2024; 17:792-794. [PMID: 38613557 DOI: 10.1016/j.jcmg.2024.02.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 02/13/2024] [Indexed: 04/15/2024]
Affiliation(s)
- William A VanDecker
- Lewis Katz School of Medicine at Temple University, Philadelphia, Pennsylvania, USA.
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22
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Ladabaum U. Of Humans and Machines in Endoscopy: Flying Solo, Instrument Aided, or on Autopilot? Gastroenterology 2024; 167:210-212. [PMID: 38548191 DOI: 10.1053/j.gastro.2024.03.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Revised: 03/17/2024] [Accepted: 03/20/2024] [Indexed: 04/21/2024]
Affiliation(s)
- Uri Ladabaum
- Division of Gastroenterology and Hepatology, Department of Medicine, Stanford University School of Medicine, Stanford, California.
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23
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Kachman MM, Brennan I, Oskvarek JJ, Waseem T, Pines JM. How artificial intelligence could transform emergency care. Am J Emerg Med 2024; 81:40-46. [PMID: 38663302 DOI: 10.1016/j.ajem.2024.04.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Revised: 04/13/2024] [Accepted: 04/15/2024] [Indexed: 06/07/2024] Open
Abstract
Artificial intelligence (AI) in healthcare is the ability of a computer to perform tasks typically associated with clinical care (e.g. medical decision-making and documentation). AI will soon be integrated into an increasing number of healthcare applications, including elements of emergency department (ED) care. Here, we describe the basics of AI, various categories of its functions (including machine learning and natural language processing) and review emerging and potential future use-cases for emergency care. For example, AI-assisted symptom checkers could help direct patients to the appropriate setting, models could assist in assigning triage levels, and ambient AI systems could document clinical encounters. AI could also help provide focused summaries of charts, summarize encounters for hand-offs, and create discharge instructions with an appropriate language and reading level. Additional use cases include medical decision making for decision rules, real-time models that predict clinical deterioration or sepsis, and efficient extraction of unstructured data for coding, billing, research, and quality initiatives. We discuss the potential transformative benefits of AI, as well as the concerns regarding its use (e.g. privacy, data accuracy, and the potential for changing the doctor-patient relationship).
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Affiliation(s)
- Marika M Kachman
- US Acute Care Solutions, Canton, OH, United States of America; Department of Emergency Medicine, Virginia Hospital Center, Arlington, VA, United States of America
| | - Irina Brennan
- US Acute Care Solutions, Canton, OH, United States of America; Department of Emergency Medicine, Inova Alexandria Hospital, Alexandria, VA, United States of America
| | - Jonathan J Oskvarek
- US Acute Care Solutions, Canton, OH, United States of America; Department of Emergency Medicine, Summa Health, Akron, OH, United States of America
| | - Tayab Waseem
- Department of Emergency Medicine, George Washington University, Washington, DC, United States of America
| | - Jesse M Pines
- US Acute Care Solutions, Canton, OH, United States of America; Department of Emergency Medicine, George Washington University, Washington, DC, United States of America.
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24
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Ye Z, Sun D, Gary SE. Revolutionizing Postoperative Free Flap Monitoring-The Promise of AI to Improve Health Outcomes. JAMA Netw Open 2024; 7:e2424297. [PMID: 39058493 DOI: 10.1001/jamanetworkopen.2024.24297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/28/2024] Open
Affiliation(s)
- Zezhong Ye
- Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Dan Sun
- Department of Technology, Tempus AI Inc, Chicago, Illinois
| | - Sam E Gary
- Medical Scientist Training Program, University of Alabama at Birmingham, Birmingham
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25
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Alwood BT, Meyer DM, Ionita C, Snyder KV, Santos R, Perrotta L, Crooks R, Van Orden K, Torres D, Poynor B, Pham N, Kelly S, Meyer BC, Bolar DS. Multicenter comparison using two AI stroke CT perfusion software packages for determining thrombectomy eligibility. J Stroke Cerebrovasc Dis 2024; 33:107750. [PMID: 38703875 PMCID: PMC11366438 DOI: 10.1016/j.jstrokecerebrovasdis.2024.107750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Revised: 04/25/2024] [Accepted: 04/29/2024] [Indexed: 05/06/2024] Open
Abstract
BACKGROUND Stroke AI platforms assess infarcted core and potentially salvageable tissue (penumbra) to identify patients suitable for mechanical thrombectomy. Few studies have compared outputs of these platforms, and none have been multicenter or considered NIHSS or scanner/protocol differences. Our objective was to compare volume estimates and thrombectomy eligibility from two widely used CT perfusion (CTP) packages, Viz.ai and RAPID.AI, in a large multicenter cohort. METHODS We analyzed CTP data of acute stroke patients with large vessel occlusion (LVO) from four institutions. Core and penumbra volumes were estimated by each software and DEFUSE-3 thrombectomy eligibility assessed. Results between software packages were compared and categorized by NIHSS score, scanner manufacturer/model, and institution. RESULTS Primary analysis of 362 cases found statistically significant differences in both software's volume estimations, with subgroup analysis showing these differences were driven by results from a single scanner model, the Canon Aquilion One. Viz.ai provided larger estimates with mean differences of 8cc and 18cc for core and penumbra, respectively (p<0.001). NIHSS subgroup analysis also showed systematically larger Viz.ai volumes (p<0.001). Despite volume differences, a significant difference in thrombectomy eligibility was not found. Additional subgroup analysis showed significant differences in penumbra volume for the Phillips Ingenuity scanner, and thrombectomy eligibility for the Canon Aquilion One scanner at one center (7 % increased eligibility with Viz.ai, p=0.03). CONCLUSIONS Despite systematic differences in core and penumbra volume estimates between Viz.ai and RAPID.AI, DEFUSE-3 eligibility was not statistically different in primary or NIHSS subgroup analysis. A DEFUSE-3 eligibility difference, however, was seen on one scanner at one institution, suggesting scanner model and local CTP protocols can influence performance and cause discrepancies in thrombectomy eligibility. We thus recommend centers discuss optimal scanning protocols with software vendors and scanner manufacturers to maximize CTP accuracy.
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Affiliation(s)
- Benjamin T Alwood
- Department of Vascular Neurology, University of Florida, Jacksonville, FL, United States; University of California San Diego Stroke Center, University of California San Diego, San Diego, CA, United States.
| | - Dawn M Meyer
- University of California San Diego Stroke Center, University of California San Diego, San Diego, CA, United States
| | - Chip Ionita
- Department of Biomedical Engineering and Neurosurgery, University at Buffalo, Buffalo NY, United States
| | - Kenneth V Snyder
- Department of Biomedical Engineering and Neurosurgery, University at Buffalo, Buffalo NY, United States
| | - Roberta Santos
- Department of Vascular Neurology, University of Florida, Jacksonville, FL, United States
| | - Lindsey Perrotta
- Department of Vascular Neurology, University of Florida, Jacksonville, FL, United States
| | - Ryan Crooks
- Department of Vascular Neurology, University of Florida, Jacksonville, FL, United States
| | - Kimberlee Van Orden
- University of California San Diego Stroke Center, University of California San Diego, San Diego, CA, United States
| | - Dolores Torres
- University of California San Diego Stroke Center, University of California San Diego, San Diego, CA, United States
| | - Briana Poynor
- University of California San Diego Stroke Center, University of California San Diego, San Diego, CA, United States
| | - Nhan Pham
- Department of Radiology, University of California San Diego, San Diego, CA, United States
| | - Sophie Kelly
- Department of Radiology, University of California San Diego, San Diego, CA, United States
| | - Brett C Meyer
- University of California San Diego Stroke Center, University of California San Diego, San Diego, CA, United States
| | - Divya S Bolar
- Department of Radiology, University of California San Diego, San Diego, CA, United States; Center for Functional MRI, University of California San Diego, San Diego, CA, United States
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26
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Cusumano G, D'Arrigo S, Terminella A, Lococo F. Artificial Intelligence Applications for Thoracic Surgeons: "The Phenomenal Cosmic Powers of the Magic Lamp". J Clin Med 2024; 13:3750. [PMID: 38999317 PMCID: PMC11242691 DOI: 10.3390/jcm13133750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2024] [Revised: 06/17/2024] [Accepted: 06/21/2024] [Indexed: 07/14/2024] Open
Abstract
In the digital age, artificial intelligence (AI) is emerging as a transformative force in various sectors, including medicine. This article explores the potential of AI, which is akin to the magical genie of Aladdin's lamp, particularly within thoracic surgery and lung cancer management. It examines AI applications like machine learning and deep learning in achieving more precise diagnoses, preoperative risk assessment, and improved surgical outcomes. The challenges and advancements in AI integration, especially in computer vision and multi-modal models, are discussed alongside their impact on robotic surgery and operating room management. Despite its transformative potential, implementing AI in medicine faces challenges regarding data scarcity, interpretability issues, and ethical concerns. Collaboration between AI and medical communities is essential to address these challenges and unlock the full potential of AI in revolutionizing clinical practice. This article underscores the importance of further research and interdisciplinary collaboration to ensure the safe and effective deployment of AI in real-world clinical settings.
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Affiliation(s)
- Giacomo Cusumano
- General Thoracic Surgery Unit, Azienda Ospedaliero Universitaria Policlinico "G. Rodolico-San Marco", Via Santa Sofia 78, 95100 Catania, Italy
- Department of Surgery and Medical-Surgical Specialties, University of Catania, Via Santa Sofia 78, 95100 Catania, Italy
| | - Stefano D'Arrigo
- Department of Computer, Control and Management Engineering, Università La Sapienza, 00185 Rome, Italy
| | - Alberto Terminella
- General Thoracic Surgery Unit, Azienda Ospedaliero Universitaria Policlinico "G. Rodolico-San Marco", Via Santa Sofia 78, 95100 Catania, Italy
| | - Filippo Lococo
- General Thoracic Surgery, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
- Department of Thoracic Surgery, "Sacro Cuore"-Catholic University, 00168 Rome, Italy
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27
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Mahmud A, Dwivedi G, Chow BJW. Exploring the Integration of Artificial Intelligence in Cardiovascular Medical Education: Unveiling Opportunities and Advancements. Can J Cardiol 2024:S0828-282X(24)00503-8. [PMID: 38908789 DOI: 10.1016/j.cjca.2024.06.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 06/10/2024] [Accepted: 06/10/2024] [Indexed: 06/24/2024] Open
Affiliation(s)
- Asma Mahmud
- Fiona Stanley Hospital, Department of Cardiology, Murdoch, Australia
| | - Girish Dwivedi
- Fiona Stanley Hospital, Department of Cardiology, Murdoch, Australia; Harry Perkins Research Institute of Medical Research and The University of Western Australia, Crawley, Western Australia, Australia
| | - Benjamin J W Chow
- University of Ottawa Heart Institute, Department of Medicine (Cardiology), Ottawa, Ontario, Canada; Department of Radiology, University of Ottawa, Ottawa, Ontario, Canada.
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28
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Theriault-Lauzier P, Cobin D, Tastet O, Langlais EL, Taji B, Kang G, Chong AY, So D, Tang A, Gichoya JW, Chandar S, Déziel PL, Hussin JG, Kadoury S, Avram R. A Responsible Framework for Applying Artificial Intelligence on Medical Images and Signals at the Point of Care: The PACS-AI Platform. Can J Cardiol 2024:S0828-282X(24)00427-6. [PMID: 38885787 DOI: 10.1016/j.cjca.2024.05.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 05/09/2024] [Accepted: 05/26/2024] [Indexed: 06/20/2024] Open
Abstract
The potential of artificial intelligence (AI) in medicine lies in its ability to enhance clinicians' capacity to analyse medical images, thereby improving diagnostic precision and accuracy and thus enhancing current tests. However, the integration of AI within health care is fraught with difficulties. Heterogeneity among health care system applications, reliance on proprietary closed-source software, and rising cybersecurity threats pose significant challenges. Moreover, before their deployment in clinical settings, AI models must demonstrate their effectiveness across a wide range of scenarios and must be validated by prospective studies, but doing so requires testing in an environment mirroring the clinical workflow, which is difficult to achieve without dedicated software. Finally, the use of AI techniques in health care raises significant legal and ethical issues, such as the protection of patient privacy, the prevention of bias, and the monitoring of the device's safety and effectiveness for regulatory compliance. This review describes challenges to AI integration in health care and provides guidelines on how to move forward. We describe an open-source solution that we developed that integrates AI models into the Picture Archives Communication System (PACS), called PACS-AI. This approach aims to increase the evaluation of AI models by facilitating their integration and validation with existing medical imaging databases. PACS-AI may overcome many current barriers to AI deployment and offer a pathway toward responsible, fair, and effective deployment of AI models in health care. In addition, we propose a list of criteria and guidelines that AI researchers should adopt when publishing a medical AI model to enhance standardisation and reproducibility.
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Affiliation(s)
- Pascal Theriault-Lauzier
- Division of Cardiovascular Medicine, Stanford School of Medicine, Palo Alto, California, USA; Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Denis Cobin
- Montréal Heart Institute, Montréal, Québec, Canada
| | | | | | - Bahareh Taji
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Guson Kang
- Division of Cardiovascular Medicine, Stanford School of Medicine, Palo Alto, California, USA
| | - Aun-Yeong Chong
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Derek So
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - An Tang
- Department of Radiology, Radiation Oncology and Nuclear Medicine, Université de Montréal, Montréal, Québec, Canada
| | - Judy Wawira Gichoya
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia, USA
| | | | | | - Julie G Hussin
- Montréal Heart Institute, Montréal, Québec, Canada; Mila-Québec AI Institute, Montréal, Québec, Canada; Faculty of Law, Université Laval, Québec, Québec, Canada
| | - Samuel Kadoury
- Department of Radiology, Radiation Oncology and Nuclear Medicine, Université de Montréal, Montréal, Québec, Canada; Polytechnique Montréal, Montréal, Québec, Canada
| | - Robert Avram
- Montréal Heart Institute, Montréal, Québec, Canada; Department of Medicine, Université de Montréal, Montréal, Québec, Canada.
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29
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Shi Z, Hu B, Lu M, Chen Z, Zhang M, Yu Y, Zhou C, Zhong J, Wu B, Zhang X, Wei Y, Zhang LJ. Assessing the Impact of an Artificial Intelligence-Based Model for Intracranial Aneurysm Detection in CT Angiography on Patient Diagnosis and Outcomes (IDEAL Study)-a protocol for a multicenter, double-blinded randomized controlled trial. Trials 2024; 25:358. [PMID: 38835091 PMCID: PMC11151720 DOI: 10.1186/s13063-024-08184-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Accepted: 05/20/2024] [Indexed: 06/06/2024] Open
Abstract
BACKGROUND This multicenter, double-blinded, randomized controlled trial (RCT) aims to assess the impact of an artificial intelligence (AI)-based model on the efficacy of intracranial aneurysm detection in CT angiography (CTA) and its influence on patients' short-term and long-term outcomes. METHODS Study design: Prospective, multicenter, double-blinded RCT. SETTINGS The model was designed for the automatic detection of intracranial aneurysms from original CTA images. PARTICIPANTS Adult inpatients and outpatients who are scheduled for head CTA scanning. Randomization groups: (1) Experimental Group: Head CTA interpreted by radiologists with the assistance of the True-AI-integrated intracranial aneurysm diagnosis strategy (True-AI arm). (2) Control Group: Head CTA interpreted by radiologists with the assistance of the Sham-AI-integrated intracranial aneurysm diagnosis strategy (Sham-AI arm). RANDOMIZATION Block randomization, stratified by center, gender, and age group. PRIMARY OUTCOMES Coprimary outcomes of superiority in patient-level sensitivity and noninferiority in specificity for the True-AI arm to the Sham-AI arm in intracranial aneurysms. SECONDARY OUTCOMES Diagnostic performance for other intracranial lesions, detection rates, workload of CTA interpretation, resource utilization, treatment-related clinical events, aneurysm-related events, quality of life, and cost-effectiveness analysis. BLINDING Study participants and participating radiologists will be blinded to the intervention. SAMPLE SIZE Based on our pilot study, the patient-level sensitivity is assumed to be 0.65 for the Sham-AI arm and 0.75 for the True-AI arm, with specificities of 0.90 and 0.88, respectively. The prevalence of intracranial aneurysms for patients undergoing head CTA in the hospital is approximately 12%. To establish superiority in sensitivity and noninferiority in specificity with a margin of 5% using a one-sided α = 0.025 to ensure that the power of coprimary endpoint testing reached 0.80 and a 5% attrition rate, the sample size was determined to be 6450 in a 1:1 allocation to True-AI or Sham-AI arm. DISCUSSION The study will determine the precise impact of the AI system on the detection performance for intracranial aneurysms in a double-blinded design and following the real-world effects on patients' short-term and long-term outcomes. TRIAL REGISTRATION This trial has been registered with the NIH, U.S. National Library of Medicine at ClinicalTrials.gov, ID: NCT06118840 . Registered 11 November 2023.
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Affiliation(s)
- Zhao Shi
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, China
| | - Bin Hu
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, China
| | - Mengjie Lu
- Health Science Center, Ningbo University, Zhejiang, 315211, China
| | - Zijian Chen
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, China
| | - Manting Zhang
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 210002, China
| | - Yizhou Yu
- Department of Computer Science, The University of Hong Kong, Hong Kong, China
| | - Changsheng Zhou
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, China
| | - Jian Zhong
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, China
| | - Bingqian Wu
- Jinling Hospital, Nanjing Medical University, Nanjing, 210002, China
| | - Xueming Zhang
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, China
| | - Yongyue Wei
- Center for Public Health and Epidemic Preparedness & Response, Peking University, Beijing, 100191, China
| | - Long Jiang Zhang
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, China.
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30
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Heye T, Segeroth M, Franzeck F, Vosshenrich J. Turning radiology reports into epidemiological data to track seasonal pulmonary infections and the COVID-19 pandemic. Eur Radiol 2024; 34:3624-3634. [PMID: 37982834 PMCID: PMC11166749 DOI: 10.1007/s00330-023-10424-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 09/18/2023] [Accepted: 10/16/2023] [Indexed: 11/21/2023]
Abstract
OBJECTIVES To automatically label chest radiographs and chest CTs regarding the detection of pulmonary infection in the report text, to calculate the number needed to image (NNI) and to investigate if these labels correlate with regional epidemiological infection data. MATERIALS AND METHODS All chest imaging reports performed in the emergency room between 01/2012 and 06/2022 were included (64,046 radiographs; 27,705 CTs). Using a regular expression-based text search algorithm, reports were labeled positive/negative for pulmonary infection if described. Data for regional weekly influenza-like illness (ILI) consultations (10/2013-3/2022), COVID-19 cases, and hospitalization (2/2020-6/2022) were matched with report labels based on calendar date. Positive rate for pulmonary infection detection, NNI, and the correlation with influenza/COVID-19 data were calculated. RESULTS Between 1/2012 and 2/2020, a 10.8-16.8% per year positive rate for detecting pulmonary infections on chest radiographs was found (NNI 6.0-9.3). A clear and significant seasonal change in mean monthly detection counts (102.3 winter; 61.5 summer; p < .001) correlated moderately with regional ILI consultations (weekly data r = 0.45; p < .001). For 2020-2021, monthly pulmonary infection counts detected by chest CT increased to 64-234 (23.0-26.7% per year positive rate, NNI 3.7-4.3) compared with 14-94 (22.4-26.7% positive rate, NNI 3.7-4.4) for 2012-2019. Regional COVID-19 epidemic waves correlated moderately with the positive pulmonary infection CT curve for 2020-2022 (weekly new cases: r = 0.53; hospitalizations: r = 0.65; p < .001). CONCLUSION Text mining of radiology reports allows to automatically extract diagnoses. It provides a metric to calculate the number needed to image and to track the trend of diagnoses in real time, i.e., seasonality and epidemic course of pulmonary infections. CLINICAL RELEVANCE Digitally labeling radiology reports represent previously neglected data and may assist in automated disease tracking, in the assessment of physicians' clinical reasoning for ordering radiology examinations and serve as actionable data for hospital workflow optimization. KEY POINTS • Radiology reports, commonly not machine readable, can be automatically labeled with the contained diagnoses using a regular-expression based text search algorithm. • Chest radiograph reports positive for pulmonary infection moderately correlated with regional influenza-like illness consultations (weekly data; r = 0.45; p < .001) and chest CT reports with the course of the regional COVID-19 pandemic (new cases: r = 0.53; hospitalizations: r = 0.65; p < 0.001). • Rendering radiology reports into data labels provides a metric for automated disease tracking, the assessment of ordering physicians clinical reasoning and can serve as actionable data for workflow optimization.
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Affiliation(s)
- Tobias Heye
- Department of Radiology, University Hospital Basel, Petersgraben 4, 4031, Basel, Switzerland.
| | - Martin Segeroth
- Department of Radiology, University Hospital Basel, Petersgraben 4, 4031, Basel, Switzerland
| | - Fabian Franzeck
- Division of Infectious Diseases and Hospital Epidemiology, University Hospital Basel, Petersgraben 4, 4031, Basel, Switzerland
| | - Jan Vosshenrich
- Department of Radiology, University Hospital Basel, Petersgraben 4, 4031, Basel, Switzerland
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Kenig N, Monton Echeverria J, Rubi C. Ethics for AI in Plastic Surgery: Guidelines and Review. Aesthetic Plast Surg 2024; 48:2204-2209. [PMID: 38456892 DOI: 10.1007/s00266-024-03932-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 02/09/2024] [Indexed: 03/09/2024]
Abstract
INTRODUCTION Artificial intelligence (AI) holds the potential to revolutionize medicine, offering vast improvements for plastic surgery. While human physicians are limited to one lifetime of experience, AI is poised to soon surpass human capabilities, as it draws on limitless information and continuous learning abilities. Nevertheless, as AI becomes increasingly prevalent in this domain, it gives rise to critical ethical considerations that must be addressed by professionals. MATERIALS AND METHODS This work reviews the literature referring to the ethical challenges brought on by the ever-expanding use of AI in plastic surgery and offers guidelines for its application. RESULTS Ethical challenges include the disclosure of use of AI by caregivers, validation of decision-making, data privacy, informed consent and autonomy, potential biases in AI systems, the opaque nature of AI models, questions of liability, and the need for regulations. CONCLUSIONS There is a lack of consensus for the ethical use of AI in plastic surgery. Guidelines, such as those presented in this work, are needed within each discipline of medicine to respond to important ethical considerations for the safe use of AI. LEVEL OF EVIDENCE V This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
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Affiliation(s)
- Nitzan Kenig
- Instituto Rubi, Cami dels Reis, 308, 07010, Palma de Mallorca, Spain.
| | | | - Carlos Rubi
- Instituto Rubi, Cami dels Reis, 308, 07010, Palma de Mallorca, Spain
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Cetera GE, Tozzi AE, Chiappa V, Castiglioni I, Merli CEM, Vercellini P. Artificial Intelligence in the Management of Women with Endometriosis and Adenomyosis: Can Machines Ever Be Worse Than Humans? J Clin Med 2024; 13:2950. [PMID: 38792490 PMCID: PMC11121846 DOI: 10.3390/jcm13102950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 04/08/2024] [Accepted: 05/06/2024] [Indexed: 05/26/2024] Open
Abstract
Artificial intelligence (AI) is experiencing advances and integration in all medical specializations, and this creates excitement but also concerns. This narrative review aims to critically assess the state of the art of AI in the field of endometriosis and adenomyosis. By enabling automation, AI may speed up some routine tasks, decreasing gynecologists' risk of burnout, as well as enabling them to spend more time interacting with their patients, increasing their efficiency and patients' perception of being taken care of. Surgery may also benefit from AI, especially through its integration with robotic surgery systems. This may improve the detection of anatomical structures and enhance surgical outcomes by combining intra-operative findings with pre-operative imaging. Not only that, but AI promises to improve the quality of care by facilitating clinical research. Through the introduction of decision-support tools, it can enhance diagnostic assessment; it can also predict treatment effectiveness and side effects, as well as reproductive prognosis and cancer risk. However, concerns exist regarding the fact that good quality data used in tool development and compliance with data sharing guidelines are crucial. Also, professionals are worried AI may render certain specialists obsolete. This said, AI is more likely to become a well-liked team member rather than a usurper.
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Affiliation(s)
- Giulia Emily Cetera
- Gynecology Unit, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy; (G.E.C.); (C.E.M.M.)
- Academic Center for Research on Adenomyosis and Endometriosis, Department of Clinical Sciences and Community Health, Università degli Studi di Milano, 20122 Milan, Italy
| | - Alberto Eugenio Tozzi
- Predictive and Preventive Medicine Research Unit, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy;
| | - Valentina Chiappa
- Gynaecologic Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy;
| | | | - Camilla Erminia Maria Merli
- Gynecology Unit, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy; (G.E.C.); (C.E.M.M.)
| | - Paolo Vercellini
- Gynecology Unit, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy; (G.E.C.); (C.E.M.M.)
- Academic Center for Research on Adenomyosis and Endometriosis, Department of Clinical Sciences and Community Health, Università degli Studi di Milano, 20122 Milan, Italy
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Alajaji SA, Khoury ZH, Jessri M, Sciubba JJ, Sultan AS. An Update on the Use of Artificial Intelligence in Digital Pathology for Oral Epithelial Dysplasia Research. Head Neck Pathol 2024; 18:38. [PMID: 38727841 PMCID: PMC11087425 DOI: 10.1007/s12105-024-01643-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Accepted: 03/30/2024] [Indexed: 05/13/2024]
Abstract
INTRODUCTION Oral epithelial dysplasia (OED) is a precancerous histopathological finding which is considered the most important prognostic indicator for determining the risk of malignant transformation into oral squamous cell carcinoma (OSCC). The gold standard for diagnosis and grading of OED is through histopathological examination, which is subject to inter- and intra-observer variability, impacting accurate diagnosis and prognosis. The aim of this review article is to examine the current advances in digital pathology for artificial intelligence (AI) applications used for OED diagnosis. MATERIALS AND METHODS We included studies that used AI for diagnosis, grading, or prognosis of OED on histopathology images or intraoral clinical images. Studies utilizing imaging modalities other than routine light microscopy (e.g., scanning electron microscopy), or immunohistochemistry-stained histology slides, or immunofluorescence were excluded from the study. Studies not focusing on oral dysplasia grading and diagnosis, e.g., to discriminate OSCC from normal epithelial tissue were also excluded. RESULTS A total of 24 studies were included in this review. Nineteen studies utilized deep learning (DL) convolutional neural networks for histopathological OED analysis, and 4 used machine learning (ML) models. Studies were summarized by AI method, main study outcomes, predictive value for malignant transformation, strengths, and limitations. CONCLUSION ML/DL studies for OED grading and prediction of malignant transformation are emerging as promising adjunctive tools in the field of digital pathology. These adjunctive objective tools can ultimately aid the pathologist in more accurate diagnosis and prognosis prediction. However, further supportive studies that focus on generalization, explainable decisions, and prognosis prediction are needed.
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Affiliation(s)
- Shahd A Alajaji
- Department of Oncology and Diagnostic Sciences, University of Maryland School of Dentistry, 650 W. Baltimore Street, 7 Floor, Baltimore, MD, 21201, USA
- Department of Oral Medicine and Diagnostic Sciences, College of Dentistry, King Saud University, Riyadh, Saudi Arabia
- Division of Artificial Intelligence Research, University of Maryland School of Dentistry, Baltimore, MD, USA
| | - Zaid H Khoury
- Department of Oral Diagnostic Sciences and Research, Meharry Medical College School of Dentistry, Nashville, TN, USA
| | - Maryam Jessri
- Oral Medicine and Pathology Department, School of Dentistry, University of Queensland, Herston, QLD, Australia
- Oral Medicine Department, Metro North Hospital and Health Services, Queensland Health, Brisbane, QLD, Australia
| | - James J Sciubba
- Department of Otolaryngology, Head & Neck Surgery, The Johns Hopkins University, Baltimore, MD, USA
| | - Ahmed S Sultan
- Department of Oncology and Diagnostic Sciences, University of Maryland School of Dentistry, 650 W. Baltimore Street, 7 Floor, Baltimore, MD, 21201, USA.
- Division of Artificial Intelligence Research, University of Maryland School of Dentistry, Baltimore, MD, USA.
- University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center, Baltimore, MD, USA.
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Bhatia A, Khalvati F, Ertl-Wagner BB. Artificial Intelligence in the Future Landscape of Pediatric Neuroradiology: Opportunities and Challenges. AJNR Am J Neuroradiol 2024; 45:549-553. [PMID: 38176730 PMCID: PMC11288527 DOI: 10.3174/ajnr.a8086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 10/17/2023] [Indexed: 01/06/2024]
Abstract
This paper will review how artificial intelligence (AI) will play an increasingly important role in pediatric neuroradiology in the future. A safe, transparent, and human-centric AI is needed to tackle the quadruple aim of improved health outcomes, enhanced patient and family experience, reduced costs, and improved well-being of the healthcare team in pediatric neuroradiology. Equity, diversity and inclusion, data safety, and access to care will need to always be considered. In the next decade, AI algorithms are expected to play an increasingly important role in access to care, workflow management, abnormality detection, classification, response prediction, prognostication, report generation, as well as in the patient and family experience in pediatric neuroradiology. Also, AI algorithms will likely play a role in recognizing and flagging rare diseases and in pattern recognition to identify previously unknown disorders. While AI algorithms will play an important role, humans will not only need to be in the loop, but in the center of pediatric neuroimaging. AI development and deployment will need to be closely watched and monitored by experts in the field. Patient and data safety need to be at the forefront, and the risks of a dependency on technology will need to be contained. The applications and implications of AI in pediatric neuroradiology will differ from adult neuroradiology.
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Affiliation(s)
- Aashim Bhatia
- From the Children's Hospital of Philadelphia (A.B.), Philadelphia, Pennsylvania
| | - Farzad Khalvati
- Hospital for Sick Children (F.K., B.B.E.-W.), Toronto, Ontario, Canada
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Venkatesh K, Mutasa S, Moore F, Sulam J, Yi PH. Gradient-Based Saliency Maps Are Not Trustworthy Visual Explanations of Automated AI Musculoskeletal Diagnoses. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01136-4. [PMID: 38710971 DOI: 10.1007/s10278-024-01136-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 04/30/2024] [Accepted: 05/01/2024] [Indexed: 05/08/2024]
Abstract
Saliency maps are popularly used to "explain" decisions made by modern machine learning models, including deep convolutional neural networks (DCNNs). While the resulting heatmaps purportedly indicate important image features, their "trustworthiness," i.e., utility and robustness, has not been evaluated for musculoskeletal imaging. The purpose of this study was to systematically evaluate the trustworthiness of saliency maps used in disease diagnosis on upper extremity X-ray images. The underlying DCNNs were trained using the Stanford MURA dataset. We studied four trustworthiness criteria-(1) localization accuracy of abnormalities, (2) repeatability, (3) reproducibility, and (4) sensitivity to underlying DCNN weights-across six different gradient-based saliency methods (Grad-CAM (GCAM), gradient explanation (GRAD), integrated gradients (IG), Smoothgrad (SG), smooth IG (SIG), and XRAI). Ground-truth was defined by the consensus of three fellowship-trained musculoskeletal radiologists who each placed bounding boxes around abnormalities on a holdout saliency test set. Compared to radiologists, all saliency methods showed inferior localization (AUPRCs: 0.438 (SG)-0.590 (XRAI); average radiologist AUPRC: 0.816), repeatability (IoUs: 0.427 (SG)-0.551 (IG); average radiologist IOU: 0.613), and reproducibility (IoUs: 0.250 (SG)-0.502 (XRAI); average radiologist IOU: 0.613) on abnormalities such as fractures, orthopedic hardware insertions, and arthritis. Five methods (GCAM, GRAD, IG, SG, XRAI) passed the sensitivity test. Ultimately, no saliency method met all four trustworthiness criteria; therefore, we recommend caution and rigorous evaluation of saliency maps prior to their clinical use.
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Affiliation(s)
- Kesavan Venkatesh
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Simukayi Mutasa
- University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 520 W Lombard St, Baltimore, MD, USA
| | - Fletcher Moore
- University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 520 W Lombard St, Baltimore, MD, USA
| | - Jeremias Sulam
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Paul H Yi
- University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 520 W Lombard St, Baltimore, MD, USA.
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Ingvar Å, Oloruntoba A, Sashindranath M, Miller R, Soyer HP, Guitera P, Caccetta T, Shumack S, Abbott L, Arnold C, Lawn C, Button-Sloan A, Janda M, Mar V. Minimum labelling requirements for dermatology artificial intelligence-based Software as Medical Device (SaMD): A consensus statement. Australas J Dermatol 2024; 65:e21-e29. [PMID: 38419186 DOI: 10.1111/ajd.14222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Accepted: 01/21/2024] [Indexed: 03/02/2024]
Abstract
BACKGROUND/OBJECTIVES Artificial intelligence (AI) holds remarkable potential to improve care delivery in dermatology. End users (health professionals and general public) of AI-based Software as Medical Devices (SaMD) require relevant labelling information to ensure that these devices can be used appropriately. Currently, there are no clear minimum labelling requirements for dermatology AI-based SaMDs. METHODS Common labelling recommendations for AI-based SaMD identified in a recent literature review were evaluated by an Australian expert panel in digital health and dermatology via a modified Delphi consensus process. A nine-point Likert scale was used to indicate importance of 10 items, and voting was conducted to determine the specific characteristics to include for some items. Consensus was achieved when more than 75% of the experts agreed that inclusion of information was necessary. RESULTS There was robust consensus supporting inclusion of all proposed items as minimum labelling requirements; indication for use, intended user, training and test data sets, algorithm design, image processing techniques, clinical validation, performance metrics, limitations, updates and adverse events. Nearly all suggested characteristics of the labelling items received endorsement, except for some characteristics related to performance metrics. Moreover, there was consensus that uniform labelling criteria should apply across all AI categories and risk classes set out by the Therapeutic Goods Administration. CONCLUSIONS This study provides critical evidence for setting labelling standards by the Therapeutic Goods Administration to safeguard patients, health professionals, consumers, industry, and regulatory bodies from AI-based dermatology SaMDs that do not currently provide adequate information about how they were developed and tested.
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Affiliation(s)
- Åsa Ingvar
- Victorian Melanoma Service, Alfred Health, Melbourne, Victoria, Australia
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
- Department of Dermatology, Skåne University Hospital, Lund, Sweden
- Department of Clinical Sciences, Lund University, Lund, Sweden
| | | | - Maithili Sashindranath
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Robert Miller
- Australasian College of Dermatologists, Sydney, Australia
| | - H Peter Soyer
- Australasian College of Dermatologists, Sydney, Australia
- Dermatology Research Centre, Frazer Institute, The University of Queensland, Brisbane, Queensland, Australia
| | - Pascale Guitera
- Australasian College of Dermatologists, Sydney, Australia
- Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
- Sydney Melanoma Diagnostic Centre, Royal Prince Alfred Hospital, Camperdown, Victoria, Australia
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia
| | - Tony Caccetta
- Australasian College of Dermatologists, Sydney, Australia
- Perth Dermatology Clinic, Perth, Western Australia, Australia
| | - Stephen Shumack
- Australasian College of Dermatologists, Sydney, Australia
- Royal North Shore Hospital of Sydney, Sydney, New South Wales, Australia
| | - Lisa Abbott
- Australasian College of Dermatologists, Sydney, Australia
- Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
- The Skin Hospital, Sydney, New South Wales, Australia
| | - Chris Arnold
- BioGrid Australia Ltd, Melbourne, Australia
- Hodgson Associates, Melbourne, Australia
- Australasian Society of Cosmetic Dermatologists, Melbourne, Australia
| | - Craig Lawn
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia
- Centre of Excellence in Melanoma Imaging, Brisbane, Queensland, Australia
| | | | - Monika Janda
- Australasian College of Dermatologists, Sydney, Australia
- Dermatology Research Centre, Frazer Institute, The University of Queensland, Brisbane, Queensland, Australia
- Centre for Health Services Research, The University of Queensland, Brisbane, Queensland, Australia
| | - Victoria Mar
- Victorian Melanoma Service, Alfred Health, Melbourne, Victoria, Australia
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
- Australasian College of Dermatologists, Sydney, Australia
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Han R, Acosta JN, Shakeri Z, Ioannidis JPA, Topol EJ, Rajpurkar P. Randomised controlled trials evaluating artificial intelligence in clinical practice: a scoping review. Lancet Digit Health 2024; 6:e367-e373. [PMID: 38670745 PMCID: PMC11068159 DOI: 10.1016/s2589-7500(24)00047-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 03/01/2024] [Accepted: 03/04/2024] [Indexed: 04/28/2024]
Abstract
This scoping review of randomised controlled trials on artificial intelligence (AI) in clinical practice reveals an expanding interest in AI across clinical specialties and locations. The USA and China are leading in the number of trials, with a focus on deep learning systems for medical imaging, particularly in gastroenterology and radiology. A majority of trials (70 [81%] of 86) report positive primary endpoints, primarily related to diagnostic yield or performance; however, the predominance of single-centre trials, little demographic reporting, and varying reports of operational efficiency raise concerns about the generalisability and practicality of these results. Despite the promising outcomes, considering the likelihood of publication bias and the need for more comprehensive research including multicentre trials, diverse outcome measures, and improved reporting standards is crucial. Future AI trials should prioritise patient-relevant outcomes to fully understand AI's true effects and limitations in health care.
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Affiliation(s)
- Ryan Han
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA; Department of Computer Science, Stanford University, Stanford, CA, USA; University of California Los Angeles-Caltech Medical Scientist Training Program, Los Angeles, CA, USA
| | - Julián N Acosta
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA; Rad AI, San Francisco, CA, USA
| | - Zahra Shakeri
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - John P A Ioannidis
- Stanford Prevention Research Center, Department of Medicine, Stanford University, Stanford, CA, USA; Meta-Research Innovation Center at Stanford, Stanford University, Stanford, CA, USA
| | - Eric J Topol
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA, USA.
| | - Pranav Rajpurkar
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
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Parker J, Coey J, Alambrouk T, Lakey SM, Green T, Brown A, Maxwell I, Ripley DP. Evaluating a Novel AI Tool for Automated Measurement of the Aortic Root and Valve in Cardiac Magnetic Resonance Imaging. Cureus 2024; 16:e59647. [PMID: 38832163 PMCID: PMC11146459 DOI: 10.7759/cureus.59647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/03/2024] [Indexed: 06/05/2024] Open
Abstract
Objective Evaluating an artificial intelligence (AI) tool (AIATELLA, version 1.0; AIATELLA Oy, Helsinki, Finland) in interpreting cardiac magnetic resonance (CMR) imaging to produce measurements of the aortic root and valve by comparison of accuracy and efficiency with that of three National Health Service (NHS) cardiologists. Methods AI-derived aortic root and valve measurements were recorded alongside manual measurements from three experienced NHS consultant cardiologists (CCs) over three separate sites in the northeast part of the United Kingdom. The study utilised a comprehensive dataset of CMR images, with the intraclass correlation coefficient (ICC) being the primary measure of concordance between the AI and the cardiologist assessments. Patient imaging was anonymised and blinded at the point of transfer to a secure data server. Results The study demonstrates a high level of concordance between AI assessment of the aortic root and valve with NHS cardiologists (ICC of 0.98). Notably, the AI delivered results in 2.6 seconds (+/- 0.532) compared to a mean of 334.5 seconds (+/- 61.9) by the cardiologists, a statistically significant improvement in efficiency without compromising accuracy. Conclusion AI's accuracy and speed of analysis suggest that it could be a valuable tool in cardiac diagnostics, addressing the challenges of time-consuming and variable clinician-based assessments. This research reinforces AI's role in optimising the patient journey and improving the efficiency of the diagnostic pathway.
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Affiliation(s)
- Jack Parker
- Health and Life Sciences, Northumbria University, Newcastle upon Tyne, GBR
- Imaging, AIATELLA Oy, Helsinki, FIN
- Imaging, AIATELLA Ltd., Newcastle upon Tyne, GBR
| | - James Coey
- School of Medicine, St. George's University, Newcastle upon Tyne, GBR
- Health and Life Sciences, Northumbria University, Newcastle upon Tyne, GBR
- Imaging, AIATELLA Oy, Helsinki, FIN
| | - Tarek Alambrouk
- School of Medicine, St. George's University, Newcastle upon Tyne, GBR
| | - Samuel M Lakey
- Cardiology, Northumbria Healthcare NHS Foundation Trust, Newcastle upon Tyne, GBR
| | - Thomas Green
- Cardiology, Northumbria Healthcare NHS Foundation Trust, Newcastle upon Tyne, GBR
| | - Alexander Brown
- Cardiology, Northumbria Healthcare NHS Foundation Trust, Newcastle upon Tyne, GBR
| | - Ian Maxwell
- Faculty of Health Sciences and Wellbeing, University of Sunderland, Sunderland, GBR
| | - David P Ripley
- Cardiology, Northumbria Healthcare NHS Foundation Trust, Newcastle upon Tyne, GBR
- Faculty of Health Sciences and Wellbeing, University of Sunderland, Sunderland, GBR
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Serrano RA, Smeltz AM. The Promise of Artificial Intelligence-Assisted Point-of-Care Ultrasonography in Perioperative Care. J Cardiothorac Vasc Anesth 2024; 38:1244-1250. [PMID: 38402063 DOI: 10.1053/j.jvca.2024.01.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 01/29/2024] [Indexed: 02/26/2024]
Abstract
The role of point-of-care ultrasonography in the perioperative setting has expanded rapidly over recent years. Revolutionizing this technology further is integrating artificial intelligence to assist clinicians in optimizing images, identifying anomalies, performing automated measurements and calculations, and facilitating diagnoses. Artificial intelligence can increase point-of-care ultrasonography efficiency and accuracy, making it an even more valuable point-of-care tool. Given this topic's importance and ever-changing landscape, this review discusses the latest trends to serve as an introduction and update in this area.
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Affiliation(s)
| | - Alan M Smeltz
- University of North Carolina School of Medicine, Chapel Hill, NC
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40
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Gennari AG, Rossi A, De Cecco CN, van Assen M, Sartoretti T, Giannopoulos AA, Schwyzer M, Huellner MW, Messerli M. Artificial intelligence in coronary artery calcium score: rationale, different approaches, and outcomes. Int J Cardiovasc Imaging 2024; 40:951-966. [PMID: 38700819 PMCID: PMC11147943 DOI: 10.1007/s10554-024-03080-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Accepted: 03/09/2024] [Indexed: 06/05/2024]
Abstract
Almost 35 years after its introduction, coronary artery calcium score (CACS) not only survived technological advances but became one of the cornerstones of contemporary cardiovascular imaging. Its simplicity and quantitative nature established it as one of the most robust approaches for atherosclerotic cardiovascular disease risk stratification in primary prevention and a powerful tool to guide therapeutic choices. Groundbreaking advances in computational models and computer power translated into a surge of artificial intelligence (AI)-based approaches directly or indirectly linked to CACS analysis. This review aims to provide essential knowledge on the AI-based techniques currently applied to CACS, setting the stage for a holistic analysis of the use of these techniques in coronary artery calcium imaging. While the focus of the review will be detailing the evidence, strengths, and limitations of end-to-end CACS algorithms in electrocardiography-gated and non-gated scans, the current role of deep-learning image reconstructions, segmentation techniques, and combined applications such as simultaneous coronary artery calcium and pulmonary nodule segmentation, will also be discussed.
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Affiliation(s)
- Antonio G Gennari
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, Zurich, 8091, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Alexia Rossi
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, Zurich, 8091, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Carlo N De Cecco
- Division of Cardiothoracic Imaging, Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA
- Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Emory University, Atlanta, GA, USA
| | - Marly van Assen
- Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Emory University, Atlanta, GA, USA
| | - Thomas Sartoretti
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, Zurich, 8091, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Andreas A Giannopoulos
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, Zurich, 8091, Switzerland
| | - Moritz Schwyzer
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, Zurich, 8091, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Martin W Huellner
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, Zurich, 8091, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Michael Messerli
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, Zurich, 8091, Switzerland.
- University of Zurich, Zurich, Switzerland.
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Cheng CT, Ooyang CH, Kang SC, Liao CH. Applications of Deep Learning in Trauma Radiology: A Narrative Review. Biomed J 2024:100743. [PMID: 38679199 DOI: 10.1016/j.bj.2024.100743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 03/26/2024] [Accepted: 04/24/2024] [Indexed: 05/01/2024] Open
Abstract
Diagnostic imaging is essential in modern trauma care for initial evaluation and identifying injuries requiring intervention. Deep learning (DL) has become mainstream in medical image analysis and has shown promising efficacy for classification, segmentation, and lesion detection. This narrative review provides the fundamental concepts for developing DL algorithms in trauma imaging and presents an overview of current progress in each modality. DL has been applied to detect free fluid on Focused Assessment with Sonography for Trauma (FAST), traumatic findings on chest and pelvic X-rays, and computed tomography (CT) scans, identify intracranial hemorrhage on head CT, detect vertebral fractures, and identify injuries to organs like the spleen, liver, and lungs on abdominal and chest CT. Future directions involve expanding dataset size and diversity through federated learning, enhancing model explainability and transparency to build clinician trust, and integrating multimodal data to provide more meaningful insights into traumatic injuries. Though some commercial artificial intelligence products are Food and Drug Administration-approved for clinical use in the trauma field, adoption remains limited, highlighting the need for multi-disciplinary teams to engineer practical, real-world solutions. Overall, DL shows immense potential to improve the efficiency and accuracy of trauma imaging, but thoughtful development and validation are critical to ensure these technologies positively impact patient care.
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Affiliation(s)
- Chi-Tung Cheng
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan Taiwan
| | - Chun-Hsiang Ooyang
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan Taiwan
| | - Shih-Ching Kang
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan Taiwan.
| | - Chien-Hung Liao
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan Taiwan
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Wen Z, Wang Y, Zhong Y, Hu Y, Yang C, Peng Y, Zhan X, Zhou P, Zeng Z. Advances in research and application of artificial intelligence and radiomic predictive models based on intracranial aneurysm images. Front Neurol 2024; 15:1391382. [PMID: 38694771 PMCID: PMC11061371 DOI: 10.3389/fneur.2024.1391382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Accepted: 04/02/2024] [Indexed: 05/04/2024] Open
Abstract
Intracranial aneurysm is a high-risk disease, with imaging playing a crucial role in their diagnosis and treatment. The rapid advancement of artificial intelligence in imaging technology holds promise for the development of AI-based radiomics predictive models. These models could potentially enable the automatic detection and diagnosis of intracranial aneurysms, assess their status, and predict outcomes, thereby assisting in the creation of personalized treatment plans. In addition, these techniques could improve diagnostic efficiency for physicians and patient prognoses. This article aims to review the progress of artificial intelligence radiomics in the study of intracranial aneurysms, addressing the challenges faced and future prospects, in hopes of introducing new ideas for the precise diagnosis and treatment of intracranial aneurysms.
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Affiliation(s)
- Zhongjian Wen
- School of Nursing, Southwest Medical University, Luzhou, China
- Wound Healing Basic Research and Clinical Application Key Laboratory of Luzhou, School of Nursing, Southwest Medical University, Luzhou, China
| | - Yiren Wang
- School of Nursing, Southwest Medical University, Luzhou, China
- Wound Healing Basic Research and Clinical Application Key Laboratory of Luzhou, School of Nursing, Southwest Medical University, Luzhou, China
| | - Yuxin Zhong
- School of Nursing, Guizhou Medical University, Guiyang, China
| | - Yiheng Hu
- Department of Medical Imaging, Southwest Medical University, Luzhou, China
| | - Cheng Yang
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, China
| | - Yan Peng
- Department of Interventional Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Xiang Zhan
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Ping Zhou
- Wound Healing Basic Research and Clinical Application Key Laboratory of Luzhou, School of Nursing, Southwest Medical University, Luzhou, China
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Zhen Zeng
- Psychiatry Department, The Affiliated Hospital of Southwest Medical University, Luzhou, China
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Gui H, Rezaei SJ, Schlessinger D, Weed J, Lester J, Wongvibulsin S, Mitchell D, Ko J, Rotemberg V, Lee I, Daneshjou R. Dermatologists' Perspectives and Usage of Large Language Models in Practice: An Exploratory Survey. J Invest Dermatol 2024:S0022-202X(24)00270-7. [PMID: 38582369 DOI: 10.1016/j.jid.2024.03.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 03/19/2024] [Indexed: 04/08/2024]
Affiliation(s)
- Haiwen Gui
- Department of Dermatology, Stanford University, Redwood City, California, USA.
| | - Shawheen J Rezaei
- Department of Dermatology, Stanford University, Redwood City, California, USA
| | - Daniel Schlessinger
- Division of Dermatology, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Jason Weed
- The Ronald O. Perelman Department of Dermatology, NYU Grossman School of Medicine, New York, New York, USA
| | - Jenna Lester
- Department of Dermatology, University of California San Francisco, San Francisco, California, USA
| | - Shannon Wongvibulsin
- Division of Dermatology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Dom Mitchell
- Department of Dermatology, Stanford University, Redwood City, California, USA
| | - Justin Ko
- Department of Dermatology, Stanford University, Redwood City, California, USA
| | - Veronica Rotemberg
- Dermatology Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Ivy Lee
- Pasadena Premier Dermatology, Pasadena, California, USA
| | - Roxana Daneshjou
- Department of Dermatology, Stanford University, Redwood City, California, USA
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Zhenzhu L, Jingfeng Z, Wei Z, Jianjun Z, Yinshui X. GPT-agents based on medical guidelines can improve the responsiveness and explainability of outcomes for traumatic brain injury rehabilitation. Sci Rep 2024; 14:7626. [PMID: 38561445 PMCID: PMC10985066 DOI: 10.1038/s41598-024-58514-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Accepted: 03/30/2024] [Indexed: 04/04/2024] Open
Abstract
This study explored the application of generative pre-trained transformer (GPT) agents based on medical guidelines using large language model (LLM) technology for traumatic brain injury (TBI) rehabilitation-related questions. To assess the effectiveness of multiple agents (GPT-agents) created using GPT-4, a comparison was conducted using direct GPT-4 as the control group (GPT-4). The GPT-agents comprised multiple agents with distinct functions, including "Medical Guideline Classification", "Question Retrieval", "Matching Evaluation", "Intelligent Question Answering (QA)", and "Results Evaluation and Source Citation". Brain rehabilitation questions were selected from the doctor-patient Q&A database for assessment. The primary endpoint was a better answer. The secondary endpoints were accuracy, completeness, explainability, and empathy. Thirty questions were answered; overall GPT-agents took substantially longer and more words to respond than GPT-4 (time: 54.05 vs. 9.66 s, words: 371 vs. 57). However, GPT-agents provided superior answers in more cases compared to GPT-4 (66.7 vs. 33.3%). GPT-Agents surpassed GPT-4 in accuracy evaluation (3.8 ± 1.02 vs. 3.2 ± 0.96, p = 0.0234). No difference in incomplete answers was found (2 ± 0.87 vs. 1.7 ± 0.79, p = 0.213). However, in terms of explainability (2.79 ± 0.45 vs. 07 ± 0.52, p < 0.001) and empathy (2.63 ± 0.57 vs. 1.08 ± 0.51, p < 0.001) evaluation, the GPT-agents performed notably better. Based on medical guidelines, GPT-agents enhanced the accuracy and empathy of responses to TBI rehabilitation questions. This study provides guideline references and demonstrates improved clinical explainability. However, further validation through multicenter trials in a clinical setting is necessary. This study offers practical insights and establishes groundwork for the potential theoretical integration of LLM-agents medicine.
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Affiliation(s)
- Li Zhenzhu
- Radiology Department, Ningbo NO.2 Hospital, Ningbo, 315211, China
- Department of Neurosurgery, Ningbo NO.2 Hospital, Ningbo, 315211, China
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315211, China
| | - Zhang Jingfeng
- Radiology Department, Ningbo NO.2 Hospital, Ningbo, 315211, China
| | - Zhou Wei
- Department of Neurosurgery, Ningbo NO.2 Hospital, Ningbo, 315211, China
| | - Zheng Jianjun
- Radiology Department, Ningbo NO.2 Hospital, Ningbo, 315211, China.
| | - Xia Yinshui
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315211, China.
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Chang C, Shi W, Wang Y, Zhang Z, Huang X, Jiao Y. The path from task-specific to general purpose artificial intelligence for medical diagnostics: A bibliometric analysis. Comput Biol Med 2024; 172:108258. [PMID: 38467093 DOI: 10.1016/j.compbiomed.2024.108258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 02/08/2024] [Accepted: 03/06/2024] [Indexed: 03/13/2024]
Abstract
Artificial intelligence (AI) has revolutionized many fields, and its potential in healthcare has been increasingly recognized. Based on diverse data sources such as imaging, laboratory tests, medical records, and electrophysiological data, diagnostic AI has witnessed rapid development in recent years. A comprehensive understanding of the development status, contributing factors, and their relationships in the application of AI to medical diagnostics is essential to further promote its use in clinical practice. In this study, we conducted a bibliometric analysis to explore the evolution of task-specific to general-purpose AI for medical diagnostics. We used the Web of Science database to search for relevant articles published between 2010 and 2023, and applied VOSviewer, the R package Bibliometrix, and CiteSpace to analyze collaborative networks and keywords. Our analysis revealed that the field of AI in medical diagnostics has experienced rapid growth in recent years, with a focus on tasks such as image analysis, disease prediction, and decision support. Collaborative networks were observed among researchers and institutions, indicating a trend of global cooperation in this field. Additionally, we identified several key factors contributing to the development of AI in medical diagnostics, including data quality, algorithm design, and computational power. Challenges to progress in the field include model explainability, robustness, and equality, which will require multi-stakeholder, interdisciplinary collaboration to tackle. Our study provides a holistic understanding of the path from task-specific, mono-modal AI toward general-purpose, multimodal AI for medical diagnostics. With the continuous improvement of AI technology and the accumulation of medical data, we believe that AI will play a greater role in medical diagnostics in the future.
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Affiliation(s)
- Chuheng Chang
- Department of General Practice (General Internal Medicine), Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China; 4+4 Medical Doctor Program, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
| | - Wen Shi
- Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
| | - Youyang Wang
- Department of General Practice (General Internal Medicine), Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
| | - Zhan Zhang
- Department of Computer Science and Technology, Tsinghua University, Beijing, China.
| | - Xiaoming Huang
- Department of General Practice (General Internal Medicine), Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
| | - Yang Jiao
- Department of General Practice (General Internal Medicine), Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
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Cozzi A, Pinker K, Hidber A, Zhang T, Bonomo L, Lo Gullo R, Christianson B, Curti M, Rizzo S, Del Grande F, Mann RM, Schiaffino S, Panzer A. BI-RADS Category Assignments by GPT-3.5, GPT-4, and Google Bard: A Multilanguage Study. Radiology 2024; 311:e232133. [PMID: 38687216 PMCID: PMC11070611 DOI: 10.1148/radiol.232133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 03/08/2024] [Accepted: 03/12/2024] [Indexed: 05/02/2024]
Abstract
Background The performance of publicly available large language models (LLMs) remains unclear for complex clinical tasks. Purpose To evaluate the agreement between human readers and LLMs for Breast Imaging Reporting and Data System (BI-RADS) categories assigned based on breast imaging reports written in three languages and to assess the impact of discordant category assignments on clinical management. Materials and Methods This retrospective study included reports for women who underwent MRI, mammography, and/or US for breast cancer screening or diagnostic purposes at three referral centers. Reports with findings categorized as BI-RADS 1-5 and written in Italian, English, or Dutch were collected between January 2000 and October 2023. Board-certified breast radiologists and the LLMs GPT-3.5 and GPT-4 (OpenAI) and Bard, now called Gemini (Google), assigned BI-RADS categories using only the findings described by the original radiologists. Agreement between human readers and LLMs for BI-RADS categories was assessed using the Gwet agreement coefficient (AC1 value). Frequencies were calculated for changes in BI-RADS category assignments that would affect clinical management (ie, BI-RADS 0 vs BI-RADS 1 or 2 vs BI-RADS 3 vs BI-RADS 4 or 5) and compared using the McNemar test. Results Across 2400 reports, agreement between the original and reviewing radiologists was almost perfect (AC1 = 0.91), while agreement between the original radiologists and GPT-4, GPT-3.5, and Bard was moderate (AC1 = 0.52, 0.48, and 0.42, respectively). Across human readers and LLMs, differences were observed in the frequency of BI-RADS category upgrades or downgrades that would result in changed clinical management (118 of 2400 [4.9%] for human readers, 611 of 2400 [25.5%] for Bard, 573 of 2400 [23.9%] for GPT-3.5, and 435 of 2400 [18.1%] for GPT-4; P < .001) and that would negatively impact clinical management (37 of 2400 [1.5%] for human readers, 435 of 2400 [18.1%] for Bard, 344 of 2400 [14.3%] for GPT-3.5, and 255 of 2400 [10.6%] for GPT-4; P < .001). Conclusion LLMs achieved moderate agreement with human reader-assigned BI-RADS categories across reports written in three languages but also yielded a high percentage of discordant BI-RADS categories that would negatively impact clinical management. © RSNA, 2024 Supplemental material is available for this article.
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Affiliation(s)
| | | | - Andri Hidber
- From the Imaging Institute of Southern Switzerland (IIMSI), Ente
Ospedaliero Cantonale, Via Tesserete 46, 6900 Lugano, Switzerland (A.C., L.B.,
M.C., S.R., F.D.G., S.S.); Breast Imaging Service, Department of Radiology,
Memorial Sloan Kettering Cancer Center, New York, NY (K.P., R.L.G., B.C.);
Faculty of Biomedical Sciences, Università della Svizzera Italiana,
Lugano, Switzerland (A.H., S.R., F.D.G., S.S.); Department of Radiology,
Netherlands Cancer Institute, Amsterdam, the Netherlands (T.Z., R.M.M.);
Department of Diagnostic Imaging, Radboud University Medical Center, Nijmegen,
the Netherlands (T.Z., R.M.M.); and GROW Research Institute for Oncology and
Reproduction, Maastricht University, Maastricht, the Netherlands (T.Z.)
| | - Tianyu Zhang
- From the Imaging Institute of Southern Switzerland (IIMSI), Ente
Ospedaliero Cantonale, Via Tesserete 46, 6900 Lugano, Switzerland (A.C., L.B.,
M.C., S.R., F.D.G., S.S.); Breast Imaging Service, Department of Radiology,
Memorial Sloan Kettering Cancer Center, New York, NY (K.P., R.L.G., B.C.);
Faculty of Biomedical Sciences, Università della Svizzera Italiana,
Lugano, Switzerland (A.H., S.R., F.D.G., S.S.); Department of Radiology,
Netherlands Cancer Institute, Amsterdam, the Netherlands (T.Z., R.M.M.);
Department of Diagnostic Imaging, Radboud University Medical Center, Nijmegen,
the Netherlands (T.Z., R.M.M.); and GROW Research Institute for Oncology and
Reproduction, Maastricht University, Maastricht, the Netherlands (T.Z.)
| | - Luca Bonomo
- From the Imaging Institute of Southern Switzerland (IIMSI), Ente
Ospedaliero Cantonale, Via Tesserete 46, 6900 Lugano, Switzerland (A.C., L.B.,
M.C., S.R., F.D.G., S.S.); Breast Imaging Service, Department of Radiology,
Memorial Sloan Kettering Cancer Center, New York, NY (K.P., R.L.G., B.C.);
Faculty of Biomedical Sciences, Università della Svizzera Italiana,
Lugano, Switzerland (A.H., S.R., F.D.G., S.S.); Department of Radiology,
Netherlands Cancer Institute, Amsterdam, the Netherlands (T.Z., R.M.M.);
Department of Diagnostic Imaging, Radboud University Medical Center, Nijmegen,
the Netherlands (T.Z., R.M.M.); and GROW Research Institute for Oncology and
Reproduction, Maastricht University, Maastricht, the Netherlands (T.Z.)
| | - Roberto Lo Gullo
- From the Imaging Institute of Southern Switzerland (IIMSI), Ente
Ospedaliero Cantonale, Via Tesserete 46, 6900 Lugano, Switzerland (A.C., L.B.,
M.C., S.R., F.D.G., S.S.); Breast Imaging Service, Department of Radiology,
Memorial Sloan Kettering Cancer Center, New York, NY (K.P., R.L.G., B.C.);
Faculty of Biomedical Sciences, Università della Svizzera Italiana,
Lugano, Switzerland (A.H., S.R., F.D.G., S.S.); Department of Radiology,
Netherlands Cancer Institute, Amsterdam, the Netherlands (T.Z., R.M.M.);
Department of Diagnostic Imaging, Radboud University Medical Center, Nijmegen,
the Netherlands (T.Z., R.M.M.); and GROW Research Institute for Oncology and
Reproduction, Maastricht University, Maastricht, the Netherlands (T.Z.)
| | - Blake Christianson
- From the Imaging Institute of Southern Switzerland (IIMSI), Ente
Ospedaliero Cantonale, Via Tesserete 46, 6900 Lugano, Switzerland (A.C., L.B.,
M.C., S.R., F.D.G., S.S.); Breast Imaging Service, Department of Radiology,
Memorial Sloan Kettering Cancer Center, New York, NY (K.P., R.L.G., B.C.);
Faculty of Biomedical Sciences, Università della Svizzera Italiana,
Lugano, Switzerland (A.H., S.R., F.D.G., S.S.); Department of Radiology,
Netherlands Cancer Institute, Amsterdam, the Netherlands (T.Z., R.M.M.);
Department of Diagnostic Imaging, Radboud University Medical Center, Nijmegen,
the Netherlands (T.Z., R.M.M.); and GROW Research Institute for Oncology and
Reproduction, Maastricht University, Maastricht, the Netherlands (T.Z.)
| | - Marco Curti
- From the Imaging Institute of Southern Switzerland (IIMSI), Ente
Ospedaliero Cantonale, Via Tesserete 46, 6900 Lugano, Switzerland (A.C., L.B.,
M.C., S.R., F.D.G., S.S.); Breast Imaging Service, Department of Radiology,
Memorial Sloan Kettering Cancer Center, New York, NY (K.P., R.L.G., B.C.);
Faculty of Biomedical Sciences, Università della Svizzera Italiana,
Lugano, Switzerland (A.H., S.R., F.D.G., S.S.); Department of Radiology,
Netherlands Cancer Institute, Amsterdam, the Netherlands (T.Z., R.M.M.);
Department of Diagnostic Imaging, Radboud University Medical Center, Nijmegen,
the Netherlands (T.Z., R.M.M.); and GROW Research Institute for Oncology and
Reproduction, Maastricht University, Maastricht, the Netherlands (T.Z.)
| | - Stefania Rizzo
- From the Imaging Institute of Southern Switzerland (IIMSI), Ente
Ospedaliero Cantonale, Via Tesserete 46, 6900 Lugano, Switzerland (A.C., L.B.,
M.C., S.R., F.D.G., S.S.); Breast Imaging Service, Department of Radiology,
Memorial Sloan Kettering Cancer Center, New York, NY (K.P., R.L.G., B.C.);
Faculty of Biomedical Sciences, Università della Svizzera Italiana,
Lugano, Switzerland (A.H., S.R., F.D.G., S.S.); Department of Radiology,
Netherlands Cancer Institute, Amsterdam, the Netherlands (T.Z., R.M.M.);
Department of Diagnostic Imaging, Radboud University Medical Center, Nijmegen,
the Netherlands (T.Z., R.M.M.); and GROW Research Institute for Oncology and
Reproduction, Maastricht University, Maastricht, the Netherlands (T.Z.)
| | - Filippo Del Grande
- From the Imaging Institute of Southern Switzerland (IIMSI), Ente
Ospedaliero Cantonale, Via Tesserete 46, 6900 Lugano, Switzerland (A.C., L.B.,
M.C., S.R., F.D.G., S.S.); Breast Imaging Service, Department of Radiology,
Memorial Sloan Kettering Cancer Center, New York, NY (K.P., R.L.G., B.C.);
Faculty of Biomedical Sciences, Università della Svizzera Italiana,
Lugano, Switzerland (A.H., S.R., F.D.G., S.S.); Department of Radiology,
Netherlands Cancer Institute, Amsterdam, the Netherlands (T.Z., R.M.M.);
Department of Diagnostic Imaging, Radboud University Medical Center, Nijmegen,
the Netherlands (T.Z., R.M.M.); and GROW Research Institute for Oncology and
Reproduction, Maastricht University, Maastricht, the Netherlands (T.Z.)
| | | | | | - Ariane Panzer
- From the Imaging Institute of Southern Switzerland (IIMSI), Ente
Ospedaliero Cantonale, Via Tesserete 46, 6900 Lugano, Switzerland (A.C., L.B.,
M.C., S.R., F.D.G., S.S.); Breast Imaging Service, Department of Radiology,
Memorial Sloan Kettering Cancer Center, New York, NY (K.P., R.L.G., B.C.);
Faculty of Biomedical Sciences, Università della Svizzera Italiana,
Lugano, Switzerland (A.H., S.R., F.D.G., S.S.); Department of Radiology,
Netherlands Cancer Institute, Amsterdam, the Netherlands (T.Z., R.M.M.);
Department of Diagnostic Imaging, Radboud University Medical Center, Nijmegen,
the Netherlands (T.Z., R.M.M.); and GROW Research Institute for Oncology and
Reproduction, Maastricht University, Maastricht, the Netherlands (T.Z.)
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Lin L, Dacal E, Díez N, Carmona C, Martin Ramirez A, Barón Argos L, Bermejo-Peláez D, Caballero C, Cuadrado D, Darias-Plasencia O, García-Villena J, Bakardjiev A, Postigo M, Recalde-Jaramillo E, Flores-Chavez M, Santos A, Ledesma-Carbayo MJ, Rubio JM, Luengo-Oroz M. Edge Artificial Intelligence (AI) for real-time automatic quantification of filariasis in mobile microscopy. PLoS Negl Trop Dis 2024; 18:e0012117. [PMID: 38630833 PMCID: PMC11057975 DOI: 10.1371/journal.pntd.0012117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 04/29/2024] [Accepted: 03/29/2024] [Indexed: 04/19/2024] Open
Abstract
Filariasis, a neglected tropical disease caused by roundworms, is a significant public health concern in many tropical countries. Microscopic examination of blood samples can detect and differentiate parasite species, but it is time consuming and requires expert microscopists, a resource that is not always available. In this context, artificial intelligence (AI) can assist in the diagnosis of this disease by automatically detecting and differentiating microfilariae. In line with the target product profile for lymphatic filariasis as defined by the World Health Organization, we developed an edge AI system running on a smartphone whose camera is aligned with the ocular of an optical microscope that detects and differentiates filarias species in real time without the internet connection. Our object detection algorithm that uses the Single-Shot Detection (SSD) MobileNet V2 detection model was developed with 115 cases, 85 cases with 1903 fields of view and 3342 labels for model training, and 30 cases with 484 fields of view and 873 labels for model validation before clinical validation, is able to detect microfilariae at 10x magnification and distinguishes four species of them at 40x magnification: Loa loa, Mansonella perstans, Wuchereria bancrofti, and Brugia malayi. We validated our augmented microscopy system in the clinical environment by replicating the diagnostic workflow encompassed examinations at 10x and 40x with the assistance of the AI models analyzing 18 samples with the AI running on a middle range smartphone. It achieved an overall precision of 94.14%, recall of 91.90% and F1 score of 93.01% for the screening algorithm and 95.46%, 97.81% and 96.62% for the species differentiation algorithm respectively. This innovative solution has the potential to support filariasis diagnosis and monitoring, particularly in resource-limited settings where access to expert technicians and laboratory equipment is scarce.
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Affiliation(s)
- Lin Lin
- Spotlab, Madrid, Spain
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Madrid, Spain
| | | | | | - Claudia Carmona
- Malaria and Emerging Parasitic Diseases Laboratory, National Microbiology Centre, Instituto de Salud Carlos III—Madrid, Madrid, Spain
| | - Alexandra Martin Ramirez
- Malaria and Emerging Parasitic Diseases Laboratory, National Microbiology Centre, Instituto de Salud Carlos III—Madrid, Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Infecciosas (CIBERINFEC) Instituto de Salud Carlos III—Madrid, Madrid, Spain
| | - Lourdes Barón Argos
- Malaria and Emerging Parasitic Diseases Laboratory, National Microbiology Centre, Instituto de Salud Carlos III—Madrid, Madrid, Spain
| | | | | | | | | | | | | | | | - Ethan Recalde-Jaramillo
- Spotlab, Madrid, Spain
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Madrid, Spain
| | - Maria Flores-Chavez
- Malaria and Emerging Parasitic Diseases Laboratory, National Microbiology Centre, Instituto de Salud Carlos III—Madrid, Madrid, Spain
- Fundación Mundo Sano, Madrid, Spain
| | - Andrés Santos
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Madrid, Spain
| | - María Jesús Ledesma-Carbayo
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Madrid, Spain
| | - José M. Rubio
- Malaria and Emerging Parasitic Diseases Laboratory, National Microbiology Centre, Instituto de Salud Carlos III—Madrid, Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Infecciosas (CIBERINFEC) Instituto de Salud Carlos III—Madrid, Madrid, Spain
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48
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Fong FW, Hwang S, Xu Y, Hui WHA, Leung KHG, Lin L, Ho SY, Tang HS, Kwan CT, Ng PP, Hai JSH, Kwok FYJ, Sze HF, Fong AHT, Wan EYF, Lai YTA, Leung ST, Chan HL, Chan WSC, Cheung SCW, Lee CYJ, Yiu KH, Pennell DJ, Mohiaddin RH, Yan AT, Ng MY. Prognostic Utility of Left Atrial Strain From MRI Feature Tracking in Ischemic and Nonischemic Dilated Cardiomyopathy: A Multicenter Study. AJR Am J Roentgenol 2024; 222:e2330357. [PMID: 38323782 DOI: 10.2214/ajr.23.30357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2024]
Abstract
BACKGROUND. MRI-based prognostic evaluation in patients with dilated cardiomyopathy (DCM) has historically used markers of late gadolinium enhancement (LGE) and feature tracking (FT)-derived left ventricular global longitudinal strain (LVGLS). Early data indicate that FT-derived left atrial strain (LAS) parameters, including reservoir, conduit, and booster, may also have prognostic roles in such patients. OBJECTIVE. The purpose of our study was to evaluate the prognostic utility of LAS parameters, derived from MRI FT, in patients with ischemic or nonischemic DCM, including in comparison with the traditional parameters of LGE and LVGLS. METHODS. This retrospective study included 811 patients with ischemic or nonischemic DCM (median age, 60 years; 640 men, 171 women) who underwent cardiac MRI at any of five centers. FT-derived LAS parameters and LVGLS were measured using two- and four-chamber cine images. LGE percentage was quantified. Patients were assessed for a composite outcome of all-cause mortality or heart failure hospitalization. Multivariable Cox regression analyses including demographic characteristics, cardiovascular risk factors, medications used, and a wide range of cardiac MRI parameters were performed. Kaplan-Meier analyses with log-rank tests were also performed. RESULTS. A total of 419 patients experienced the composite outcome. Patients who did, versus those who did not, experience the composite outcome had larger LVGLS (-6.7% vs -8.3%, respectively; p < .001) as well as a smaller LAS reservoir (13.3% vs 19.3%, p < .001), LAS conduit (4.7% vs 8.0%, p < .001), and LAS booster (8.1% vs 10.3%, p < .001) but no significant difference in LGE (10.1% vs 11.3%, p = .51). In multivariable Cox regression analyses, significant independent predictors of the composite outcome included LAS reservoir (HR = 0.96, p < .001) and LAS conduit (HR = 0.91, p < .001). LAS booster and LGE were not significant independent predictors in the models. LVGLS was a significant independent predictor only in a model that initially included LAS booster but not the other LAS parameters. In Kaplan-Meier analysis, all three LAS parameters were significantly associated with the composite outcome (p < .001). CONCLUSION. In this multicenter study, LAS reservoir and LAS conduit were significant independent prognostic markers in patients with ischemic or nonischemic DCM, showing greater prognostic utility than the currently applied markers of LVGLS and LGE. CLINICAL IMPACT. FT-derived LAS analysis provides incremental prognostic information in patients with DCM.
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Affiliation(s)
- Fai Wang Fong
- Department of Diagnostic Radiology, The University of Hong Kong, Rm 406, Block K, Queen Mary Hospital, 102 Pokfulam Rd, Hong Kong SAR
| | - Subin Hwang
- Department of Diagnostic Radiology, The University of Hong Kong, Rm 406, Block K, Queen Mary Hospital, 102 Pokfulam Rd, Hong Kong SAR
| | - Yueyi Xu
- Department of Diagnostic Radiology, The University of Hong Kong, Rm 406, Block K, Queen Mary Hospital, 102 Pokfulam Rd, Hong Kong SAR
| | | | - Kwan Ho Gordon Leung
- Department of Diagnostic Radiology, The University of Hong Kong, Rm 406, Block K, Queen Mary Hospital, 102 Pokfulam Rd, Hong Kong SAR
| | - Lu Lin
- Department of Diagnostic Radiology, The University of Hong Kong, Rm 406, Block K, Queen Mary Hospital, 102 Pokfulam Rd, Hong Kong SAR
- Department of Medical Imaging, Peking Union Medical College, Beijing, China
| | - Shui Yan Ho
- Department of Diagnostic Radiology, The University of Hong Kong, Rm 406, Block K, Queen Mary Hospital, 102 Pokfulam Rd, Hong Kong SAR
| | - Hok Shing Tang
- Department of Diagnostic Radiology, The University of Hong Kong, Rm 406, Block K, Queen Mary Hospital, 102 Pokfulam Rd, Hong Kong SAR
| | - Chi Ting Kwan
- Department of Diagnostic Radiology, The University of Hong Kong, Rm 406, Block K, Queen Mary Hospital, 102 Pokfulam Rd, Hong Kong SAR
| | - Pan Pan Ng
- Department of Radiology and Imaging, Queen Elizabeth Hospital, Hong Kong SAR
| | - Jojo Siu Han Hai
- Department of Medicine, The University of Hong Kong, Queen Mary Hospital, Hong Kong SAR
| | - Fung Yu James Kwok
- Department of Diagnostic Radiology, The University of Hong Kong, Rm 406, Block K, Queen Mary Hospital, 102 Pokfulam Rd, Hong Kong SAR
| | - Ho Fung Sze
- Department of Diagnostic Radiology, The University of Hong Kong, Rm 406, Block K, Queen Mary Hospital, 102 Pokfulam Rd, Hong Kong SAR
| | - Ambrose Ho Tung Fong
- Department of Diagnostic Radiology, The University of Hong Kong, Rm 406, Block K, Queen Mary Hospital, 102 Pokfulam Rd, Hong Kong SAR
| | - Eric Yuk Fai Wan
- Department of Pharmacology and Pharmacy, The University of Hong Kong, Hong Kong SAR
| | - Yee Tak Alta Lai
- Department of Radiology, Pamela Youde Nethersole Eastern Hospital, Hong Kong SAR
- Department of Radiology, Ruttonjee and Tang Shiu Kin Hospitals, Hong Kong SAR
| | - Siu Ting Leung
- Imaging and Intervention Radiology Centre, CUHK Medical Centre, Hong Kong SAR
| | - Hiu Lam Chan
- Department of Medicine, Pamela Youde Nethersole Eastern Hospital, Hong Kong SAR
| | | | | | - Chun Yin Jonan Lee
- Department of Radiology and Imaging, Queen Elizabeth Hospital, Hong Kong SAR
| | - Kai-Hang Yiu
- Department of Medicine, The University of Hong Kong, Queen Mary Hospital, Hong Kong SAR
| | - Dudley J Pennell
- Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Raad H Mohiaddin
- Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Andrew T Yan
- Departments of Medicine and Medical Imaging, St. Michael's Hospital, University of Toronto, Toronto, ON, Canada
| | - Ming-Yen Ng
- Department of Diagnostic Radiology, The University of Hong Kong, Rm 406, Block K, Queen Mary Hospital, 102 Pokfulam Rd, Hong Kong SAR
- Department of Medical Imaging, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
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Caglayan A, Slusarczyk W, Rabbani RD, Ghose A, Papadopoulos V, Boussios S. Large Language Models in Oncology: Revolution or Cause for Concern? Curr Oncol 2024; 31:1817-1830. [PMID: 38668040 PMCID: PMC11049602 DOI: 10.3390/curroncol31040137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 03/13/2024] [Accepted: 03/29/2024] [Indexed: 04/28/2024] Open
Abstract
The technological capability of artificial intelligence (AI) continues to advance with great strength. Recently, the release of large language models has taken the world by storm with concurrent excitement and concern. As a consequence of their impressive ability and versatility, their provide a potential opportunity for implementation in oncology. Areas of possible application include supporting clinical decision making, education, and contributing to cancer research. Despite the promises that these novel systems can offer, several limitations and barriers challenge their implementation. It is imperative that concerns, such as accountability, data inaccuracy, and data protection, are addressed prior to their integration in oncology. As the progression of artificial intelligence systems continues, new ethical and practical dilemmas will also be approached; thus, the evaluation of these limitations and concerns will be dynamic in nature. This review offers a comprehensive overview of the potential application of large language models in oncology, as well as concerns surrounding their implementation in cancer care.
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Affiliation(s)
- Aydin Caglayan
- Department of Medical Oncology, Medway NHS Foundation Trust, Gillingham ME7 5NY, UK; (A.C.); (R.D.R.); (A.G.)
| | | | - Rukhshana Dina Rabbani
- Department of Medical Oncology, Medway NHS Foundation Trust, Gillingham ME7 5NY, UK; (A.C.); (R.D.R.); (A.G.)
| | - Aruni Ghose
- Department of Medical Oncology, Medway NHS Foundation Trust, Gillingham ME7 5NY, UK; (A.C.); (R.D.R.); (A.G.)
- Department of Medical Oncology, Barts Cancer Centre, St Bartholomew’s Hospital, Barts Heath NHS Trust, London EC1A 7BE, UK
- Department of Medical Oncology, Mount Vernon Cancer Centre, East and North Hertfordshire Trust, London HA6 2RN, UK
- Health Systems and Treatment Optimisation Network, European Cancer Organisation, 1040 Brussels, Belgium
- Oncology Council, Royal Society of Medicine, London W1G 0AE, UK
| | | | - Stergios Boussios
- Department of Medical Oncology, Medway NHS Foundation Trust, Gillingham ME7 5NY, UK; (A.C.); (R.D.R.); (A.G.)
- Kent Medway Medical School, University of Kent, Canterbury CT2 7LX, UK;
- Faculty of Life Sciences & Medicine, School of Cancer & Pharmaceutical Sciences, King’s College London, Strand Campus, London WC2R 2LS, UK
- Faculty of Medicine, Health, and Social Care, Canterbury Christ Church University, Canterbury CT2 7PB, UK
- AELIA Organization, 9th Km Thessaloniki—Thermi, 57001 Thessaloniki, Greece
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Yu H, Ye X, Hong W, Shi R, Ding Y, Liu C. A cascading learning method with SegFormer for radiographic measurement of periodontal bone loss. BMC Oral Health 2024; 24:325. [PMID: 38468273 PMCID: PMC10929133 DOI: 10.1186/s12903-024-04079-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 02/27/2024] [Indexed: 03/13/2024] Open
Abstract
OBJECTIVE Marginal alveolar bone loss is one of the key features of periodontitis and can be observed via panoramic radiographs. This study aimed to establish a cascading learning method with deep learning (DL) for precise radiographic bone loss (RBL) measurements at specific tooth positions. MATERIALS AND METHODS Through the design of two tasks for tooth position recognition and tooth semantic segmentation using the SegFormer model, specific tooth's crown, intrabony portion, and suprabony portion of the roots were obtained. The RBL was subsequently measured by length through these three areas using the principal component analysis (PCA) principal axis. RESULTS The average intersection over union (IoU) for the tooth position recognition task was 0.8906, with an F1-score of 0.9338. The average IoU for the tooth semantic segmentation task was 0.8465, with an F1-score of 0.9138. When the two tasks were combined, the average IoU was 0.7889, with an F1-score of 0.8674. The correlation coefficient between the RBL prediction results based on the PCA principal axis and the clinicians' measurements exceeded 0.85. Compared to those of the other two methods, the average precision of the predicted RBL was 0.7722, the average sensitivity was 0.7416, and the average F1-score was 0.7444. CONCLUSIONS The method for predicting RBL using DL and PCA produced promising results, offering rapid and reliable auxiliary information for future periodontal disease diagnosis. CLINICAL RELEVANCE Precise RBL measurements are important for periodontal diagnosis. The proposed RBL-SF can measure RBL at specific tooth positions and assign the bone loss stage. The ability of the RBL-SF to measure RBL at specific tooth positions can guide clinicians to a certain extent in the accurate diagnosis of periodontitis.
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Affiliation(s)
- Hanwen Yu
- School of Resources and Environment, University of Electronic Science and Technology, Chengdu, Sichuan, 610097, China
| | - Xin Ye
- School of Resources and Environment, University of Electronic Science and Technology, Chengdu, Sichuan, 610097, China
| | - Wanjing Hong
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Rui Shi
- School of Resources and Environment, University of Electronic Science and Technology, Chengdu, Sichuan, 610097, China
| | - Yi Ding
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Chengcheng Liu
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, 610041, China.
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