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Wanis KN, Madenci AL, Hao S, Moukheiber M, Moukheiber L, Moukheiber D, Moukheiber S, Young JG, Celi LA. Emulating Target Trials Comparing Early and Delayed Intubation Strategies. Chest 2023; 164:885-891. [PMID: 37150505 PMCID: PMC10567927 DOI: 10.1016/j.chest.2023.04.048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 04/15/2023] [Accepted: 04/30/2023] [Indexed: 05/09/2023] Open
Abstract
BACKGROUND Whether intubation should be initiated early in the clinical course of critically ill patients remains a matter of debate. Results from prior observational studies are difficult to interpret because of avoidable flaws including immortal time bias, inappropriate eligibility criteria, and unrealistic treatment strategies. RESEARCH QUESTION Do treatment strategies that intubate patients early in the critical care admission improve 30-day survival compared with strategies that delay intubation? STUDY DESIGN AND METHODS We estimated the effect of strategies that require early intubation of critically ill patients compared with those that delay intubation. With data extracted from the Medical Information Mart for Intensive Care-IV database, we emulated three target trials, varying the flexibility of the treatment strategies and the baseline eligibility criteria. RESULTS Under unrealistically strict treatment strategies with broad eligibility criteria, the 30-day mortality risk was 7.1 percentage points higher for intubating early compared with delaying intubation (95% CI, 6.2-7.9). Risk differences were 0.4 (95% CI, -0.1 to 0.9) and -0.9 (95% CI, -2.5 to 0.7) percentage points in subsequent target trial emulations that included more realistic treatment strategies and eligibility criteria. INTERPRETATION When realistic treatment strategies and eligibility criteria are used, strategies that delay intubation result in similar 30-day mortality risks compared with those that intubate early. Delaying intubation ultimately avoids intubation in most patients.
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Affiliation(s)
- Kerollos Nashat Wanis
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA; Division of General Surgery, Department of Surgery, Western University, London, ON, Canada.
| | - Arin L Madenci
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA; Department of Surgery, Boston Children's Hospital, Harvard Medical School, Boston, MA
| | - Sicheng Hao
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA
| | - Mira Moukheiber
- The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA
| | - Lama Moukheiber
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA
| | - Dana Moukheiber
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA
| | - Sulaiman Moukheiber
- Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA
| | - Jessica G Young
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA
| | - Leo Anthony Celi
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA; Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA; Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA
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Wang R, Chen LC, Moukheiber L, Seastedt KP, Moukheiber M, Moukheiber D, Zaiman Z, Moukheiber S, Litchman T, Trivedi H, Steinberg R, Gichoya JW, Kuo PC, Celi LA. Enabling chronic obstructive pulmonary disease diagnosis through chest X-rays: A multi-site and multi-modality study. Int J Med Inform 2023; 178:105211. [PMID: 37690225 DOI: 10.1016/j.ijmedinf.2023.105211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 07/23/2023] [Accepted: 09/01/2023] [Indexed: 09/12/2023]
Abstract
PURPOSE Chronic obstructive pulmonary disease (COPD) is one of the most common chronic illnesses in the world. Unfortunately, COPD is often difficult to diagnose early when interventions can alter the disease course, and it is underdiagnosed or only diagnosed too late for effective treatment. Currently, spirometry is the gold standard for diagnosing COPD but it can be challenging to obtain, especially in resource-poor countries. Chest X-rays (CXRs), however, are readily available and may have the potential as a screening tool to identify patients with COPD who should undergo further testing or intervention. In this study, we used three CXR datasets alongside their respective electronic health records (EHR) to develop and externally validate our models. METHOD To leverage the performance of convolutional neural network models, we proposed two fusion schemes: (1) model-level fusion, using Bootstrap aggregating to aggregate predictions from two models, (2) data-level fusion, using CXR image data from different institutions or multi-modal data, CXR image data, and EHR data for model training. Fairness analysis was then performed to evaluate the models across different demographic groups. RESULTS Our results demonstrate that DL models can detect COPD using CXRs with an area under the curve of over 0.75, which could facilitate patient screening for COPD, especially in low-resource regions where CXRs are more accessible than spirometry. CONCLUSIONS By using a ubiquitous test, future research could build on this work to detect COPD in patients early who would not otherwise have been diagnosed or treated, altering the course of this highly morbid disease.
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Affiliation(s)
- Ryan Wang
- Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan
| | - Li-Ching Chen
- Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan
| | - Lama Moukheiber
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kenneth P Seastedt
- Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Mira Moukheiber
- The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Dana Moukheiber
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Zachary Zaiman
- Department of Computer Science, Emory University, Atlanta, GA, USA
| | - Sulaiman Moukheiber
- Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Tess Litchman
- Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Hari Trivedi
- Department of Radiology, Emory University, Atlanta, GA, USA
| | | | - Judy W Gichoya
- Department of Radiology, Emory University, Atlanta, GA, USA
| | - Po-Chih Kuo
- Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan.
| | - Leo A Celi
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Division of Pulmonary Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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Holmes Fee C, Hicklen RS, Jean S, Abu Hussein N, Moukheiber L, de Lota MF, Moukheiber M, Moukheiber D, Anthony Celi L, Dankwa-Mullan I. Strategies and solutions to address Digital Determinants of Health (DDOH) across underinvested communities. PLOS Digit Health 2023; 2:e0000314. [PMID: 37824481 PMCID: PMC10569606 DOI: 10.1371/journal.pdig.0000314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
Healthcare has long struggled to improve services through technology without further widening health disparities. With the significant expansion of digital health, a group of healthcare professionals and scholars from across the globe are proposing the official usage of the term "Digital Determinants of Health" (DDOH) to explicitly call out the relationship between technology, healthcare, and equity. This is the final paper in a series published in PLOS Digital Health that seeks to understand and summarize current knowledge of the strategies and solutions that help to mitigate the negative effects of DDOH for underinvested communities. Through a search of English-language Medline, Scopus, and Google Scholar articles published since 2010, 345 articles were identified that discussed the application of digital health technology among underinvested communities. A group of 8 reviewers assessed 132 articles selected at random for the mention of solutions that minimize differences in DDOH. Solutions were then organized by categories of policy; design and development; implementation and adoption; and evaluation and ongoing monitoring. The data were then assessed by category and the findings summarized. The reviewers also looked for common themes across the solutions and evidence of effectiveness. From this limited scoping review, the authors found numerous solutions mentioned across the papers for addressing DDOH and many common themes emerged regardless of the specific community or digital health technology under review. There was notably less information on solutions regarding ongoing evaluation and monitoring which corresponded with a lack of research evidence regarding effectiveness. The findings directionally suggest that universal strategies and solutions can be developed to address DDOH independent of the specific community under focus. With the need for the further development of DDOH measures, we also provide a framework for DDOH assessment.
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Affiliation(s)
- Casey Holmes Fee
- Healthcare Consultant, Newton, Massachusetts, United States of America
| | - Rachel Scarlett Hicklen
- Research Medical Library, MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Sidney Jean
- Massachusetts Executive Office of Health and Human Services, Boston, Massachusetts, United States of America
- Simmons University, Boston, Massachusetts, United States of America
| | - Nebal Abu Hussein
- Pulmonary, Critical Care and Sleep Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America
- Department for BioMedical Research DBMR, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Lama Moukheiber
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | | | - Mira Moukheiber
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Dana Moukheiber
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Leo Anthony Celi
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Division of Pulmonary, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Irene Dankwa-Mullan
- Marti Health, Atlanta, Georgia, United States of America
- Department of Health Policy and Management, Milken Institute School of Public Health, The George Washington University, Washington, DC, United States of America
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Nazer LH, Zatarah R, Waldrip S, Ke JXC, Moukheiber M, Khanna AK, Hicklen RS, Moukheiber L, Moukheiber D, Ma H, Mathur P. Bias in artificial intelligence algorithms and recommendations for mitigation. PLOS Digit Health 2023; 2:e0000278. [PMID: 37347721 DOI: 10.1371/journal.pdig.0000278] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 06/24/2023]
Abstract
The adoption of artificial intelligence (AI) algorithms is rapidly increasing in healthcare. Such algorithms may be shaped by various factors such as social determinants of health that can influence health outcomes. While AI algorithms have been proposed as a tool to expand the reach of quality healthcare to underserved communities and improve health equity, recent literature has raised concerns about the propagation of biases and healthcare disparities through implementation of these algorithms. Thus, it is critical to understand the sources of bias inherent in AI-based algorithms. This review aims to highlight the potential sources of bias within each step of developing AI algorithms in healthcare, starting from framing the problem, data collection, preprocessing, development, and validation, as well as their full implementation. For each of these steps, we also discuss strategies to mitigate the bias and disparities. A checklist was developed with recommendations for reducing bias during the development and implementation stages. It is important for developers and users of AI-based algorithms to keep these important considerations in mind to advance health equity for all populations.
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Affiliation(s)
- Lama H Nazer
- Department of Pharmacy, King Hussein Cancer Center, Amman, Jordan
| | - Razan Zatarah
- Department of Pharmacy, King Hussein Cancer Center, Amman, Jordan
| | - Shai Waldrip
- Department of Medicine, Morehouse School of Medicine, Atlanta, Georgia, United States of America
| | - Janny Xue Chen Ke
- Department of Medicine, St. Paul's Hospital, University of British Columbia, Dalhousie University, Vancouver, British Columbia, Canada
| | - Mira Moukheiber
- Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Ashish K Khanna
- Department of Anaesthesiology, Atrium Health Wake Forest Baptist Medical Center, Winston-Salem, North Carolina, United States of America
- Perioperative Outcomes and Informatics Collaborative, Winston-Salem, North Carolina, United States of America
- Outcomes Research Consortium, Cleveland, Ohio, United States of America
| | - Rachel S Hicklen
- Research Medical Library, University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Lama Moukheiber
- Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Dana Moukheiber
- Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Haobo Ma
- Department of Anaesthesia and Critical Care Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America
| | - Piyush Mathur
- Department of Anaesthesia and Critical Care Medicine, Cleveland Clinic, Cleveland, Ohio, United States of America
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Alberto IRI, Alberto NRI, Ghosh AK, Jain B, Jayakumar S, Martinez-Martin N, McCague N, Moukheiber D, Moukheiber L, Moukheiber M, Moukheiber S, Yaghy A, Zhang A, Celi LA. The impact of commercial health datasets on medical research and health-care algorithms. Lancet Digit Health 2023; 5:e288-e294. [PMID: 37100543 PMCID: PMC10155113 DOI: 10.1016/s2589-7500(23)00025-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 12/26/2022] [Accepted: 02/03/2023] [Indexed: 04/28/2023]
Abstract
As the health-care industry emerges into a new era of digital health driven by cloud data storage, distributed computing, and machine learning, health-care data have become a premium commodity with value for private and public entities. Current frameworks of health data collection and distribution, whether from industry, academia, or government institutions, are imperfect and do not allow researchers to leverage the full potential of downstream analytical efforts. In this Health Policy paper, we review the current landscape of commercial health data vendors, with special emphasis on the sources of their data, challenges associated with data reproducibility and generalisability, and ethical considerations for data vending. We argue for sustainable approaches to curating open-source health data to enable global populations to be included in the biomedical research community. However, to fully implement these approaches, key stakeholders should come together to make health-care datasets increasingly accessible, inclusive, and representative, while balancing the privacy and rights of individuals whose data are being collected.
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Affiliation(s)
| | | | - Arnab K Ghosh
- Department of Medicine, Weill Cornell Medical College, Cornell University, New York, NY, USA
| | - Bhav Jain
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | | | - Ned McCague
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Markforged, Watertown, MA, USA
| | - Dana Moukheiber
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Lama Moukheiber
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Mira Moukheiber
- The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Sulaiman Moukheiber
- Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Antonio Yaghy
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA; New England Eye Center, Tufts University Medical Center, Boston, MA, USA
| | - Andrew Zhang
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Leo Anthony Celi
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Biostatistics, Harvard T H Chan School of Public Health, Boston, MA, USA.
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Moukheiber D, Mahindre S, Moukheiber L, Moukheiber M, Wang S, Ma C, Shih G, Peng Y, Gao M. Few-Shot Learning Geometric Ensemble for Multi-label Classification of Chest X-Rays. Data Augment Label Imperfections (2022) 2022; 13567:112-122. [PMID: 36383493 PMCID: PMC9652771 DOI: 10.1007/978-3-031-17027-0_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
This paper aims to identify uncommon cardiothoracic diseases and patterns on chest X-ray images. Training a machine learning model to classify rare diseases with multi-label indications is challenging without sufficient labeled training samples. Our model leverages the information from common diseases and adapts to perform on less common mentions. We propose to use multi-label few-shot learning (FSL) schemes including neighborhood component analysis loss, generating additional samples using distribution calibration and fine-tuning based on multi-label classification loss. We utilize the fact that the widely adopted nearest neighbor-based FSL schemes like ProtoNet are Voronoi diagrams in feature space. In our method, the Voronoi diagrams in the features space generated from multi-label schemes are combined into our geometric DeepVoro Multi-label ensemble. The improved performance in multi-label few-shot classification using the multi-label ensemble is demonstrated in our experiments (The code is publicly available at https://github.com/Saurabh7/Few-shot-learning-multilabel-cxray).
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Affiliation(s)
| | - Saurabh Mahindre
- University at Buffalo, The State University of New York, Buffalo, NY, USA
| | | | | | - Song Wang
- The University of Texas at Austin, Austin, TX, USA
| | - Chunwei Ma
- University at Buffalo, The State University of New York, Buffalo, NY, USA
| | | | - Yifan Peng
- Weill Cornell Medicine, New York, NY, USA
| | - Mingchen Gao
- University at Buffalo, The State University of New York, Buffalo, NY, USA
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Crowson MG, Moukheiber D, Arévalo AR, Lam BD, Mantena S, Rana A, Goss D, Bates DW, Celi LA. A systematic review of federated learning applications for biomedical data. PLOS Digit Health 2022; 1:e0000033. [PMID: 36812504 PMCID: PMC9931322 DOI: 10.1371/journal.pdig.0000033] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 03/30/2022] [Indexed: 11/18/2022]
Abstract
OBJECTIVES Federated learning (FL) allows multiple institutions to collaboratively develop a machine learning algorithm without sharing their data. Organizations instead share model parameters only, allowing them to benefit from a model built with a larger dataset while maintaining the privacy of their own data. We conducted a systematic review to evaluate the current state of FL in healthcare and discuss the limitations and promise of this technology. METHODS We conducted a literature search using PRISMA guidelines. At least two reviewers assessed each study for eligibility and extracted a predetermined set of data. The quality of each study was determined using the TRIPOD guideline and PROBAST tool. RESULTS 13 studies were included in the full systematic review. Most were in the field of oncology (6 of 13; 46.1%), followed by radiology (5 of 13; 38.5%). The majority evaluated imaging results, performed a binary classification prediction task via offline learning (n = 12; 92.3%), and used a centralized topology, aggregation server workflow (n = 10; 76.9%). Most studies were compliant with the major reporting requirements of the TRIPOD guidelines. In all, 6 of 13 (46.2%) of studies were judged at high risk of bias using the PROBAST tool and only 5 studies used publicly available data. CONCLUSION Federated learning is a growing field in machine learning with many promising uses in healthcare. Few studies have been published to date. Our evaluation found that investigators can do more to address the risk of bias and increase transparency by adding steps for data homogeneity or sharing required metadata and code.
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Affiliation(s)
- Matthew G. Crowson
- Department of Otolaryngology-Head & Neck Surgery, Massachusetts Eye & Ear, Boston, Massachusetts, United States of America
- Department of Otolaryngology-Head & Neck Surgery, Harvard Medical School, Massachusetts, United States of America
| | - Dana Moukheiber
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, United States of America
| | - Aldo Robles Arévalo
- IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
- Data & Analytics, NTT DATA Portugal, Lisbon, Portugal
| | - Barbara D. Lam
- Department of Hematology & Oncology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America
| | - Sreekar Mantena
- Harvard College, Boston, Massachusetts, United States of America
| | - Aakanksha Rana
- Massachusetts Institute of Technology, Boston, Massachusetts, United States of America
| | - Deborah Goss
- Department of Otolaryngology-Head & Neck Surgery, Massachusetts Eye & Ear, Boston, Massachusetts, United States of America
| | - David W. Bates
- Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital, Boston, MA, United States of America
- Department of Health Policy and Management, Harvard T. H. Chan School of Public Health, Boston, MA, United States of America
| | - Leo Anthony Celi
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America
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Seastedt KP, Moukheiber D, Mahindre SA, Thammineni C, Rosen DT, Watkins AA, Hashimoto DA, Hoang CD, Kpodonu J, Celi LA. A scoping review of artificial intelligence applications in thoracic surgery. Eur J Cardiothorac Surg 2021; 61:239-248. [PMID: 34601587 DOI: 10.1093/ejcts/ezab422] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 09/16/2021] [Accepted: 09/16/2021] [Indexed: 02/07/2023] Open
Abstract
OBJECTIVES Machine learning (ML) has great potential, but there are few examples of its implementation improving outcomes. The thoracic surgeon must be aware of pertinent ML literature and how to evaluate this field for the safe translation to patient care. This scoping review provides an introduction to ML applications specific to the thoracic surgeon. We review current applications, limitations and future directions. METHODS A search of the PubMed database was conducted with inclusion requirements being the use of an ML algorithm to analyse patient information relevant to a thoracic surgeon and contain sufficient details on the data used, ML methods and results. Twenty-two papers met the criteria and were reviewed using a methodological quality rubric. RESULTS ML demonstrated enhanced preoperative test accuracy, earlier pathological diagnosis, therapies to maximize survival and predictions of adverse events and survival after surgery. However, only 4 performed external validation. One demonstrated improved patient outcomes, nearly all failed to perform model calibration and one addressed fairness and bias with most not generalizable to different populations. There was a considerable variation to allow for reproducibility. CONCLUSIONS There is promise but also challenges for ML in thoracic surgery. The transparency of data and algorithm design and the systemic bias on which models are dependent remain issues to be addressed. Although there has yet to be widespread use in thoracic surgery, it is essential thoracic surgeons be at the forefront of the eventual safe introduction of ML to the clinic and operating room.
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Affiliation(s)
- Kenneth P Seastedt
- Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Dana Moukheiber
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Saurabh A Mahindre
- Institute for Computational and Data Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Chaitanya Thammineni
- HILS Laboratory, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Darin T Rosen
- Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Ammara A Watkins
- Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Daniel A Hashimoto
- Surgical AI & Innovation Laboratory, Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Chuong D Hoang
- Thoracic Surgery Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jacques Kpodonu
- Division of Cardiac Surgery, Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Leo A Celi
- Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
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Ji Z, Shaikh MA, Moukheiber D, Srihari SN, Peng Y, Gao M. Improving Joint Learning of Chest X-Ray and Radiology Report by Word Region Alignment. Mach Learn Med Imaging 2021; 12966:110-119. [PMID: 35647616 DOI: 10.1007/978-3-030-87589-3_12] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Self-supervised learning provides an opportunity to explore unlabeled chest X-rays and their associated free-text reports accumulated in clinical routine without manual supervision. This paper proposes a Joint Image Text Representation Learning Network (JoImTeRNet) for pre-training on chest X-ray images and their radiology reports. The model was pre-trained on both the global image-sentence level and the local image region-word level for visual-textual matching. Both are bidirectionally constrained on Cross-Entropy based and ranking-based Triplet Matching Losses. The region-word matching is calculated using the attention mechanism without direct supervision about their mapping. The pre-trained multi-modal representation learning paves the way for downstream tasks concerning image and/or text encoding. We demonstrate the representation learning quality by cross-modality retrievals and multi-label classifications on two datasets: OpenI-IU and MIMIC-CXR. Our code is available at https://github.com/mshaikh2/JoImTeR_MLMI_2021.
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Affiliation(s)
- Zhanghexuan Ji
- Department of Computer Science and Engineering, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Mohammad Abuzar Shaikh
- Department of Computer Science and Engineering, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Dana Moukheiber
- Department of Computer Science and Engineering, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Sargur N Srihari
- Department of Computer Science and Engineering, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Yifan Peng
- Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Mingchen Gao
- Department of Computer Science and Engineering, University at Buffalo, The State University of New York, Buffalo, NY, USA
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Cosgriff CV, Charpignon M, Moukheiber D, Lough ME, Gichoya J, Stone DJ, Celi LA. Village mentoring and hive learning: The MIT Critical Data experience. iScience 2021; 24:102656. [PMID: 34169236 PMCID: PMC8209268 DOI: 10.1016/j.isci.2021.102656] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Affiliation(s)
- Christopher V Cosgriff
- Department of Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Marie Charpignon
- MIT Institute for Data, Systems, and Society, Cambridge, MA 02138, USA
| | | | - Mary E Lough
- Primary Care and Population Health, Stanford University, Stanford, CA 94305, USA
| | | | - David J Stone
- Departments of Anesthesiology and Neurosurgery, and the Center for Advanced Medical Analytics, University of Virginia, Charlottesville, VA 22908, USA
| | - Leo Anthony Celi
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA 02138, USA.,Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA 02215
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Moukheiber D, Chitgupi U, Carter KA, Luo D, Sun B, Goel S, Ferreira CA, Engle JW, Wang D, Geng J, Zhang Y, Xia J, Cai W, Lovell JF. Surfactant-Stripped Pheophytin Micelles for Multimodal Tumor Imaging and Photodynamic Therapy. ACS Appl Bio Mater 2018; 2:544-554. [PMID: 31853516 DOI: 10.1021/acsabm.8b00703] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Porphyrin-based nanomaterials can inherently integrate multiple contrast imaging functionalities with phototherapeutic capabilities. We dispersed pheophytin (Pheo) into Pluronic F127 and carried out low-temperature surfactant-stripping to remove the bulk surfactant. Surfactant-stripped Pheo (ss-Pheo) micelles exhibited a similar size, but higher near-infrared fluorescence, compared to two other nanomaterials also with high porphyrin density (surfactant-stripped chlorophyll micelles and porphysomes). Singlet oxygen generation, which was higher for ss-Pheo, enabled photodynamic therapy (PDT). ss-Pheo provided contrast for photoacoustic and fluorescence imaging, and following seamless labeling with 64Cu, was used for positron emission tomography. ss-Pheo had a long blood circulation and favorable accumulation in an orthotopic murine mammary tumor model. Trimodal tumor imaging was demonstrated, and PDT resulted in delayed tumor growth.
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Affiliation(s)
- Dana Moukheiber
- Department of Biomedical Engineering, University at Buffalo, State University of New York, Buffalo, New York 14260, United States
| | - Upendra Chitgupi
- Department of Biomedical Engineering, University at Buffalo, State University of New York, Buffalo, New York 14260, United States
| | - Kevin A Carter
- Department of Biomedical Engineering, University at Buffalo, State University of New York, Buffalo, New York 14260, United States
| | - Dandan Luo
- Department of Biomedical Engineering, University at Buffalo, State University of New York, Buffalo, New York 14260, United States
| | - Boyang Sun
- Department of Biomedical Engineering, University at Buffalo, State University of New York, Buffalo, New York 14260, United States
| | - Shreya Goel
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin 53705, United States
| | - Carolina A Ferreira
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin 53705, United States
| | - Jonathan W Engle
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin 53705, United States
| | - Depeng Wang
- Department of Biomedical Engineering, University at Buffalo, State University of New York, Buffalo, New York 14260, United States
| | - Jumin Geng
- Department of Biomedical Engineering, University at Buffalo, State University of New York, Buffalo, New York 14260, United States
| | - Yumiao Zhang
- Department of Biomedical Engineering, University at Buffalo, State University of New York, Buffalo, New York 14260, United States
| | - Jun Xia
- Department of Biomedical Engineering, University at Buffalo, State University of New York, Buffalo, New York 14260, United States
| | - Weibo Cai
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin 53705, United States
| | - Jonathan F Lovell
- Department of Biomedical Engineering, University at Buffalo, State University of New York, Buffalo, New York 14260, United States
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