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  1. Article ; Online: Understanding New Machine Learning Architectures: Practical Generative Artificial Intelligence for Anesthesiologists.

    Connor, Christopher W

    Anesthesiology

    2024  Volume 140, Issue 3, Page(s) 599–609

    Abstract: Recent advances in neural networks have given rise to generative artificial intelligence, systems able to produce fluent responses to natural questions or attractive and even photorealistic images from text prompts. These systems were developed through ... ...

    Abstract Recent advances in neural networks have given rise to generative artificial intelligence, systems able to produce fluent responses to natural questions or attractive and even photorealistic images from text prompts. These systems were developed through new network architectures that permit massive computational resources to be applied efficiently to enormous data sets. First, this review examines autoencoder architecture and its derivatives the variational autoencoder and the U-Net in annotating and manipulating images and extracting salience. This architecture will be important for applications like automated x-ray interpretation or real-time highlighting of anatomy in ultrasound images. Second, this article examines the transformer architecture in the interpretation and generation of natural language, as it will be useful in producing automated summarization of medical records or performing initial patient screening. The author also applies the GPT-3.5 algorithm to example questions from the American Board of Anesthesiologists Basic Examination and find that, under surprisingly reasonable conditions, it correctly answers more than half the questions.
    MeSH term(s) Humans ; Artificial Intelligence ; Anesthesiologists ; Machine Learning ; Neural Networks, Computer ; Algorithms
    Language English
    Publishing date 2024-02-13
    Publishing country United States
    Document type Review ; Journal Article
    ZDB-ID 269-0
    ISSN 1528-1175 ; 0003-3022
    ISSN (online) 1528-1175
    ISSN 0003-3022
    DOI 10.1097/ALN.0000000000004841
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: In Response.

    Connor, Christopher W

    Anesthesia and analgesia

    2023  Volume 136, Issue 6, Page(s) e35–e36

    Language English
    Publishing date 2023-05-19
    Publishing country United States
    Document type Letter ; Comment
    ZDB-ID 80032-6
    ISSN 1526-7598 ; 0003-2999
    ISSN (online) 1526-7598
    ISSN 0003-2999
    DOI 10.1213/ANE.0000000000006434
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: In Response.

    Connor, Christopher W

    Anesthesia and analgesia

    2023  Volume 136, Issue 5, Page(s) e22–e24

    Language English
    Publishing date 2023-04-14
    Publishing country United States
    Document type Letter ; Comment
    ZDB-ID 80032-6
    ISSN 1526-7598 ; 0003-2999
    ISSN (online) 1526-7598
    ISSN 0003-2999
    DOI 10.1213/ANE.0000000000006431
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Open Reimplementation of the BIS Algorithms for Depth of Anesthesia.

    Connor, Christopher W

    Anesthesia and analgesia

    2022  Volume 135, Issue 4, Page(s) 855–864

    Abstract: Background: BIS (a brand of processed electroencephalogram [EEG] depth-of-anesthesia monitor) scores have become interwoven into clinical anesthesia care and research. Yet, the algorithms used by such monitors remain proprietary. We do not actually know ...

    Abstract Background: BIS (a brand of processed electroencephalogram [EEG] depth-of-anesthesia monitor) scores have become interwoven into clinical anesthesia care and research. Yet, the algorithms used by such monitors remain proprietary. We do not actually know what we are measuring. If we knew, we could better understand the clinical prognostic significance of deviations in the score and make greater research advances in closed-loop control or avoiding postoperative cognitive dysfunction or juvenile neurological injury. In previous work, an A-2000 BIS monitor was forensically disassembled and its algorithms (the BIS Engine) retrieved as machine code. Development of an emulator allowed BIS scores to be calculated from arbitrary EEG data for the first time. We now address the fundamental questions of how these algorithms function and what they represent physiologically.
    Methods: EEG data were obtained during induction, maintenance, and emergence from 12 patients receiving customary anesthetic management for orthopedic, general, vascular, and neurosurgical procedures. These data were used to trigger the closely monitored execution of the various parts of the BIS Engine, allowing it to be reimplemented in a high-level language as an algorithm entitled ibis. Ibis was then rewritten for concision and physiological clarity to produce a novel completely clear-box depth-of-anesthesia algorithm titled openibis .
    Results: The output of the ibis algorithm is functionally indistinguishable from the native BIS A-2000, with r = 0.9970 (0.9970-0.9971) and Bland-Altman mean difference between methods of -0.25 ± 2.6 on a unitless 0 to 100 depth-of-anesthesia scale. This precision exceeds the performance of any earlier attempt to reimplement the function of the BIS algorithms. The openibis algorithm also matches the output of the native algorithm very closely ( r = 0.9395 [0.9390-0.9400], Bland-Altman 2.62 ± 12.0) in only 64 lines of readable code whose function can be unambiguously related to observable features in the EEG signal. The operation of the openibis algorithm is described in an intuitive, graphical form.
    Conclusions: The openibis algorithm finally provides definitive answers about the BIS: the reliance of the most important signal components on the low-gamma waveband and how these components are weighted against each other. Reverse engineering allows these conclusions to be reached with a clarity and precision that cannot be obtained by other means. These results contradict previous review articles that were believed to be authoritative: the BIS score does not appear to depend on a bispectral index at all. These results put clinical anesthesia research using depth-of-anesthesia scores on a firm footing by elucidating their physiological basis and enabling comparison to other animal models for mechanistic research.
    MeSH term(s) Algorithms ; Anesthesia ; Anesthesiology ; Anesthetics ; Consciousness Monitors ; Electroencephalography
    Chemical Substances Anesthetics
    Language English
    Publishing date 2022-06-27
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 80032-6
    ISSN 1526-7598 ; 0003-2999
    ISSN (online) 1526-7598
    ISSN 0003-2999
    DOI 10.1213/ANE.0000000000006119
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Emulation of the BIS engine.

    Connor, Christopher W

    Journal of clinical monitoring and computing

    2021  Volume 36, Issue 2, Page(s) 483–492

    Abstract: The operation of the BIS monitor remains undescribed, despite 20 years of clinical use and 3000 academic articles. The core algorithmic software (the BIS Engine) can be retrieved from the motherboard of the A-2000 monitor in binary form through forensic ... ...

    Abstract The operation of the BIS monitor remains undescribed, despite 20 years of clinical use and 3000 academic articles. The core algorithmic software (the BIS Engine) can be retrieved from the motherboard of the A-2000 monitor in binary form through forensic disassembly using debugging interfaces left in place by the original designers, opening the possibility of executing the BIS algorithms on contemporary computers through emulation. Three steps were required for emulation. Firstly, the monitor input stage monitor was disassembled to determine how EEG signals can be compatibly presented to the Engine. Secondly, the Digital Signal Processor on which the Engine executes was recreated in software. Thirdly, the Engine code was patched, allowing execution separated from monitor hardware. Code performance under noise load was evaluated. EEG signals and BIS variables were obtained from a 13-year-old child in normal physiological sleep using a modern BIS monitor. BIS values in sleeping children exhibit a wide dynamic range, including values nominally associated with clinical anesthesia, providing a risk-free technique to obtain empirical EEG data that broadly exercise the algorithms. Emulation demonstrated a correlation coefficient of R = 0.943, consistent with correlations between official Engine iterations. Additive white noise in the EEG caused a progressive lifting and flattening of BIS values. Emulation replicates BIS Engine behavior, allowing calculation upon existing EEG datasets or signals from other, potentially remote or wireless, devices. Emulation provides advantages for elucidating the mathematical expression of the algorithms, which remain important as practical constraints on any hypothetical mechanism of action of anesthetics.
    MeSH term(s) Adolescent ; Anesthesia ; Anesthetics ; Child ; Electroencephalography/methods ; Humans ; Monitoring, Intraoperative/methods
    Chemical Substances Anesthetics
    Language English
    Publishing date 2021-03-19
    Publishing country Netherlands
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 1418733-4
    ISSN 1573-2614 ; 1387-1307 ; 0748-1977
    ISSN (online) 1573-2614
    ISSN 1387-1307 ; 0748-1977
    DOI 10.1007/s10877-021-00676-2
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Visual assessment of effectiveness of ultrasound probe hygiene using ultraviolet fluorescent powder: a pilot study.

    Gerner, Philipp / Connor, Christopher W / Stone, Alexander B

    Regional anesthesia and pain medicine

    2023  

    Language English
    Publishing date 2023-06-01
    Publishing country England
    Document type Journal Article
    ZDB-ID 1425299-5
    ISSN 1532-8651 ; 1098-7339 ; 0146-521X
    ISSN (online) 1532-8651
    ISSN 1098-7339 ; 0146-521X
    DOI 10.1136/rapm-2023-104632
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  7. Article ; Online: A Forensic Disassembly of the BIS Monitor.

    Connor, Christopher W

    Anesthesia and analgesia

    2020  Volume 131, Issue 6, Page(s) 1923–1933

    Abstract: Background: The bispectral index (BIS) monitor has been available for clinical use for >20 years and has had an immense impact on academic activity in Anesthesiology, with >3000 articles referencing the bispectral index. Despite attempts to infer its ... ...

    Abstract Background: The bispectral index (BIS) monitor has been available for clinical use for >20 years and has had an immense impact on academic activity in Anesthesiology, with >3000 articles referencing the bispectral index. Despite attempts to infer its algorithms by external observation, its operation has nevertheless remained undescribed, in contrast to the algorithms of other less commercially successful monitors of electroencephalogram (EEG) activity under anesthesia. With the expiration of certain key patents, the time is therefore ripe to examine the operation of the monitor on its own terms through careful dismantling, followed by extraction and examination of its internal software.
    Methods: An A-2000 BIS Monitor (gunmetal blue case, amber monochrome display) was purchased on the secondary market. After identifying the major data processing and storage components, a set of free or inexpensive tools was used to retrieve and disassemble the monitor's onboard software. The software executes primarily on an ARMv7 microprocessor (Sharp/NXP LH77790B) and a digital signal processor (Texas Instruments TMS320C32). The device software can be retrieved directly from the monitor's hardware by using debugging interfaces that have remained in place from its original development.
    Results: Critical numerical parameters such as the spectral edge frequency (SEF), total power, and BIS values were retraced from external delivery at the device's serial port back to the point of their calculation in the extracted software. In doing so, the locations of the critical algorithms were determined. To demonstrate the validity of the technique, the algorithms for SEF and total power were disassembled, comprehensively annotated and compared to their theoretically ideal behaviors. A bug was identified in the device's implementation of the SEF algorithm, which can be provoked by a perfectly isoelectric EEG.
    Conclusions: This article demonstrates that the electronic design of the A-2000 BIS Monitor does not pose any insuperable obstacles to retrieving its device software in hexadecimal machine code form directly from the motherboard. This software can be reverse engineered through disassembly and decompilation to reveal the methods by which the BIS monitor implements its algorithms, which ultimately must form the definitive statement of its function. Without further revealing any algorithms that might be considered trade secrets, the manufacturer of the BIS monitor should be encouraged to release the device software in its original format to place BIS-related academic literature on a firm theoretical foundation and to promote further academic development of EEG monitoring algorithms.
    MeSH term(s) Anesthesiology/instrumentation ; Anesthesiology/trends ; Biomedical Engineering/instrumentation ; Biomedical Engineering/trends ; Consciousness Monitors/trends ; Electroencephalography/instrumentation ; Electroencephalography/trends ; Equipment Design/trends ; Humans ; Monitoring, Intraoperative/instrumentation ; Monitoring, Intraoperative/trends
    Language English
    Publishing date 2020-10-22
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 80032-6
    ISSN 1526-7598 ; 0003-2999
    ISSN (online) 1526-7598
    ISSN 0003-2999
    DOI 10.1213/ANE.0000000000005220
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Artificial Intelligence and Machine Learning in Anesthesiology.

    Connor, Christopher W

    Anesthesiology

    2019  Volume 131, Issue 6, Page(s) 1346–1359

    Abstract: Commercial applications of artificial intelligence and machine learning have made remarkable progress recently, particularly in areas such as image recognition, natural speech processing, language translation, textual analysis, and self-learning. ... ...

    Abstract Commercial applications of artificial intelligence and machine learning have made remarkable progress recently, particularly in areas such as image recognition, natural speech processing, language translation, textual analysis, and self-learning. Progress had historically languished in these areas, such that these skills had come to seem ineffably bound to intelligence. However, these commercial advances have performed best at single-task applications in which imperfect outputs and occasional frank errors can be tolerated.The practice of anesthesiology is different. It embodies a requirement for high reliability, and a pressured cycle of interpretation, physical action, and response rather than any single cognitive act. This review covers the basics of what is meant by artificial intelligence and machine learning for the practicing anesthesiologist, describing how decision-making behaviors can emerge from simple equations. Relevant clinical questions are introduced to illustrate how machine learning might help solve them-perhaps bringing anesthesiology into an era of machine-assisted discovery.
    MeSH term(s) Algorithms ; Anesthesiology/methods ; Anesthesiology/trends ; Artificial Intelligence/trends ; Humans ; Machine Learning/trends
    Language English
    Publishing date 2019-04-11
    Publishing country United States
    Document type Journal Article ; Review
    ZDB-ID 269-0
    ISSN 1528-1175 ; 0003-3022
    ISSN (online) 1528-1175
    ISSN 0003-3022
    DOI 10.1097/ALN.0000000000002694
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Measures of Information Content during Anesthesia and Emergence in the Caenorhabditis elegans Nervous System.

    Chang, Andrew S / Wirak, Gregory S / Li, Duan / Gabel, Christopher V / Connor, Christopher W

    Anesthesiology

    2023  Volume 139, Issue 1, Page(s) 49–62

    MeSH term(s) Animals ; Humans ; Isoflurane/pharmacology ; Caenorhabditis elegans ; Anesthetics, Inhalation/pharmacology ; Anesthesia ; Neurons
    Chemical Substances Isoflurane (CYS9AKD70P) ; Anesthetics, Inhalation
    Language English
    Publishing date 2023-04-05
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 269-0
    ISSN 1528-1175 ; 0003-3022
    ISSN (online) 1528-1175
    ISSN 0003-3022
    DOI 10.1097/ALN.0000000000004579
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Commentary: Tumor biology remains the star of the show.

    O'Connor, Michael C / Seder, Christopher W

    The Journal of thoracic and cardiovascular surgery

    2022  Volume 165, Issue 3, Page(s) 898–899

    MeSH term(s) Humans ; Cell Line, Tumor ; Biology ; Phosphoproteins
    Chemical Substances Phosphoproteins
    Language English
    Publishing date 2022-09-03
    Publishing country United States
    Document type Editorial ; Comment
    ZDB-ID 3104-5
    ISSN 1097-685X ; 0022-5223
    ISSN (online) 1097-685X
    ISSN 0022-5223
    DOI 10.1016/j.jtcvs.2022.08.022
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