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Otolaryngology–Head and Neck Surgery 1998年第6期118卷 751–761页

作者： KENNETH R. WHITTEMORE, Jr, MS SAUMIL N. MERCHANT, MD JOHN J. ROSOWSKI, PhD Boston and Cambridge MassachusettsFrom the Department of Otolaryngology (Drs. Whittemore Merchant and Rosowski) the Eaton-Peabody Laboratory of Auditory Physiology (Drs. Whittemore Merchant and Rosowski) Massachusetts Eye and Ear Infirmary the Department of Otology and Laryngology (Drs. Whittemore Merchant and Rosowski) Harvard Medical School and the Speech and Hearing Sciences Program (Drs. Merchant and Rosowski) Harvard/MIT Division of Health Sciences and Technology.

The contribution of the middle ear air spaces to sound transmission through the middle ear in canal wall-up and canal wall-down mastoidectomy was studied in human temporal bones by measurements of middle ear input impedance and sound pressure difference across the tympanic membrane for the frequency range 50 Hz to 5 kHz. These measurements indicate that, relative to canal wall-up procedures, canal wall-down mastoidectomy results in a 1 to 5 dB decrease in middle ear sound transmission below 1 kHz, a 0 to 10 dB increase between 1 and 3 kHz, and no change above 3 kHz. These results are consistent with those reported by Gyo et al. (Arch Otolaryngol Head Neck Surg 1986;112:1262-8), in which umbo displacement was used as a measure of sound transmission. A model analysis suggests that the reduction in sound transmission below 1 kHz can be explained by the smaller middle ear air space volume associated with the canal wall-down procedure. We conclude that as long as the middle ear air space is aerated and has a volume greater than 0.7 ml, canal wall-down mastoidectomy should generally cause less than 10 dB changes in middle ear sound transmission relative to the canal wall-up procedure. (Otolaryngol Head Neck Surg 1998;118:751-61.)

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Designing enhanced classifiers using prior process knowledge: Regularized maximum-likelihood

Designing enhanced classifiers using prior process knowledge...

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IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)

作者： Mohammad Shahrokh Esfahani Amin Zollanvari Byung-Jun Yoon Edward R. Dougherty Department of Electrical and Computer Engineering Texas A and M University USA Childrens Hospital Informatics Program at Harvard-MIT Division of Health Sciences and Technology Harvard Medical School and Brigham and Women's Hospital USA Computational Biology Division Translational Genomics Research Institute Phoenix Phoenix AZ USA

We propose a novel optimization-based paradigm for designing enhanced classifiers. The proposed paradigm allows us to incorporate available prior process knowledge into classifier design, thereby improving the performance of the resulting classifiers. In this work, we focus on dynamical systems that can be represented as finite-state multi-dimensional stochastic processes that possess labeled steady-state distributions. Given prior operational knowledge of the process, our goal is to build a classifier that can accurately label future observations obtained from the steady-state, by utilizing both the available prior knowledge and the training data. Simulation results show that the proposed paradigm yields improved classifiers that outperform traditional classifiers that use only training data.

关键词： Uncertainty Knowledge engineering Steady-state Training data Bioinformatics Cancer Genomics

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Metal-based porous hydrogels for highly conductive biomaterial scaffolds (vol 3, itad002, 2023)

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OXFORD OPEN MATERIALS SCIENCE 2023年第1期3卷 itad002页

作者： Tringides, Christina M. Boulingre, Marjolaine Mooney, David J. Program in Biophysics Harvard University Cambridge MA 02138 USA. Wyss Institute for Biologically Inspired Engineering Harvard University Cambridge MA 02115 USA. John A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge MA 02138 USA. Harvard-MIT Division in Health Sciences and Technology Massachusetts Institute of Technology Cambridge MA 02142 USA. Department of Bioengineering Imperial College London London SW7 2BP United Kingdom.

Multielectrode arrays are fabricated from thin films of highly conductive and ductile metals which cannot mimic the natural environment of biological tissues. These properties limit the conformability of the electrode to the underlying target tissue, and present challenges in developing seamless interfaces. By introducing porous, hydrogel materials that are embedded with metal additives, highly conductive hydrogels can be formed. Tuning the hydrogel composition, % volume and aspect ratio of different additive(s), and the processing conditions of these composite materials can alter the mechanical and electrical properties. The resulting materials have a high surface area, and can be used as biomaterial scaffolds to support the growth of macrophages for 5 days. Further optimization can enable the use of the materials for the electrodes in implantable arrays, or as living electrode platforms to study and modulate various cellular cultures. These advancements would benefit both and applications of tissue engineering.

关键词： 3D biology biohybrid electronics biomaterials composites conductive scaffolds hydrogels tissue engineering

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Joint cortical registration of geometry and function using semi-supervised learning

arXiv

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arXiv 2023年

作者： Li, Jian Tuckute, Greta Fedorenko, Evelina Edlow, Brian L. Fischl, Bruce Dalca, Adrian V. A. A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital Harvard Medical School United States Center for Neurotechnology and Neurorecovery Department of Neurology Massachusetts General Hospital Harvard Medical School United States Department of Brain and Cognitive Sciences Massachusetts Institute of Technology United States McGovern Institute for Brain Research Massachusetts Institute of Technology United States Program in Speech Hearing Bioscience and Technology Harvard University United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States Harvard-MIT Program in Health Sciences and Technology United States

Brain surface-based image registration, an important component of brain image analysis, establishes spatial correspondence between cortical surfaces. Existing iterative and learning-based approaches focus on accurate registration of folding patterns of the cerebral cortex, and assume that geometry predicts function and thus functional areas will also be well aligned. However, structure/functional variability of anatomically corresponding areas across subjects has been widely reported. In this work, we introduce a learning-based cortical registration framework, JOSA, which jointly aligns folding patterns and functional maps while simultaneously learning an optimal atlas. We demonstrate that JOSA can substantially improve registration performance in both anatomical and functional domains over existing methods. By employing a semi-supervised training strategy, the proposed framework obviates the need for functional data during inference, enabling its use in broad neuroscientific domains where functional data may not be observed. The source code of JOSA will be released to the public at https://***. © 2023, CC BY.

关键词： Iterative methods

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Digital holographic otoscope for the study of biomechanical properties of eardrum

Digital holographic otoscope for the study of biomechanical ...

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Digital Holography and Three-Dimensional Imaging, DH 2012

作者： Flores-Moreno, J.M. Mendoza-Santoyo, F. Furlong, Cosme Rosowski, J.J. Centro de Investigaciones en Optica A. C. Loma del Bosque 115 Leon Gto 37150 Mexico Center for Holographic Studies and Laser micro-mechaTronics Worcester Polytechnic Institute 100 Institute Rd Worcester MA 01609 United States Eaton-Peabody Laboratory Massachusetts Eye and Ear Infirmary 234 Charles Street Boston MA 02114 United States Speech and Hearing Bioscience and Technology Program MIT-Harvard Division of Health Sciences and Technology 77 Massachusetts Av Cambridge MA 02139 United States

A Digital Holographic system (by phase-stepping, single hologram techniques) is used at clinic to study biomechanics properties of tympanic membranes (TM);differences between the DH techniques used for digital recordi... 详细信息

ISBN: (纸本)9781557529428

关键词： Holograms

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Artificial Intelligence for Digital and Computational Pathology

arXiv

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arXiv 2023年

作者： Song, Andrew H. Jaume, Guillaume Williamson, Drew F.K. Lu, Ming Y. Vaidya, Anurag Miller, Tiffany R. Mahmood, Faisal Department of Pathology Brigham and Women's Hospital Harvard Medical School BostonMA United States Department of Pathology Massachusetts General Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States Harvard Medical School BostonMA United States

Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence, including deep learning, have boosted the field of computational pathology. This field holds tremendous potential to automate clinical diagnosis, predict patient prognosis and response to therapy, and discover new morphological biomarkers from tissue images. Some of these artificial intelligence-based systems are now getting approved to assist clinical diagnosis;however, technical barriers remain for their widespread clinical adoption and integration as a research tool. This Review consolidates recent methodological advances in computational pathology for predicting clinical end points in whole-slide images and highlights how these developments enable the automation of clinical practice and the discovery of new biomarkers. We then provide future perspectives as the field expands into a broader range of clinical and research tasks with increasingly diverse modalities of clinical data. © 2023, CC BY.

关键词： Pathology

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Predicting septic shock outcomes in a database with missing data using fuzzy modeling: Influence of pre-processing techniques on real-world data-based classification

Predicting septic shock outcomes in a database with missing ...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Rúben D. M. A. Pereira André S. Fialho Federico Cismondi Susana M. Vieira João M. C. Sousa Rui J. Almeida Uzay Kaymak Shane R. Reti Michael D. Howell Stan N. Finkelstein MIT-Portugal Program Bio-Engineering Systems MIT / HMS / IST Lisbon MA Portugal Erasmus School of Economics Econometric Institute Erasmus University Rotterdam Rotterdam The Netherlands Dept. of Mechanical Engineering CIS/IDMEC-LAETA Instituto Superior Técnico (IST) Lisbon Portugal Beth Israel Deaconess Medical Center Harvard Medical School (HMS) Boston MA USA Engineering Systems Division and Harvard-MIT Division of Health Sciences & Technology Massachusetts Institute of Technology (MIT) Cambridge MA USA

Real-world databases often contain missing data and existing correction algorithms deliver varying performance. Also, most modeling techniques are not suitable to deal with them automatically. In this study we examine different approaches to predicting septic shock in the presence of missing data. Some preprocessing techniques for managing missing data include disregarding data, or replacing it with information that by design introduces bias. In this study, we show that predictive performance improves by employing a minimum pre-processing technique, the Zero-Order-Hold (ZOH) method, by applying a Fuzzy C-Means clustering technique based on the partial distance calculation strategy (FCM-PDS) and by computing the final classification regarding the samples from each patient. Performance improvements continue to occur where up to approximately 60% of the data is missing, though for higher percentage the classification performance still is statistically improved. We further validate this approach by making comparisons with previous studies.

关键词： Electric shock Databases Sensitivity and specificity Accuracy Data models Predictive models Sensitivity

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Adaptative reconstruction distance in a lensless digital holographic otoscope

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2011 SEM Annual Conference on Experimental and Applied Mechanics

作者： Flores-Moreno, J.M. Furlong, Cosme Rosowski, John J. Center for Holographic Studies and Laser Micro-mechaTronics Worcester Polytechnic Institute 100 Institute Rd Worcester MA 01609 United States Eaton-Peabody Laboratory Massachusetts Eye and Ear Infirmary 234 Charles Street Boston MA 02114 United States Speech and Hearing Bioscience and Technology Program MIT-Harvard Division of Health Sciences and Technology 77 Massachusetts Av. Cambridge MA 02139 United States Centro de Investigaciones en Optica A. C. Loma del Bosque 115 Leon Gto 37150 Mexico

ISBN: (纸本)9781461402275

We are developing a Digital Optoelectronic Holographic System (DOEHS) for measurements of shape and deformations acoustically induced of the human tympanic membrane (TM) in the clinic. Such measurements will be used to perform quantitative diagnosis of the middle-ear. The DOEHS platform consists of laser-delivery illumination (IS), optical head (OH), image-processing computer (IP), and positioning arm (PS) subsystems. Particularly, the OH of the DOEHS has been configured as an in-line, lensless, holographic arrangement to quantify deformations of the TM when it is subjected to controlled sound excitation. Holographic information is recorded by a digital camera and reconstructed numerically by the Fresnel integral approximation. Accurate measurements are achieved when TM samples are imaged within the depth of focus of the OH, which requires the selection of the optimal numerical reconstruction distance. In this paper, we discuss an experimental approximation to identify the best reconstruction distance based on the position of the reference beam (RB) within the OH and its associated phase factor within the Fresnel integral approximation. Results of these investigations are being used to further optimize the OH design of the DOEHS system, and to improve the numerical reconstruction algorithms used. Representative experimental measurements are shown.

关键词： Deformation

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Smartphone-based detection of voice disorders by long-term monitoring of neck acceleration features

Smartphone-based detection of voice disorders by long-term m...

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International Workshop on Wearable and Implantable Body Sensor Networks (BSN)

作者： Daryush D. Mehta Matías Zañartu Jarrad H. Van Stan Shengran W. Feng Harold A. Cheyne Robert E. Hillman Department of Surgery Harvard Medical School Boston Massachusetts Center for Laryngeal Surgery & Voice Rehabilitation Massachusetts General Hospital Boston Massachusetts Department of Electronic Engineering Universidad Técnica Federico Santa María Valparaíso Chile Communication Sciences and Disorders MGH Institute of Health Professions Boston Massachusetts Harvard-MIT Division of Health Sciences & Technology Massachusetts Institute of Technology Cambridge MA Bioacoustic Research Program Cornell University Ithaca New York USA

ISBN: (纸本)9781479903313

Many common voice disorders are chronic or recurring conditions that are likely to result from inefficient and/or abusive patterns of vocal behavior, termed vocal hyperfunction. Thus an ongoing goal in clinical voice assessment is the long-term monitoring of noninvasively derived measures to track hyperfunction. This paper reports on a smartphone-based voice health monitor that records the high-bandwidth accelerometer signal from the neck skin above the collarbone. Data collection is under way from patients with vocal hyperfunction and matched-control subjects to create a dataset designed to identify the best set of diagnostic measures for hyperfunctional patterns of vocal behavior. Vocal status is tracked from neck acceleration using previously-developed vocal dose measures and novel model-based features of glottal airflow estimates. Clinically, the treatment of hyperfunctional disorders would be greatly enhanced by the ability to unobtrusively monitor and quantify detrimental behaviors and, ultimately, to provide real-time biofeedback that could facilitate healthier voice use.

关键词： Monitoring Accelerometers Neck Atmospheric modeling Calibration Time measurement Skin

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A General-Purpose Self-Supervised Model for Computational Pathology

arXiv

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arXiv 2023年

作者： Chen, Richard J. Ding, Tong Lu, Ming Y. Williamson, Drew F.K. Jaume, Guillaume Chen, Bowen Zhang, Andrew Shao, Daniel Song, Andrew H. Shaban, Muhammad Williams, Mane Vaidya, Anurag Sahai, Sharifa Oldenburg, Lukas Weishaupt, Luca L. Wang, Judy J. Williams, Walt Le, Long Phi Gerber, Georg Mahmood, Faisal Department of Pathology Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Pathology Massachusetts General Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard MIT CambridgeMA United States Cancer Data Science Program Dana-Farber Cancer Institute BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States CambridgeMA United States Health Sciences and Technology Harvard-MIT CambridgeMA United States Harvard John A. Paulson School of Engineering And Applied Sciences Harvard University CambridgeMA United States Department of Systems Biology Harvard University CambridgeMA United States Harvard Data Science Initiative Harvard University CambridgeMA United States

Tissue phenotyping is a fundamental computational pathology (CPath) task in learning objective characterizations of histopathologic biomarkers in anatomic pathology. However, whole-slide imaging (WSI) poses a complex computer vision problem in which the large-scale image resolutions of WSIs and the enormous diversity of morphological phenotypes preclude large-scale data annotation. Current efforts have proposed using pretrained image encoders with either transfer learning from natural image datasets or self-supervised pretraining on publicly-available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale. We introduce UNI, a general-purpose self-supervised model for pathology, pretrained using over 100 million tissue patches from over 100,000 diagnostic haematoxylin and eosin-stained WSIs across 20 major tissue types, and evaluated on 33 representative CPath clinical tasks in CPath of varying diagnostic difficulties. In addition to outperforming previous state-of-the-art models, we demonstrate new modeling capabilities in CPath such as resolution-agnostic tissue classification, slide classification using few-shot class prototypes, and disease subtyping generalization in classifying up to 108 cancer types in the OncoTree code classification system. UNI advances unsupervised representation learning at scale in CPath in terms of both pretraining data and downstream evaluation, enabling data-efficient AI models that can generalize and transfer to a gamut of diagnostically-challenging tasks and clinical workflows in anatomic pathology. Copyright © 2023, The Authors. All rights reserved.

关键词： Pathology

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