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Schrott J, Affortunati S, Stadler C, Hintermüller C. DEIT-Based Bone Position and Orientation Estimation for Robotic Support in Total Knee Arthroplasty-A Computational Feasibility Study. SENSORS (BASEL, SWITZERLAND) 2024; 24:5269. [PMID: 39204964 PMCID: PMC11359506 DOI: 10.3390/s24165269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 08/09/2024] [Accepted: 08/13/2024] [Indexed: 09/04/2024]
Abstract
Total knee arthroplasty (TKA) is a well-established and successful treatment option for patients with end-stage osteoarthritis of the knee, providing high patient satisfaction. Robotic systems have been widely adopted to perform TKA in orthopaedic centres. The exact spatial positions of the femur and tibia are usually determined through pinned trackers, providing the surgeon with an exact illustration of the axis of the lower limb. The drilling of holes required for mounting the trackers creates weak spots, causing adverse events such as bone fracture. In the presented computational feasibility study, time differential electrical impedance tomography is used to locate the femur positions, thereby the difference in conductivity distribution between two distinct states s0 and s1 of the measured object is reconstructed. The overall approach was tested by simulating five different configurations of thigh shape and considered tissue conductivity distributions. For the cylinder models used for verification and reference, the reconstructed position deviated by about ≈1 mm from the actual bone centre. In case of models mimicking a realistic cross section of the femur position deviated between 7.9 mm 24.8 mm. For all models, the bone axis was off by about φ=1.50° from its actual position.
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Affiliation(s)
- Jakob Schrott
- Institute of Measurement Technology, Johannes Kepler University, 4020 Linz, Austria
| | - Sabrina Affortunati
- Institute of Measurement Technology, Johannes Kepler University, 4020 Linz, Austria
| | - Christian Stadler
- Department for Orthopedics and Traumatology, Kepler University Hospital, 4020 Linz, Austria
- Medical Faculty, Johannes Kepler University, 4020 Linz, Austria
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Koterwa A, Pierpaoli M, Nejman-Faleńczyk B, Bloch S, Zieliński A, Adamus-Białek W, Jeleniewska Z, Trzaskowski B, Bogdanowicz R, Węgrzyn G, Niedziałkowski P, Ryl J. Discriminating macromolecular interactions based on an impedimetric fingerprint supported by multivariate data analysis for rapid and label-free Escherichia coli recognition in human urine. Biosens Bioelectron 2023; 238:115561. [PMID: 37549553 DOI: 10.1016/j.bios.2023.115561] [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: 05/12/2023] [Revised: 07/29/2023] [Accepted: 07/31/2023] [Indexed: 08/09/2023]
Abstract
This manuscript presents a novel approach to address the challenges of electrode fouling and highly complex electrode nanoarchitecture, which are primary concerns for biosensors operating in real environments. The proposed approach utilizes multiparametric impedance discriminant analysis (MIDA) to obtain a fingerprint of the macromolecular interactions on flat glassy carbon surfaces, achieved through self-organized, drop-cast, receptor-functionalized Au nanocube (AuNC) patterns. Real-time monitoring is combined with singular value decomposition and partial least squares discriminant analysis, which enables selective identification of the analyte from raw impedance data, without the use of electric equivalent circuits. As a proof-of-concept, the authors demonstrate the ability to detect Escherichia coli in real human urine using an aptamer-based biosensor that targets RNA polymerase. This is significant, as uropathogenic E. coli is a difficult-to-treat pathogen that is responsible for the majority of hospital-acquired urinary tract infection cases. The proposed approach offers a limit of detection of 11.3 CFU/mL for the uropathogenic E. coli strain No. 57, an analytical range in all studied concentrations (up to 105 CFU/mL), without the use of antifouling strategies, yet not being specific vs other E.coli strain studied (BL21(DE3)). The MIDA approach allowed to identify negative overpotentials (-0.35 to -0.10 V vs Ag/AgCl) as most suitable for the analysis, offering over 80% sensitivity and accuracy, and the measurement was carried out in just 2 min. Moreover, this approach is scalable and can be applied to other biosensor platforms.
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Affiliation(s)
- Adrian Koterwa
- Department of Analytical Chemistry, Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, 80-308, Gdańsk, Poland.
| | - Mattia Pierpaoli
- Department of Metrology and Optoelectronics, Gdańsk University of Technology, Narutowicza 11/12, 80-233, Gdańsk, Poland.
| | - Bożena Nejman-Faleńczyk
- Department of Molecular Biology, Faculty of Biology, University of Gdansk, Wita Stwosza 59, 80-308, Poland.
| | - Sylwia Bloch
- Department of Molecular Biology, Faculty of Biology, University of Gdansk, Wita Stwosza 59, 80-308, Poland.
| | - Artur Zieliński
- Faculty of Chemistry, Gdańsk University of Technology, Narutowicza 11/12, 80-233, Gdańsk, Poland.
| | - Wioletta Adamus-Białek
- Institute of Medical Sciences, Jan Kochanowski University of Kielce, IX Wieków Kielc 19A, 25-317, Kielce, Poland.
| | - Zofia Jeleniewska
- Division of Electrochemistry and Surface Physical Chemistry, Faculty of Applied Physics and Mathematics, Gdańsk University of Technology, Narutowicza 11/12, Gdańsk, 80-233, Poland.
| | - Bartosz Trzaskowski
- Centre of New Technologies, University of Warsaw, Banach 2c, 02-097, Warsaw, Poland.
| | - Robert Bogdanowicz
- Department of Metrology and Optoelectronics, Gdańsk University of Technology, Narutowicza 11/12, 80-233, Gdańsk, Poland.
| | - Grzegorz Węgrzyn
- Department of Molecular Biology, Faculty of Biology, University of Gdansk, Wita Stwosza 59, 80-308, Poland.
| | - Paweł Niedziałkowski
- Department of Analytical Chemistry, Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, 80-308, Gdańsk, Poland.
| | - Jacek Ryl
- Division of Electrochemistry and Surface Physical Chemistry, Faculty of Applied Physics and Mathematics, Gdańsk University of Technology, Narutowicza 11/12, Gdańsk, 80-233, Poland.
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Yang D, Gu C, Gu Y, Zhang X, Ge D, Zhang Y, Wang N, Zheng X, Wang H, Yang L, Chen S, Xie P, Chen D, Yu J, Sun J, Bai C. Electrical Impedance Analysis for Lung Cancer: A Prospective, Multicenter, Blind Validation Study. Front Oncol 2022; 12:900110. [PMID: 35936739 PMCID: PMC9348894 DOI: 10.3389/fonc.2022.900110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 06/17/2022] [Indexed: 12/02/2022] Open
Abstract
Hypothesis Patients with cancer have different impedances or conductances than patients with benign normal tissue; thus, we can apply electrical impedance analysis (EIA) to identify patients with cancer. Method To evaluate EIA’s efficacy and safety profile in diagnosing pulmonary lesions, we conducted a prospective, multicenter study among patients with pulmonary lesions recruited from 4 clinical centers (Zhongshan Hospital Ethics Committee, Approval No. 2015-16R and 2017-035(3). They underwent EIA to obtain an Algorithm Composite Score or ‘Prolung Index,’ PI. The classification threshold of 29 was first tested in an analytical validation set of 144 patients and independently validated in a clinical validation set of 418 patients. The subject’s final diagnosis depended on histology and a 2-year follow-up. Results In total, 418 patients completed the entire protocol for clinical validation, with 186 true positives, 145 true negatives, 52 false positives, and 35 false negatives. The sensitivity, specificity, and diagnostic yield were 84% (95% CI 79.3%-89.0%), 74% (95% CI 67.4%-79.8%), and 79% (95%CI 75.3%-83.1%), respectively, and did not differ according to age, sex, smoking history, body mass index, or lesion types. The sensitivity of small lesions was comparable to that of large lesions (p = 0.13). Four hundred eighty-four patients who underwent the analysis received a safety evaluation. No adverse events were considered to be related to the test. Conclusion Electrical impedance analysis is a safe and efficient tool for risk stratification of pulmonary lesions, especially for patients with a suspicious lung lesion.
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Affiliation(s)
- Dawei Yang
- Department of Pulmonary Medicine and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Respiratory Research Institution, Shanghai, China
- Chinese Alliance Against Lung Cancer, Shanghai, China
- Shanghai Engineer & Technology Research Center of Internet of Things for Respiratory Medicine, Shanghai, China
| | - Chuanjia Gu
- Department of Respiratory Endoscopy, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
- Department of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Ye Gu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University, Shanghai, China
| | - Xiaodong Zhang
- Department of Pulmonary Medicine, Nantong Tumor Hospital, Nantong, China
| | - Di Ge
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yong Zhang
- Department of Pulmonary Medicine and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Ningfang Wang
- Department of Pulmonary Medicine and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Xiaoxuan Zheng
- Department of Respiratory Endoscopy, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
- Department of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Hao Wang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University, Shanghai, China
| | - Li Yang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University, Shanghai, China
| | - Saihua Chen
- Department of Pulmonary Medicine, Nantong Tumor Hospital, Nantong, China
| | - Pengfei Xie
- Department of Pulmonary Medicine, Nantong Tumor Hospital, Nantong, China
| | - Deng Chen
- Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai, China
| | - Jinming Yu
- Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai, China
| | - Jiayuan Sun
- Department of Respiratory Endoscopy, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
- Department of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Engineering Research Center of Respiratory Endoscopy, Shanghai, China
- *Correspondence: Chunxue Bai, ; Jiayuan Sun,
| | - Chunxue Bai
- Department of Pulmonary Medicine and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Respiratory Research Institution, Shanghai, China
- Chinese Alliance Against Lung Cancer, Shanghai, China
- Shanghai Engineer & Technology Research Center of Internet of Things for Respiratory Medicine, Shanghai, China
- *Correspondence: Chunxue Bai, ; Jiayuan Sun,
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Romero-Coripuna RL, Hernández-Farías DI, Murillo-Ortiz B, Córdova-Fraga T. Electro-impedance mammograms for automatic breast cancer screening: First insights on Mexican patients. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-219254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Breast cancer is a very important health concern around the world. Early detection of such a disease increases the chances of survival. Among the available screening tools, there is the Electro-Impedance Mammography (EIM), which is a novel and less invasive method that captures the potential difference stored in breast tissues under the assumption that electrical properties among normal and pathologically altered tissues are different. In this paper, we address breast cancer detection as a multi-class problem aiming to determine the corresponding label in terms of the Breast Imaging Electrical Impedance classification system, the standard used by physicians for interpreting an EIM mammogram. For experimental purposes, for the first time in the literature, we took advantage of a dataset comprising EIM of Mexican patients. Aiming to establish a baseline for this task, traditional supervised learning methods were used together with two different feature extraction techniques: raw pixel data and transfer learning. Besides, data augmentation was exploited for compensating data imbalance. Different experimental settings were evaluated reaching classification rates over 0.85 in F-score. KNN emerges as a very promising classifier for addressing this task. The obtained results allow us to validate the usefulness of traditional methods for classifying electro-impedance mammograms.
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Affiliation(s)
- Rosario Lissiet Romero-Coripuna
- División de Ciencias e Ingenierías Campus León Universidad de Guanajuato, León, Guanajuato, México
- Escuela profesional de Física, Facultad deCiencias Naturales y Formales, Universidad Nacional de SanAgustín, Arequipa, Perú
| | | | - Blanca Murillo-Ortiz
- Unidad de Investigación en EpidemiologíaClínica, Unidad Médica de Alta Especialidad No. 1 Bajío, Instituto Mexicano del Seguro Social; León, Guanajuato, México
- OOAD Guanajuato, Instituto Mexicano del SeguroSocial, León, Guanajuato, México
| | - Teodoro Córdova-Fraga
- División de Ciencias e Ingenierías Campus León Universidad de Guanajuato, León, Guanajuato, México
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Gómez-Cortés JC, Díaz-Carmona JJ, Padilla-Medina JA, Calderon AE, Gutiérrez AIB, Gutiérrez-López M, Prado-Olivarez J. Electrical Impedance Tomography Technical Contributions for Detection and 3D Geometric Localization of Breast Tumors: A Systematic Review. MICROMACHINES 2022; 13:mi13040496. [PMID: 35457801 PMCID: PMC9025021 DOI: 10.3390/mi13040496] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 03/18/2022] [Accepted: 03/21/2022] [Indexed: 12/30/2022]
Abstract
Impedance measuring acquisition systems focused on breast tumor detection, as well as image processing techniques for 3D imaging, are reviewed in this paper in order to define potential opportunity areas for future research. The description of reported works using electrical impedance tomography (EIT)-based techniques and methodologies for 3D bioimpedance imaging of breast tissues with tumors is presented. The review is based on searching and analyzing related works reported in the most important research databases and is structured according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) parameters and statements. Nineteen papers reporting breast tumor detection and location using EIT were systematically selected and analyzed in this review. Clinical trials in the experimental stage did not produce results in most of analyzed proposals (about 80%), wherein statistical criteria comparison was not possible, such as specificity, sensitivity and predictive values. A 3D representation of bioimpedance is a potential tool for medical applications in malignant breast tumors detection being capable to estimate an ap-proximate the tumor volume and geometric location, in contrast with a tumor area computing capacity, but not the tumor extension depth, in a 2D representation.
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Xu F, Li M, Li J, Jiang H. Diagnostic accuracy and prognostic value of three-dimensional electrical impedance tomography imaging in patients with breast cancer. Gland Surg 2021; 10:2673-2685. [PMID: 34733717 DOI: 10.21037/gs-21-348] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 08/02/2021] [Indexed: 11/06/2022]
Abstract
Background Three-dimensional electrical impedance tomography (3D EIT) is a novel, non-invasive, radiation-free imaging technology for breast cancer screening. This study aimed to identify characteristics and classification of 3D EIT breast cancer imaging that could provide diagnostic accuracy and prognostic value for breast cancer patients. Methods A total of 645 suspicious breast lesions [Breast Imaging Reporting and Data System (BI-RADS) III, IV, V] identified by mammography or ultrasound were examined with 3D EIT (MEIK, SIM-Technika, Yaroslavl, Russia). Breast tissue conductivity was quantified using MEIK 5.6 software. Diagnostic performance of visually interpreted 3D EIT was assessed using histology (surgical excision or vacuum core biopsy) and clinical follow-up. Kaplan-Meier analysis was used to calculate progression-free survival (PFS) and overall survival (OS) rates. Hazard ratio (HR) with a 95% confidence interval (95% CI) for various clinicopathological variables were determined using univariate and multivariate Cox regression models. Results Breast cancer was confirmed in 272 of 645 patients by histopathology and other diagnostic imaging modalities. Among the confirmed cases, 218 patients had positive 3D EIT findings. The sensitivity, specificity, accuracy, positive likelihood, and negative likelihood ratios of 3D EIT were 80.1%, 75.1%, 77.2%, 70.1%, and 83.8%. There were no significant differences in the diagnostic accuracy, sensitivity, or specificity between 3D EIT and mammography, ultrasound, or combined mammography and ultrasound. 3D EIT breast cancer images were classified into 3 different types, including Ia [non-complicated breast cancer (NCBC), 62 cases], Ib [complicated breast cancer (CBC), 131 cases], and Ic [edematous-infiltrative breast cancer (EIBC), 25 cases], which were associated with tumor size (P<0.001), TNM stage (P<0.001), and lymph node metastasis (P=0.012). At 5-year follow-up, multivariate analysis demonstrated that breast cancer 3D EIT imaging classification was an independent predictor for decreased OS (HR: 2.399, 95% CI: 1.035, 5.564, P=0.041) and PFS (HR: 2.836, 95% CI: 1.555, 5.172, P=0.012) in patients with breast cancer. Conclusions 3D EIT breast cancer images were classified into 3 types based on different image characteristics. 3D EIT appeared to be useful in clinical diagnostic performance and prognostic evaluation in patients with breast cancer.
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Affiliation(s)
- Feng Xu
- Department of Breast Surgery, Beijing Chao-Yang Hospital, Beijing, China
| | - Mengxin Li
- Department of Breast Surgery, Beijing Chao-Yang Hospital, Beijing, China
| | - Jie Li
- Department of Breast Surgery, Beijing Chao-Yang Hospital, Beijing, China
| | - Hongchuan Jiang
- Department of Breast Surgery, Beijing Chao-Yang Hospital, Beijing, China
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