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Ye P, Zhao W, Shimomura T, Li KW, Haga A, Geng LS. Pixel-by-pixel correction of beam hardening artifacts by bowtie filter in fan-beam CT. Phys Med Biol 2024; 69:105020. [PMID: 38640915 DOI: 10.1088/1361-6560/ad40fa] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 04/19/2024] [Indexed: 04/21/2024]
Abstract
Objective. Beam hardening (BH) artifacts in computed tomography (CT) images originate from the polychromatic nature of x-ray photons. In a CT system with a bowtie filter, residual BH artifacts remain when polynomial fits are used. These artifacts lead to worse visuals, reduced contrast, and inaccurate CT numbers. This work proposes a pixel-by-pixel correction (PPC) method to reduce the residual BH artifacts caused by a bowtie filter.Approach. The energy spectrum for each pixel at the detector after the photons pass through the bowtie filter was calculated. Then, the spectrum was filtered through a series of water slabs with different thicknesses. The polychromatic projection corresponding to the thickness of the water slab for each detector pixel could be obtained. Next, we carried out a water slab experiment with a mono energyE= 69 keV to get the monochromatic projection. The polychromatic and monochromatic projections were then fitted with a 2nd-order polynomial. The proposed method was evaluated on digital phantoms in a virtual CT system and phantoms in a real CT machine.Main results. In the case of a virtual CT system, the standard deviation of the line profile was reduced by 23.8%, 37.3%, and 14.3%, respectively, in the water phantom with different shapes. The difference of the linear attenuation coefficients (LAC) in the central and peripheral areas of an image was reduced from 0.010 to 0.003cm-1and 0.007cm-1to 0 in the biological tissue phantom and human phantom, respectively. The method was also validated using CT projection data obtained from Activion16 (Canon Medical Systems, Japan). The difference in the LAC in the central and peripheral areas can be reduced by a factor of two.Significance. The proposed PPC method can successfully remove the cupping artifacts in both virtual and authentic CT images. The scanned object's shapes and materials do not affect the technique.
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Affiliation(s)
- Ping Ye
- School of Physics, Beihang University, Beijing 102206, People's Republic of China
| | - Wei Zhao
- School of Physics, Beihang University, Beijing 102206, People's Republic of China
- Hangzhou International Innovation Institute, Beihang University, Hangzhou 311115, People's Republic of China
| | - Taisei Shimomura
- Graduate School of Biomedical Sciences, Tokushima University, Tokushima 770-8503, Japan
| | - Kai-Wen Li
- Research and Development Department, CAS Ion Medical Technology Co., Ltd, Beijing 100190, People's Republic of China
| | - Akihiro Haga
- Graduate School of Biomedical Sciences, Tokushima University, Tokushima 770-8503, Japan
| | - Li-Sheng Geng
- School of Physics, Beihang University, Beijing 102206, People's Republic of China
- Beijing Key Laboratory of Advanced Nuclear Materials and Physics, Beihang University, Beijing 102206, People's Republic of China
- Peng Huanwu Collaborative Center for Research and Education, Beihang University, Beijing 100191, People's Republic of China
- Southern Center for Nuclear-Science Theory (SCNT), Institute of Modern Physics, Chinese Academy of Sciences, Huizhou 516000, People's Republic of China
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Khan NU, Ullah S, Khan FU, Merla A. Development of 2400-2450 MHz Frequency Band RF Energy Harvesting System for Low-Power Device Operation. SENSORS (BASEL, SWITZERLAND) 2024; 24:2986. [PMID: 38793841 PMCID: PMC11125279 DOI: 10.3390/s24102986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 05/02/2024] [Accepted: 05/06/2024] [Indexed: 05/26/2024]
Abstract
Recently, there has been an increasing fascination for employing radio frequency (RF) energy harvesting techniques to energize various low-power devices by harnessing the ambient RF energy in the surroundings. This work outlines a novel advancement in RF energy harvesting (RFEH) technology, intending to power portable gadgets with minimal operating power demands. A high-gain receiver microstrip patch antenna was designed and tested to capture ambient RF residue, operating at 2450 MHz. Similarly, a two-stage Dickson voltage booster was developed and employed with the RFEH to transform the received RF signals into useful DC voltage signals. Additionally, an LC series circuit was utilized to ensure impedance matching between the antenna and rectifier, facilitating the extraction of maximum power from the developed prototype. The findings indicate that the developed rectifier attained a peak power conversion efficiency (PCE) of 64% when operating at an input power level of 0 dBm. During experimentation, the voltage booster demonstrated its capability to rectify a minimum input AC signal of only 50 mV, yielding a corresponding 180 mV output DC signal. Moreover, the maximum power of 4.60 µW was achieved when subjected to an input AC signal of 1500 mV with a load resistance of 470 kΩ. Finally, the devised RFEH was also tested in an open environment, receiving signals from Wi-Fi modems positioned at varying distances for evaluation.
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Affiliation(s)
- Nasir Ullah Khan
- Department of Engineering and Geology, Università degli Studi “G. d’Annunzio” Chieti—Pescara, 65127 Pescara, Italy;
| | - Sana Ullah
- Department of Electrical and Information Engineering, Politecnico di Bari, 70126 Bari, Italy
| | - Farid Ullah Khan
- Department of Mechatronics Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan
| | - Arcangelo Merla
- Department of Engineering and Geology, Università degli Studi “G. d’Annunzio” Chieti—Pescara, 65127 Pescara, Italy;
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Feng M, Xu J. Electroencephalogram-Based ConvMixer Architecture for Recognizing Attention Deficit Hyperactivity Disorder in Children. Brain Sci 2024; 14:469. [PMID: 38790448 PMCID: PMC11118831 DOI: 10.3390/brainsci14050469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 04/29/2024] [Accepted: 05/06/2024] [Indexed: 05/26/2024] Open
Abstract
Attention deficit hyperactivity disorder (ADHD) is a neuro-developmental disorder that affects approximately 5-10% of school-aged children worldwide. Early diagnosis and intervention are essential to improve the quality of life of patients and their families. In this study, we propose ConvMixer-ECA, a novel deep learning architecture that combines ConvMixer with efficient channel attention (ECA) blocks for the accurate diagnosis of ADHD using electroencephalogram (EEG) signals. The model was trained and evaluated using EEG recordings from 60 healthy children and 61 children with ADHD. A series of experiments were conducted to evaluate the performance of the ConvMixer-ECA. The results showed that the ConvMixer-ECA performed well in ADHD recognition with 94.52% accuracy. The incorporation of attentional mechanisms, in particular ECA, improved the performance of ConvMixer; it outperformed other attention-based variants. In addition, ConvMixer-ECA outperformed state-of-the-art deep learning models including EEGNet, CNN, RNN, LSTM, and GRU. t-SNE visualization of the output of this model layer validated the effectiveness of ConvMixer-ECA in capturing the underlying patterns and features that separate ADHD from typically developing individuals through hierarchical feature learning. These outcomes demonstrate the potential of ConvMixer-ECA as a valuable tool to assist clinicians in the early diagnosis and intervention of ADHD in children.
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Affiliation(s)
- Min Feng
- Nanjing Rehabilitation Medical Center, The Affiliated Brain Hospital, Nanjing Medical University, Nanjing 210029, China
- School of Chinese Language and Literature, Nanjing Normal University, Nanjing 210024, China
| | - Juncai Xu
- School of Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
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Cha J, Kim C, Choi SH. Extrinsic Laryngeal Muscle Activity With Different Diameters and Water Depths in a Semi-Occluded Vocal Tract Exercise. JOURNAL OF SPEECH, LANGUAGE, AND HEARING RESEARCH : JSLHR 2024; 67:1324-1338. [PMID: 38592964 DOI: 10.1044/2024_jslhr-23-00194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/11/2024]
Abstract
PURPOSE Surface electromyography (sEMG) has been used to evaluate extrinsic laryngeal muscle activity during swallowing and phonation. In the current study, sEMG amplitudes were measured from the infrahyoid and suprahyoid muscles during phonation through a tube submerged in water. METHOD The sEMG amplitude values measured from the extrinsic laryngeal muscles and the electroglottographic contact quotient (CQ) were obtained simultaneously from 62 healthy participants (31 men, 31 women) during phonation through a tube at six different depths (2, 4, 7, 10, 15, and 20 cm) while using two tubes with different diameters (1 and 0.5 cm). RESULTS With increasing depth, the sEMG amplitude for the suprahyoid muscles increased in men and women. However, sEMG amplitudes for the infrahyoid muscles increased significantly only in men. Tube diameter had a significant effect on the suprahyoid sEMG amplitudes only for men, with higher sEMG amplitudes when phonating with a 1.0-cm tube. CQ values increased with submerged depth for both men and women. Tube diameter affected results such than CQ values were higher for men when using the wider tube and for women with the narrower tube. CONCLUSIONS Vocal fold vibratory patterns changed with the depth of tube submersion in water for both men and women, but the patterns of muscle activation differed between the sexes. This suggests that men and women use different strategies when confronted with increased intraoral pressure during semi-occluded vocal tract exercises. In this study, sEMG provided insight into the mechanism for differences between vocally normal individuals and could help detect compensatory muscle activation during tube phonation in water for people with voice disorders.
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Affiliation(s)
- Junseo Cha
- Department of Audiology and Speech-Language Pathology, Research Institute of Biomimetic Sensory Control, Catholic Hearing Voice Speech Center, Daegu Catholic University, Gyeongsan, South Korea
| | - Chaehyun Kim
- Department of Audiology and Speech-Language Pathology, Research Institute of Biomimetic Sensory Control, Catholic Hearing Voice Speech Center, Daegu Catholic University, Gyeongsan, South Korea
| | - Seong Hee Choi
- Department of Audiology and Speech-Language Pathology, Research Institute of Biomimetic Sensory Control, Catholic Hearing Voice Speech Center, Daegu Catholic University, Gyeongsan, South Korea
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Jebreen K, Radwan E, Kammoun-Rebai W, Alattar E, Radwan A, Safi W, Radwan W, Alajez M. Perceptions of undergraduate medical students on artificial intelligence in medicine: mixed-methods survey study from Palestine. BMC MEDICAL EDUCATION 2024; 24:507. [PMID: 38714993 PMCID: PMC11077786 DOI: 10.1186/s12909-024-05465-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 04/24/2024] [Indexed: 05/12/2024]
Abstract
BACKGROUND The current applications of artificial intelligence (AI) in medicine continue to attract the attention of medical students. This study aimed to identify undergraduate medical students' attitudes toward AI in medicine, explore present AI-related training opportunities, investigate the need for AI inclusion in medical curricula, and determine preferred methods for teaching AI curricula. METHODS This study uses a mixed-method cross-sectional design, including a quantitative study and a qualitative study, targeting Palestinian undergraduate medical students in the academic year 2022-2023. In the quantitative part, we recruited a convenience sample of undergraduate medical students from universities in Palestine from June 15, 2022, to May 30, 2023. We collected data by using an online, well-structured, and self-administered questionnaire with 49 items. In the qualitative part, 15 undergraduate medical students were interviewed by trained researchers. Descriptive statistics and an inductive content analysis approach were used to analyze quantitative and qualitative data, respectively. RESULTS From a total of 371 invitations sent, 362 responses were received (response rate = 97.5%), and 349 were included in the analysis. The mean age of participants was 20.38 ± 1.97, with 40.11% (140) in their second year of medical school. Most participants (268, 76.79%) did not receive formal education on AI before or during medical study. About two-thirds of students strongly agreed or agreed that AI would become common in the future (67.9%, 237) and would revolutionize medical fields (68.7%, 240). Participants stated that they had not previously acquired training in the use of AI in medicine during formal medical education (260, 74.5%), confirming a dire need to include AI training in medical curricula (247, 70.8%). Most participants (264, 75.7%) think that learning opportunities for AI in medicine have not been adequate; therefore, it is very important to study more about employing AI in medicine (228, 65.3%). Male students (3.15 ± 0.87) had higher perception scores than female students (2.81 ± 0.86) (p < 0.001). The main themes that resulted from the qualitative analysis of the interview questions were an absence of AI learning opportunities, the necessity of including AI in medical curricula, optimism towards the future of AI in medicine, and expected challenges related to AI in medical fields. CONCLUSION Medical students lack access to educational opportunities for AI in medicine; therefore, AI should be included in formal medical curricula in Palestine.
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Affiliation(s)
- Kamel Jebreen
- Department of Mathematics, Palestine Technical University - Kadoorie, Hebron, Palestine
- Department of Mathematics, An-Najah National University, Nablus, Palestine
- Unité de Recherche Clinique Saint-Louis Fernand-Widal Lariboisière, APHP, Paris, France
| | - Eqbal Radwan
- Department of Biology, Faculty of Science, Islamic University of Gaza, Gaza, Palestine.
| | | | - Etimad Alattar
- Department of Biology, Faculty of Science, Islamic University of Gaza, Gaza, Palestine
| | - Afnan Radwan
- Faculty of Education, Islamic University of Gaza, Gaza, Palestine
| | - Walaa Safi
- Department of Biotechnology, Faculty of Science, Islamic University of Gaza, Gaza, Palestine
| | - Walaa Radwan
- University College of Applied Sciences - Gaza, Gaza, Palestine
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Ibáñez P, Villa-Abaunza A, Udías JM. Impact on the estimated dose of different tissue assignment strategies during partial breast irradiations with INTRABEAM. Brachytherapy 2024:S1538-4721(24)00034-5. [PMID: 38705803 DOI: 10.1016/j.brachy.2024.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 01/19/2024] [Accepted: 02/12/2024] [Indexed: 05/07/2024]
Abstract
PURPOSE Partial breast irradiations with electronic brachytherapy or kilovoltage intraoperative radiotherapy devices such as Axxent or INTRABEAM are becoming more common every day. Breast is mainly composed of glandular and adipose tissues, which are not always clearly disentangled in planning breast CTs. In these cases, breast tissues are replaced with an average soft tissue, or even water. However, at kilovoltage energies, this may lead to large differences in the delivered dose, due to the dominance of photoelectric effect. Therefore, the aim of this work was to study the effect on the dose prescribed in breast with the INTRABEAM device using different soft tissue assignment strategies that would replace the adipose and glandular tissues that constitute the breast in cases where these tissues cannot be adequately distinguished in a CT scan. METHODS AND MATERIALS Dose was computed with a Monte Carlo code in five patients with a 3 cm diameter INTRABEAM spherical applicator. Tissues within the breast were assigned following six different strategies: one based on the TG-43 recommendations, representing the whole breast as water of unity density, another one also water-based but with CT derived density, and the other four also based on CT-derived densities, using a single tissue resulting from different mixes of glandular and adipose tissues. These were compared against the reference dose computed in an accurately segmented CT, following TG-186 recommendations. Relative differences and dose ratios between the reference and the other tissue assignment strategies were obtained in three regions of interest inside the breast. RESULTS AND CONCLUSIONS Dose planning in water-based tissues was found inaccurate for breast treatment with INTRABEAM, as it would incur in up to 30% under-prescription of dose. If accurate soft tissue assignments in the breast cannot be safely done, a single-tissue composition of 80% adipose and 20% glandular tissue, or even a 100% adipose tissue, would be recommended to avoid dose under-prescription.
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Affiliation(s)
- Paula Ibáñez
- Nuclear Physics Group and IPARCOS, Department of Structure of Matter, Thermal Physics and Electronics, CEI Moncloa, Universidad Complutense de Madrid, Madrid, Spain; Instituto de Investigación Sanitaria del Hospital Clínico San Carlos, Madrid, Spain.
| | - Amaia Villa-Abaunza
- Nuclear Physics Group and IPARCOS, Department of Structure of Matter, Thermal Physics and Electronics, CEI Moncloa, Universidad Complutense de Madrid, Madrid, Spain
| | - José Manuel Udías
- Nuclear Physics Group and IPARCOS, Department of Structure of Matter, Thermal Physics and Electronics, CEI Moncloa, Universidad Complutense de Madrid, Madrid, Spain; Instituto de Investigación Sanitaria del Hospital Clínico San Carlos, Madrid, Spain
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Brambilla C, Beltrame G, Marino G, Lanzani V, Gatti R, Portinaro N, Molinari Tosatti L, Scano A. Biomechanical Analysis of Human Gait When Changing Velocity and Carried Loads: Simulation Study with OpenSim. BIOLOGY 2024; 13:321. [PMID: 38785803 PMCID: PMC11118041 DOI: 10.3390/biology13050321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 04/22/2024] [Accepted: 05/02/2024] [Indexed: 05/25/2024]
Abstract
Walking is one of the main activities of daily life and gait analysis can provide crucial data for the computation of biomechanics in many fields. In multiple applications, having reference data that include a variety of gait conditions could be useful for assessing walking performance. However, limited extensive reference data are available as many conditions cannot be easily tested experimentally. For this reason, a musculoskeletal model in OpenSim coupled with gait data (at seven different velocities) was used to simulate seven carried loads and all the combinations between the two parameters. The effects on lower limb biomechanics were measured with torque, power, and mechanical work. The results demonstrated that biomechanics was influenced by both speed and load. Our results expand the previous literature: in the majority of previous work, only a subset of the presented conditions was investigated. Moreover, our simulation approach provides comprehensive data that could be useful for applications in many areas, such as rehabilitation, orthopedics, medical care, and sports.
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Affiliation(s)
- Cristina Brambilla
- Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), 20133 Milan, Italy; (C.B.); (V.L.); (L.M.T.)
| | - Giulia Beltrame
- Residency Program in Orthopedics and Traumatology, Universitá degli Studi di Milano, 20122 Milan, Italy; (G.B.); (N.P.)
| | - Giorgia Marino
- Physiotherapy Unit, IRCCS Humanitas Research Hospital, Rozzano, 20098 Milan, Italy; (G.M.); (R.G.)
| | - Valentina Lanzani
- Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), 20133 Milan, Italy; (C.B.); (V.L.); (L.M.T.)
| | - Roberto Gatti
- Physiotherapy Unit, IRCCS Humanitas Research Hospital, Rozzano, 20098 Milan, Italy; (G.M.); (R.G.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy
| | - Nicola Portinaro
- Residency Program in Orthopedics and Traumatology, Universitá degli Studi di Milano, 20122 Milan, Italy; (G.B.); (N.P.)
- Department of Pediatric Surgery, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, 20122 Milan, Italy
| | - Lorenzo Molinari Tosatti
- Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), 20133 Milan, Italy; (C.B.); (V.L.); (L.M.T.)
| | - Alessandro Scano
- Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), 20133 Milan, Italy; (C.B.); (V.L.); (L.M.T.)
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de la Merced Díaz-González C, Pérez-Bello C, De la Rosa-Hormiga M, González-Henríquez JJ, de las Mercedes Reyes-Noha M. Hospital Environmental Factors That Influence Peripheral Oxygen Saturation Measurements: A Cross-Sectional Study. Healthcare (Basel) 2024; 12:940. [PMID: 38727497 PMCID: PMC11083166 DOI: 10.3390/healthcare12090940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Revised: 04/28/2024] [Accepted: 04/30/2024] [Indexed: 05/13/2024] Open
Abstract
Pulse oximetry is a non-invasive, cost-effective, and generally reliable instrument measuring pulse rate and peripheral oxygen saturation (SpO2). However, these measurements can be affected by the patient's internal or external factors, including the type of pulse oximeter device (POD). (1) This study's objective was to identify potential environmental factors that may impact the measurements taken by three PODs. (2) Methods: A descriptive-analytical cross-sectional study was designed. The patients' SpO2 levels were measured using a standard monitor and two PODs owned by the professionals. The measurements were taken on the patients' fingers. Concurrently, we evaluated the surrounding environmental conditions, encompassing temperature, humidity, illuminance, and noise. (3) Results: This study involved 288 adult participants in the sample. For each 20-decibel increment in noise, there was a reduction in SpO2 by an average of 1%, whereas for every additional degree of ambient temperature, SpO2 decreased by an average of 2% (4) Conclusions: Significant correlations between SpO2 and age, as well as with noise and ambient temperature, were observed. No significant differences between oxygen saturation and lighting or humidity were observed. This study was prospectively registered with the Clinical Research Ethics Committee of Gran Canaria at the Dr. Negrín University Hospital, with protocol code 2019-247-1, and approved on 24 May 2019.
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Affiliation(s)
- Candelaria de la Merced Díaz-González
- Department of Nursing, Faculty of Health Sciences, University of Las Palmas de Gran Canaria, 35016 Las Palmas de Gran Canaria, Canary Islands, Spain; (C.P.-B.); (M.D.l.R.-H.)
| | - Cristina Pérez-Bello
- Department of Nursing, Faculty of Health Sciences, University of Las Palmas de Gran Canaria, 35016 Las Palmas de Gran Canaria, Canary Islands, Spain; (C.P.-B.); (M.D.l.R.-H.)
- Hospital Insular de Gran Canaria, 35016 Las Palmas de Gran Canaria, Canary Islands, Spain
| | - Milagros De la Rosa-Hormiga
- Department of Nursing, Faculty of Health Sciences, University of Las Palmas de Gran Canaria, 35016 Las Palmas de Gran Canaria, Canary Islands, Spain; (C.P.-B.); (M.D.l.R.-H.)
| | - Juan José González-Henríquez
- Department of Mathematics, Faculty of Mathematics, University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Canary Islands, Spain;
| | - María de las Mercedes Reyes-Noha
- Continuous Training Department, Primary Care Management, Gran Canaria Health Area, 35006 Las Palmas de Gran Canaria, Canary Islands, Spain;
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Brookshire G, Kasper J, Blauch NM, Wu YC, Glatt R, Merrill DA, Gerrol S, Yoder KJ, Quirk C, Lucero C. Data leakage in deep learning studies of translational EEG. Front Neurosci 2024; 18:1373515. [PMID: 38765672 PMCID: PMC11099244 DOI: 10.3389/fnins.2024.1373515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 04/04/2024] [Indexed: 05/22/2024] Open
Abstract
A growing number of studies apply deep neural networks (DNNs) to recordings of human electroencephalography (EEG) to identify a range of disorders. In many studies, EEG recordings are split into segments, and each segment is randomly assigned to the training or test set. As a consequence, data from individual subjects appears in both the training and the test set. Could high test-set accuracy reflect data leakage from subject-specific patterns in the data, rather than patterns that identify a disease? We address this question by testing the performance of DNN classifiers using segment-based holdout (in which segments from one subject can appear in both the training and test set), and comparing this to their performance using subject-based holdout (where all segments from one subject appear exclusively in either the training set or the test set). In two datasets (one classifying Alzheimer's disease, and the other classifying epileptic seizures), we find that performance on previously-unseen subjects is strongly overestimated when models are trained using segment-based holdout. Finally, we survey the literature and find that the majority of translational DNN-EEG studies use segment-based holdout. Most published DNN-EEG studies may dramatically overestimate their classification performance on new subjects.
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Affiliation(s)
| | - Jake Kasper
- SPARK Neuro Inc., New York, NY, United States
| | - Nicholas M. Blauch
- SPARK Neuro Inc., New York, NY, United States
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, United States
| | | | - Ryan Glatt
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, United States
| | - David A. Merrill
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, United States
- Saint John's Cancer Institute at Providence Saint John's Health Center, Santa Monica, CA, United States
- Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA, United States
| | | | | | - Colin Quirk
- SPARK Neuro Inc., New York, NY, United States
| | - Ché Lucero
- SPARK Neuro Inc., New York, NY, United States
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Du W, Guo H, Chen B, Cui M, Zhang T, Sun D, Ma H. Cascaded-TOARNet: A cascaded framework based on mixed attention and multiscale information for thoracic OARs segmentation. Med Phys 2024; 51:3405-3420. [PMID: 38063140 DOI: 10.1002/mp.16881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Revised: 10/20/2023] [Accepted: 11/19/2023] [Indexed: 05/08/2024] Open
Abstract
BACKGROUND Accurate and automated segmentation of thoracic organs-at-risk (OARs) is critical for radiotherapy treatment planning of thoracic cancers. However, this has remained a challenging task for four major reasons: (1) thoracic OARs have diverse morphologies; (2) thoracic OARs have low contrast with the background; (3) boundaries of thoracic OARs are blurry; (4) class imbalance issue caused by small organs. PURPOSE To overcome the above challenges and achieve accurate and automated segmentation of thoracic OARs on thoracic CT. METHODS A novel cascaded framework based on mixed attention and multiscale information for thoracic OARs segmentation, called Cascaded-TOARNet. This cascaded framework comprises two stages: localization and segmentation. During the localization stage, TOARNet locates each organ to crop the regions of interest (ROIs). During the segmentation stage, TOARNet accurately segments the ROIs, and the segmentation results are merged into a complete result. RESULTS We evaluated our proposed method and other common segmentation methods on two public datasets: the AAPM Thoracic Auto-Segmentation Challenge dataset and the Segmentation of Thoracic Organs at Risk (SegTHOR) dataset. Our method demonstrated superior performance, achieving a mean Dice score of 92.6% on the SegTHOR dataset and 90.8% on the AAPM dataset. CONCLUSIONS This segmentation method holds great promise as an essential tool for enhancing the efficiency of thoracic radiotherapy planning.
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Affiliation(s)
- Wu Du
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Huimin Guo
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Boyang Chen
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Ming Cui
- Gastrointestinal and Urinary and Musculoskeletal Cancer, Cancer Hospital of Dalian University of Technology, Shenyang, Liaoning, China
| | - Teng Zhang
- Gastrointestinal and Urinary and Musculoskeletal Cancer, Cancer Hospital of Dalian University of Technology, Shenyang, Liaoning, China
| | - Deyu Sun
- Gastrointestinal and Urinary and Musculoskeletal Cancer, Cancer Hospital of Dalian University of Technology, Shenyang, Liaoning, China
| | - He Ma
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, China
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang, Liaoning, China
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Musicant O, Richmond-Hacham B, Botzer A. Cardiac indices of driver fatigue across in-lab and on-road studies. APPLIED ERGONOMICS 2024; 117:104202. [PMID: 38215606 DOI: 10.1016/j.apergo.2023.104202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 10/31/2023] [Accepted: 12/05/2023] [Indexed: 01/14/2024]
Abstract
Driver fatigue is a major contributor to road accidents. Therefore, driver assistance systems (DAS) that would monitor drivers' states may contribute to road safety. Such monitoring can potentially be achieved with input from ECG indices (e.g., heart rate). We reviewed the empirical literature on responses of cardiac measures to driver fatigue and on detecting fatigue with cardiac indices and classification algorithms. We used meta-analytical methods to explore the pooled effect sizes of different cardiac indices of fatigue, their heterogeneity, and the consistency of their responses across studies. Our large pool of studies (N = 39) allowed us to stratify the results across on-road and simulator studies. We found that despite the large heterogeneity of the effect sizes between the studies, many indices had significant pooled effect sizes across the studies, and more frequently across the on-road studies. We also found that most indices showed consistent responses across both on-road and simulator studies. Regarding the detection accuracy, we found that even on-road classification could have been as accurate as 70% with only 2-min of data. However, we could only find two on-road studies that employed fatigue classification algorithms. Overall, our findings are encouraging with respect to the prospect of using cardiac measures for detecting driver fatigue. Yet, to fully explore this possibility, there is a need for additional on-road studies that would employ a similar set of cardiac indices and detection algorithms, a unified definition of fatigue, and additional levels of fatigue than the two fatigue vs alert states.
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Affiliation(s)
- Oren Musicant
- Industrial Engineering & Management, Ariel University, Kiriat Hamada, Ariel, Israel.
| | - Bar Richmond-Hacham
- Industrial Engineering & Management, Ariel University, Kiriat Hamada, Ariel, Israel.
| | - Assaf Botzer
- Industrial Engineering & Management, Ariel University, Kiriat Hamada, Ariel, Israel.
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Wang L, Wang Q, Wang X, Ma Y, Zhang L, Liu M. Triplet-constrained deep hashing for chest X-ray image retrieval in COVID-19 assessment. Neural Netw 2024; 173:106182. [PMID: 38387203 DOI: 10.1016/j.neunet.2024.106182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 01/15/2024] [Accepted: 02/15/2024] [Indexed: 02/24/2024]
Abstract
Radiology images of the chest, such as computer tomography scans and X-rays, have been prominently used in computer-aided COVID-19 analysis. Learning-based radiology image retrieval has attracted increasing attention recently, which generally involves image feature extraction and finding matches in extensive image databases based on query images. Many deep hashing methods have been developed for chest radiology image search due to the high efficiency of retrieval using hash codes. However, they often overlook the complex triple associations between images; that is, images belonging to the same category tend to share similar characteristics and vice versa. To this end, we develop a triplet-constrained deep hashing (TCDH) framework for chest radiology image retrieval to facilitate automated analysis of COVID-19. The TCDH consists of two phases, including (a) feature extraction and (b) image retrieval. For feature extraction, we have introduced a triplet constraint and an image reconstruction task to enhance discriminative ability of learned features, and these features are then converted into binary hash codes to capture semantic information. Specifically, the triplet constraint is designed to pull closer samples within the same category and push apart samples from different categories. Additionally, an auxiliary image reconstruction task is employed during feature extraction to help effectively capture anatomical structures of images. For image retrieval, we utilize learned hash codes to conduct searches for medical images. Extensive experiments on 30,386 chest X-ray images demonstrate the superiority of the proposed method over several state-of-the-art approaches in automated image search. The code is now available online.
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Affiliation(s)
- Linmin Wang
- School of Mathematics Science, Liaocheng University, Liaocheng, Shandong, 252000, China
| | - Qianqian Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Xiaochuan Wang
- School of Mathematics Science, Liaocheng University, Liaocheng, Shandong, 252000, China
| | - Yunling Ma
- School of Mathematics Science, Liaocheng University, Liaocheng, Shandong, 252000, China
| | - Limei Zhang
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, Shandong, 250101, China.
| | - Mingxia Liu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
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Li B, Du K, Qu G, Tang N. Big data research in nursing: A bibliometric exploration of themes and publications. J Nurs Scholarsh 2024; 56:466-477. [PMID: 38140780 DOI: 10.1111/jnu.12954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 10/14/2023] [Accepted: 12/08/2023] [Indexed: 12/24/2023]
Abstract
AIMS To comprehend the current research hotspots and emerging trends in big data research within the global nursing domain. DESIGN Bibliometric analysis. METHODS The quality articles for analysis indexed by the science core collection were obtained from the Web of Science database as of February 10, 2023.The descriptive, visual analysis and text mining were realized by CiteSpace and VOSviewer. RESULTS The research on big data in the nursing field has experienced steady growth over the past decade. A total of 45 core authors and 17 core journals around the world have contributed to this field. The author's keyword analysis has revealed five distinct clusters of research focus. These encompass machine/deep learning and artificial intelligence, natural language processing, big data analytics and data science, IoT and cloud computing, and the development of prediction models through data mining. Furthermore, a comparative examination was conducted with data spanning from 1980 to 2016, and an extended analysis was performed covering the years from 1980 to 2019. This bibliometric mapping comparison allowed for the identification of prevailing research trends and the pinpointing of potential future research hotspots within the field. CONCLUSIONS The fusion of data mining and nursing research has steadily advanced and become more refined over time. Technologically, it has expanded from initial natural language processing to encompass machine learning, deep learning, artificial intelligence, and data mining approach that amalgamates multiple technologies. Professionally, it has progressed from addressing patient safety and pressure ulcers to encompassing chronic diseases, critical care, emergency response, community and nursing home settings, and specific diseases (Cardiovascular diseases, diabetes, stroke, etc.). The convergence of IoT, cloud computing, fog computing, and big data processing has opened new avenues for research in geriatric nursing management and community care. However, a global imbalance exists in utilizing big data in nursing research, emphasizing the need to enhance data science literacy among clinical staff worldwide to advance this field. CLINICAL RELEVANCE This study focused on the thematic trends and evolution of research on the big data in nursing research. Moreover, this study may contribute to the understanding of researchers, journals, and countries around the world and generate the possible collaborations of them to promote the development of big data in nursing science.
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Affiliation(s)
- Bo Li
- Department of Emergency Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Kun Du
- Department of Emergency Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Guanchen Qu
- School of Artificial Intelligence, Shenyang University of Technology, Shenyang, China
| | - Naifu Tang
- Department of Emergency Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Ghosh S, Patra S, Younis MH, Chakraborty A, Guleria A, Gupta SK, Singh K, Rakhshit S, Chakraborty S, Cai W, Chakravarty R. Brachytherapy at the nanoscale with protein functionalized and intrinsically radiolabeled [ 169Yb]Yb 2O 3 nanoseeds. Eur J Nucl Med Mol Imaging 2024; 51:1558-1573. [PMID: 38270686 DOI: 10.1007/s00259-024-06612-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Accepted: 01/09/2024] [Indexed: 01/26/2024]
Abstract
PURPOSE Classical brachytherapy of solid malignant tumors is an invasive procedure which often results in an uneven dose distribution, while requiring surgical removal of sealed radioactive seed sources after a certain period of time. To circumvent these issues, we report the synthesis of intrinsically radiolabeled and gum Arabic glycoprotein functionalized [169Yb]Yb2O3 nanoseeds as a novel nanoscale brachytherapy agent, which could directly be administered via intratumoral injection for tumor therapy. METHODS 169Yb (T½ = 32 days) was produced by neutron irradiation of enriched (15.2% in 168Yb) Yb2O3 target in a nuclear reactor, radiochemically converted to [169Yb]YbCl3 and used for nanoparticle (NP) synthesis. Intrinsically radiolabeled NP were synthesized by controlled hydrolysis of Yb3+ ions in gum Arabic glycoprotein medium. In vivo SPECT/CT imaging, autoradiography, and biodistribution studies were performed after intratumoral injection of radiolabeled NP in B16F10 tumor bearing C57BL/6 mice. Systematic tumor regression studies and histopathological analyses were performed to demonstrate therapeutic efficacy in the same mice model. RESULTS The nanoformulation was a clear solution having high colloidal and radiochemical stability. Uniform distribution and retention of the radiolabeled nanoformulation in the tumor mass were observed via SPECT/CT imaging and autoradiography studies. In a tumor regression study, tumor growth was significantly arrested with different doses of radiolabeled NP compared to the control and the best treatment effect was observed with ~ 27.8 MBq dose. In histopathological analysis, loss of mitotic cells was apparent in tumor tissue of treated groups, whereas no significant damage in kidney, lungs, and liver tissue morphology was observed. CONCLUSIONS These results hold promise for nanoscale brachytherapy to become a clinically practical treatment modality for unresectable solid cancers.
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Affiliation(s)
- Sanchita Ghosh
- Radiopharmaceuticals Division, Bhabha Atomic Research Centre, Trombay, Mumbai, 400085, India
- Homi Bhabha National Institute, Anushaktinagar, Mumbai, 400094, India
| | - Sourav Patra
- Radiopharmaceuticals Division, Bhabha Atomic Research Centre, Trombay, Mumbai, 400085, India
- Homi Bhabha National Institute, Anushaktinagar, Mumbai, 400094, India
| | - Muhsin H Younis
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, USA
| | - Avik Chakraborty
- Homi Bhabha National Institute, Anushaktinagar, Mumbai, 400094, India
- Radiation Medicine Centre, Bhabha Atomic Research Centre, Parel, Mumbai, 400012, India
| | - Apurav Guleria
- Homi Bhabha National Institute, Anushaktinagar, Mumbai, 400094, India
- Radiation and Photochemistry Division, Bhabha Atomic Research Centre, Trombay, Mumbai, 400085, India
| | - Santosh K Gupta
- Homi Bhabha National Institute, Anushaktinagar, Mumbai, 400094, India
- Radiochemistry Division, Bhabha Atomic Research Centre, Trombay, Mumbai, 400085, India
| | - Khajan Singh
- Radiopharmaceuticals Division, Bhabha Atomic Research Centre, Trombay, Mumbai, 400085, India
| | - Sutapa Rakhshit
- Radiation Medicine Centre, Bhabha Atomic Research Centre, Parel, Mumbai, 400012, India
| | - Sudipta Chakraborty
- Radiopharmaceuticals Division, Bhabha Atomic Research Centre, Trombay, Mumbai, 400085, India
- Homi Bhabha National Institute, Anushaktinagar, Mumbai, 400094, India
| | - Weibo Cai
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, USA.
| | - Rubel Chakravarty
- Radiopharmaceuticals Division, Bhabha Atomic Research Centre, Trombay, Mumbai, 400085, India.
- Homi Bhabha National Institute, Anushaktinagar, Mumbai, 400094, India.
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Barnsley H, Robertson S, Cruickshank S, McNair HA. Radiographer training for screening of patients referred for Magnetic Resonance Imaging: A scoping review. Radiography (Lond) 2024; 30:843-855. [PMID: 38579383 DOI: 10.1016/j.radi.2024.03.009] [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: 02/07/2024] [Revised: 03/12/2024] [Accepted: 03/19/2024] [Indexed: 04/07/2024]
Abstract
INTRODUCTION Strict safety practices are essential to ensure the safety of patients and staff in Magnetic Resonance Imaging (MRI). Training regarding the fundamentals of MRI safety is well-established and commonly agreed upon. However, more complex aspect of screening patients, such as image review or screening of unconscious patients/patients with communication difficulties is less well discussed. The current UK and USA guidelines do not suggest the use of communication training for MRI staff nor indicate any training to encourage reviewing images in the screening process. This review aims to map the current guidance regarding safety and patient screening training for MRI diagnostic and therapeutic radiographers. METHODS A systematic search of PubMed, Trip Medical database and Radiography journal was conducted. Studies were chosen based on the review objectives and pre-determined inclusion/exclusion criteria using the PRISMA-ScR framework. RESULTS Twenty-four studies were included in the review, which identified some key concepts including MRI safety training and delivery methods, screening and communication, screening of unconscious or non-ambulatory patients and the use of imaging. CONCLUSION Training gaps lie within the more complex elements of screening such as the inclusiveness of question phrasing, particularly to the neurodivergent population, how we teach radiographers to screen unconscious/unresponsive patients and using imaging to detect implants. IMPLICATIONS FOR PRACTICE The consequences of incomplete or inaccurate pre-MRI safety screening could be the introduction of unexpected implants into the scanner or forgoing MRI for a less desirable modality. The development of enhanced training programs in implant recognition using imaging and communication could complement existing training.
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Affiliation(s)
- H Barnsley
- The Royal Marsden NHS Foundation Trust, 203 Fulham Road, London, SW3 6JJ, UK
| | - S Robertson
- The Royal Marsden NHS Foundation Trust, 203 Fulham Road, London, SW3 6JJ, UK
| | - S Cruickshank
- The Royal Marsden NHS Foundation Trust, 203 Fulham Road, London, SW3 6JJ, UK
| | - H A McNair
- The Royal Marsden NHS Foundation Trust, 203 Fulham Road, London, SW3 6JJ, UK; The Institute of Cancer Research, UK.
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Schmierer T, Li T, Li Y. Harnessing machine learning for EEG signal analysis: Innovations in depth of anaesthesia assessment. Artif Intell Med 2024; 151:102869. [PMID: 38593683 DOI: 10.1016/j.artmed.2024.102869] [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: 09/28/2023] [Revised: 01/31/2024] [Accepted: 04/03/2024] [Indexed: 04/11/2024]
Abstract
Anaesthesia, crucial to surgical practice, is undergoing renewed scrutiny due to the integration of artificial intelligence in its medical use. The precise control over the temporary loss of consciousness is vital to ensure safe, pain-free procedures. Traditional methods of depth of anaesthesia (DoA) assessment, reliant on physical characteristics, have proven inconsistent due to individual variations. In response, electroencephalography (EEG) techniques have emerged, with indices such as the Bispectral Index offering quantifiable assessments. This literature review explores the current scope and frontier of DoA research, emphasising methods utilising EEG signals for effective clinical monitoring. This review offers a critical synthesis of recent advances, specifically focusing on electroencephalography (EEG) techniques and their role in enhancing clinical monitoring. By examining 117 high-impact papers, the review delves into the nuances of feature extraction, model building, and algorithm design in EEG-based DoA analysis. Comparative assessments of these studies highlight their methodological approaches and performance, including clinical correlations with established indices like the Bispectral Index. The review identifies knowledge gaps, particularly the need for improved collaboration for data access, which is essential for developing superior machine learning models and real-time predictive algorithms for patient management. It also calls for refined model evaluation processes to ensure robustness across diverse patient demographics and anaesthetic agents. The review underscores the potential of technological advancements to enhance precision, safety, and patient outcomes in anaesthesia, paving the way for a new standard in anaesthetic care. The findings of this review contribute to the ongoing discourse on the application of EEG in anaesthesia, providing insights into the potential for technological advancement in this critical area of medical practice.
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Affiliation(s)
- Thomas Schmierer
- School of Mathematics, Physics and Computing, University of Southern Queensland, Australia.
| | - Tianning Li
- School of Mathematics, Physics and Computing, University of Southern Queensland, Australia.
| | - Yan Li
- School of Mathematics, Physics and Computing, University of Southern Queensland, Australia.
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67
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He C, Xu P, Pei X, Wang Q, Yue Y, Han C. Fatigue at the wheel: A non-visual approach to truck driver fatigue detection by multi-feature fusion. ACCIDENT; ANALYSIS AND PREVENTION 2024; 199:107511. [PMID: 38387154 DOI: 10.1016/j.aap.2024.107511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 01/28/2024] [Accepted: 02/15/2024] [Indexed: 02/24/2024]
Abstract
BACKGROUND Monitoring of long-haul truck driver fatigue state has attracted considerable interest. Conventional fatigue driving detection methods based on the physiological and visual features are scarcely applicable, due to the intrusiveness, reliability, and cost-effectiveness concerns. METHODS We elaborately developed a fatigue driving detection method by fusion of non-visual features derived from the customized wristbands, vehicle-mounted equipment, and trip logs. To capture the spatiotemporal information within the sequential data, the bidirectional long short-term memory network with attention mechanism was proposed to determine whether the truck driver was fatigued within a fine-grained episode of one minute. The model was validated using a natural driving dataset with nine truck drivers on real-world roads in Guiyang, China during June and July 2021. RESULTS Our approach yielded 99.21 %, 84.44 %, 82.01 %, 99.63 %, and 83.21 % in accuracy, precision, recall, specificity, and F1-score, respectively. Compared with the mainstream visual-based methods, our approach outperformed particularly in terms of precision and recall. Photoplethysmogram stood out as the most important feature for truck driver fatigue state detection. Vehicle load, driving forward angle, cumulative driving time, midnight, and recent working hours were found to be positively associated with the probability of fatigue driving, while the galvanic skin response, vehicle acceleration, current time, and recent rest hours had a negative relationship. Specifically, truck drivers were more likely to fatigue when driving at 20-40 km/h, braking abruptly at 5-10 m/s2, with vehicle loads over 70 tons, and driving more than 100 min consecutively. CONCLUSIONS Our study is among the first to harness the natural driving dataset to delve into the real-life fatigue pattern of long-haul truck drivers without disruptions on routine driving tasks. The proposed method holds pragmatic prospects by providing a privacy-preserving, robust, real-time, and non-intrusive technical pathway for truck driver fatigue monitoring.
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Affiliation(s)
- Chen He
- Department of Automation, BNRIST, Tsinghua University, Beijing 100084, China
| | - Pengpeng Xu
- School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, Guangdong, China; Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems, Changsha University of Science & Technology, Changsha 410114, Hunan, China
| | - Xin Pei
- Department of Automation, BNRIST, Tsinghua University, Beijing 100084, China.
| | - Qianfang Wang
- School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, Guangdong, China
| | - Yun Yue
- Department of Automation, BNRIST, Tsinghua University, Beijing 100084, China
| | - Chunyang Han
- Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, Yunnan, China
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Baumann AN, Orellana K, Oleson CJ, Curtis DP, Cahill P, Flynn J, Baldwin KD. The impact of patient scoliosis-specific exercises for adolescent idiopathic scoliosis: a systematic review and meta-analysis of randomized controlled trials with subgroup analysis using observational studies. Spine Deform 2024; 12:545-559. [PMID: 38243155 DOI: 10.1007/s43390-023-00810-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 12/16/2023] [Indexed: 01/21/2024]
Abstract
PURPOSE Adolescent idiopathic scoliosis (AIS) is a common pediatric spinal deformity frequently treated with patient scoliosis-specific exercises (PSSE). The purpose of this study is to perform a systematic review and meta-analysis of randomized controlled trials and sensitivity analysis of observational studies to determine the impact of PSSE on outcomes for AIS. METHODS A systematic review and meta-analysis on impact of PSSE for patients with AIS was performed. Databases used included PubMed, CINAHL, MEDLINE, Cochrane, and ScienceDirect database inception to October 2022. Inclusion criteria included use of PSSE, patient population of AIS, and full text. RESULTS A total of 26 articles out of 628 initial retrieved met final inclusion criteria (10 randomized controlled trials (RCTs), 16 observational studies). Total included patients (n = 2083) had a frequency weighted mean age of 13.2 ± 0.9 years and a frequency weighted mean follow-up of 14.5 ± 20.0 months. Based on only data from RCTs with direct comparison groups (n = 7 articles), there was a statistically significant but clinically insignificant improvement in Cobb angle of 2.5 degrees in the PSSE group (n = 152) as compared to the control group (n = 148; p = 0.017). There was no statistically significant improvement in Cobb angle when stratified by small curve (< 30 degrees) or large curve (> 30 degrees) with PSSE (p = 0.140 and p = 0.142, respectively). There was no statistically significant improvement in ATR (p = 0.326) or SRS-22 score (p = 0.370). CONCLUSION PSSE may not provide any clinically significant improvements in Cobb angle, ATR, or SRS-22 scores in patients with AIS. PSSE did not significantly improve Cobb angle when stratified by curve size. LEVEL OF EVIDENCE Level I.
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Affiliation(s)
- Anthony N Baumann
- Department of Rehabilitation Services, University Hospitals, Cleveland, OH, USA
- College of Medicine, Northeast Ohio Medical University, Rootstown, OH, USA
| | - Kevin Orellana
- Department of Orthopedics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Caleb J Oleson
- College of Medicine, Northeast Ohio Medical University, Rootstown, OH, USA
| | - Deven P Curtis
- College of Medicine, Northeast Ohio Medical University, Rootstown, OH, USA
| | - Patrick Cahill
- Department of Orthopedics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - John Flynn
- Department of Orthopedics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Keith D Baldwin
- Department of Orthopedics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
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Han JH, Jo K. Development of a digital star-shot analysis system for comparing radiation and imaging isocenters of proton treatment machine. J Appl Clin Med Phys 2024; 25:e14320. [PMID: 38454657 PMCID: PMC11087181 DOI: 10.1002/acm2.14320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 01/16/2024] [Accepted: 02/22/2024] [Indexed: 03/09/2024] Open
Abstract
PURPOSE To directly compare the radiation and imaging isocenters of a proton treatment machine, we developed and evaluated a real-time radiation isocenter verification system. METHODS The system consists of a plastic scintillator (PI-200, Mitsubishi Chemical Corporation, Tokyo, Japan), an acrylic phantom, a steel ball on the detachable plate, Raspberry Pi 4 (Raspberry Pi Foundation, London, UK) with camera module, and analysis software implemented through a Python-based graphical user interface (GUI). After kV imaging alignment of the steel ball, the imaging isocenter defined as the position of the steel ball was extracted from the optical image. The proton star-shot was obtained by optical camera because the scintillator converted proton beam into visible light. Then the software computed both the minimum circle radius and the radiation isocenter position from the star-shot. And the deviation between the imaging isocenter and radiation isocenter was calculated. We compared our results with measurements obtained by Gafchromic EBT3 film (Ashland, NJ, USA). RESULTS The minimum circle radii were averaged 0.29 and 0.41 mm while the position deviations from the radiation isocenter to the laser marker were averaged 0.99 and 1.07 mm, for our system and EBT3 film, respectively. Furthermore, the average position difference between the radiation isocenter and imaging isocenter was 0.27 mm for our system. Our system reduced analysis time by 10 min. CONCLUSIONS Our system provided automated star-shot analysis with sufficient accuracy, and it is cost-effective alternative to conventional film-based method for radiation isocenter verification.
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Affiliation(s)
- Ji Hye Han
- Department of PhysicsEwha Womans UniversitySeoulSouth Korea
| | - Kwanghyun Jo
- Department of Radiation OncologySamsung Medical CenterSeoulSouth Korea
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70
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McKenzie RJ, Iskra S, Knipe P. Assessment of radio frequency fields in the 2.45 GHz band produced by smart home devices. Bioelectromagnetics 2024; 45:184-192. [PMID: 38014861 DOI: 10.1002/bem.22492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 08/02/2023] [Accepted: 11/03/2023] [Indexed: 11/29/2023]
Abstract
This paper describes the assessment of the electromagnetic fields produced by consumer "smart" devices used to control and monitor everyday equipment and appliances in a modern "smart" home. The assessment is based on the careful measurement of fields produced by a range of such devices in a laboratory environment configured to operate in a condition simulating high user activity. All devices included in this study operate in the 2.4 GHz band utilizing either Wi-Fi or Bluetooth connectivity. Overall results indicate very low levels of electromagnetic fields for all IoT smart devices in terms of human exposure safety standards (typically much less than 1%) with very low duty cycles (also less than 1%) resulting in even lower time-averaged exposure levels. These low levels of exposure, along with rapid reduction of levels with distance from the devices, suggests that the cumulative effect of multiple devices in a "smart" home are not significant.
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Affiliation(s)
- Raymond J McKenzie
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Steve Iskra
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, Victoria, Australia
- Global Networks and Technology, Telstra Corporation Ltd., Melbourne, Australia
| | - Phillip Knipe
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, Victoria, Australia
- Total Radiation Solutions, Perth, Australia
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Chen Y, Guo Z, Yuan J, Li X, Yu H. Dual-TranSpeckle: Dual-pathway transformer based encoder-decoder network for medical ultrasound image despeckling. Comput Biol Med 2024; 173:108313. [PMID: 38531247 DOI: 10.1016/j.compbiomed.2024.108313] [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: 08/30/2023] [Revised: 03/03/2024] [Accepted: 03/12/2024] [Indexed: 03/28/2024]
Abstract
The majority of existing deep learning-based image denoising algorithms mainly focus on processing the overall image features, ignoring the fine differences between the semantic and pixel features. Hence, we propose Dual-TranSpeckle (DTS), a medical ultrasound image despeckling network built on a dual-path Transformer. The DTS introduces two different paths, named "semantic path" and "pixel path," to facilitate the parallel transfer of feature information within the image. The semantic path passes a global view of the input semantic features, and the image features are passed through a Semantic Block to extract global semantic information from pixel-level features. The pixel path is employed to transmit finer-grained pixel features. Within the dual-path network framework, two essential modules, namely Dual Block and Merge Block, are designed. These leverage the Transformer architecture during the encoding and decoding stages. The Dual Block module facilitates information interaction between the semantic and pixel features by considering the interdependencies across both paths. Meanwhile, the Merge Block module enables parallel transfer of feature information by merging the dual path features, thereby facilitating the self-attention calculations for the overall feature representation. Our DTS is extensively evaluated on two public datasets and one private dataset. The DTS network demonstrates significant enhancements in quantitative evaluation results in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), feature similarity (FSIM), and naturalness image quality evaluator (NIQE). Furthermore, our qualitative analysis confirms that the DTS has significant improvements in despeckling performance, effectively suppressing speckle noise while preserving essential image structures.
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Affiliation(s)
- Yuqing Chen
- School of Electronic and Information Engineering, Hebei University of Technology, Tianjin, 300401, China.
| | - Zhitao Guo
- School of Electronic and Information Engineering, Hebei University of Technology, Tianjin, 300401, China; The Innovation and Research Institute of Hebei University of Technology in Shijiazhuang, Shijiazhuang, 050299, China.
| | - Jinli Yuan
- School of Electronic and Information Engineering, Hebei University of Technology, Tianjin, 300401, China.
| | - Xiaozeng Li
- School of Electronic and Information Engineering, Hebei University of Technology, Tianjin, 300401, China.
| | - Hengyong Yu
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA, 01854, USA.
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Jia H, Zhang J, Ma K, Qiao X, Ren L, Shi X. Application of convolutional neural networks in medical images: a bibliometric analysis. Quant Imaging Med Surg 2024; 14:3501-3518. [PMID: 38720828 PMCID: PMC11074758 DOI: 10.21037/qims-23-1600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 03/06/2024] [Indexed: 05/12/2024]
Abstract
Background In the field of medical imaging, the rapid rise of convolutional neural networks (CNNs) has presented significant opportunities for conserving healthcare resources. However, with the wide spread application of CNNs, several challenges have emerged, such as enormous data annotation costs, difficulties in ensuring user privacy and security, weak model interpretability, and the consumption of substantial computational resources. The fundamental challenge lies in optimizing and seamlessly integrating CNN technology to enhance the precision and efficiency of medical diagnosis. Methods This study sought to provide a comprehensive bibliometric overview of current research on the application of CNNs in medical imaging. Initially, bibliometric methods were used to calculate the frequency statistics, and perform the cluster analysis and the co-citation analysis of countries, institutions, authors, keywords, and references. Subsequently, the latent Dirichlet allocation (LDA) method was employed for the topic modeling of the literature. Next, an in-depth analysis of the topics was conducted, and the topics in the medical field, technical aspects, and trends in topic evolution were summarized. Finally, by integrating the bibliometrics and LDA results, the developmental trajectory, milestones, and future directions in this field were outlined. Results A data set containing 6,310 articles in this field published from January 2013 to December 2023 was complied. With a total of 55,538 articles, the United States led in terms of the citation count, while in terms of the publication volume, China led with 2,385 articles. Harvard University emerged as the most influential institution, boasting an average of 69.92 citations per article. Within the realm of CNNs, residual neural network (ResNet) and U-Net stood out, receiving 1,602 and 1,419 citations, respectively, which highlights the significant attention these models have received. The impact of coronavirus disease 2019 (COVID-19) was unmistakable, as reflected by the publication of 597 articles, making it a focal point of research. Additionally, among various disease topics, with 290 articles, brain-related research was the most prevalent. Computed tomography (CT) imaging dominated the research landscape, representing 73% of the 30 different topics. Conclusions Over the past 11 years, CNN-related research in medical imaging has grown exponentially. The findings of the present study provide insights into the field's status and research hotspots. In addition, this article meticulously chronicled the development of CNNs and highlighted key milestones, starting with LeNet in 1989, followed by a challenging 20-year exploration period, and culminating in the breakthrough moment with AlexNet in 2012. Finally, this article explored recent advancements in CNN technology, including semi-supervised learning, efficient learning, trustworthy artificial intelligence (AI), and federated learning methods, and also addressed challenges related to data annotation costs, diagnostic efficiency, model performance, and data privacy.
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Affiliation(s)
- Huixin Jia
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Jiali Zhang
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Kejun Ma
- School of Statistics, Shandong Technology and Business University, Yantai, China
| | - Xiaoyan Qiao
- School of Mathematics and Information Science, Shandong Technology and Business University, Yantai, China
| | - Lijie Ren
- Department of Neurology, Shenzhen Institute of Translational Medicine, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, China
| | - Xin Shi
- School of Health Management/Institute of Health Sciences, China Medical University, Shenyang, China
- Immersion Technology and Evaluation Shandong Engineering Research Center, Shandong Technology and Business University, Yantai, China
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Robar JL, Cherpak A, MacDonald RL, Yashayaeva A, McAloney D, McMaster N, Zhan K, Cwajna S, Patil N, Dahn H. Novel Technology Allowing Cone Beam Computed Tomography in 6 Seconds: A Patient Study of Comparative Image Quality. Pract Radiat Oncol 2024; 14:277-286. [PMID: 37939844 DOI: 10.1016/j.prro.2023.10.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 10/20/2023] [Accepted: 10/30/2023] [Indexed: 11/10/2023]
Abstract
PURPOSE The goal of this study was to evaluate the image quality provided by a novel cone beam computed tomography (CBCT) platform (HyperSight, Varian Medical Systems), a platform with enhanced reconstruction algorithms as well as rapid acquisition times. Image quality was compared with both status quo CBCT for image guidance, and to fan beam CT (FBCT) acquired on a CT simulator (CTsim). METHODS AND MATERIALS In a clinical study, 30 individuals were recruited for whom either deep inspiration (DIBH) or deep exhalation breath hold (DEBH) was used during imaging and radiation treatment of tumors involving liver, lung, breast, abdomen, chest wall, and pancreatic sites. All subjects were imaged during breath hold with CBCT on a standard image guidance platform (TrueBeam 2.7, Varian Medical Systems) and FBCT CT (CTsim, GE Optima). HyperSight imaging with both breath hold (HSBH) and free breathing (HSFB) was performed in a single session. The 4 image sets thus acquired were registered and compared using metrics quantifying artifact index, image nonuniformity, contrast, contrast-to-noise ratio, and difference of Hounsfield unit (HU) from CTsim. RESULTS HSBH provided less severe artifacts compared with both HSFB and TrueBeam. The severity of artifacts in HSBH images was similar to that in CTsim images, with statistically similar artifact index values. CTsim provided the best image uniformity; however, HSBH provided improved uniformity compared with both HSFB and TrueBeam. CTsim demonstrated elevated contrast compared with HyperSight imaging, but both HSBH and HSFB imaging showed superior contrast-to-noise ratio characteristics compared with TrueBeam. The median HU difference of HSBH from CTsim was within 1 HU for muscle/fat tissue, 12 HU for bone, and 14 HU for lung. CONCLUSIONS The HyperSight system provides 6-second CBCT acquisition with image artifacts that are significantly reduced compared with TrueBeam and comparable to those in CTsim FBCT imaging. HyperSight breath hold imaging was of higher quality compared with free breathing imaging on the same system. The median HU value in HyperSight breath hold imaging is within 15 HU of that in CTsim imaging for muscle, fat, bone, and lung tissue types, indicating the utility of image data for direct dose calculation in adaptive workflows.
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Affiliation(s)
- James L Robar
- Department of Radiation Oncology, QE2 Cancer Centre, Nova Scotia Health, Halifax, Canada; Departments of Radiation Oncology; Physics and Atmospheric Science, Dalhousie University, Halifax, Canada.
| | - Amanda Cherpak
- Department of Radiation Oncology, QE2 Cancer Centre, Nova Scotia Health, Halifax, Canada; Departments of Radiation Oncology; Physics and Atmospheric Science, Dalhousie University, Halifax, Canada
| | - Robert Lee MacDonald
- Department of Radiation Oncology, QE2 Cancer Centre, Nova Scotia Health, Halifax, Canada; Departments of Radiation Oncology; Physics and Atmospheric Science, Dalhousie University, Halifax, Canada
| | | | - David McAloney
- Department of Radiation Oncology, QE2 Cancer Centre, Nova Scotia Health, Halifax, Canada
| | - Natasha McMaster
- Department of Radiation Oncology, QE2 Cancer Centre, Nova Scotia Health, Halifax, Canada
| | - Kenny Zhan
- Department of Radiation Oncology, QE2 Cancer Centre, Nova Scotia Health, Halifax, Canada
| | - Slawa Cwajna
- Department of Radiation Oncology, QE2 Cancer Centre, Nova Scotia Health, Halifax, Canada; Departments of Radiation Oncology
| | - Nikhilesh Patil
- Department of Radiation Oncology, QE2 Cancer Centre, Nova Scotia Health, Halifax, Canada; Departments of Radiation Oncology
| | - Hannah Dahn
- Department of Radiation Oncology, QE2 Cancer Centre, Nova Scotia Health, Halifax, Canada; Departments of Radiation Oncology
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Mota ME, Schroter GT, Moreira MS, Alves FA, Jaguar GC, Lopes RN. 3D printing technology to produce intraoral stents for head and neck radiotherapy: A scoping review. SPECIAL CARE IN DENTISTRY 2024; 44:636-644. [PMID: 37909799 DOI: 10.1111/scd.12936] [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: 09/28/2023] [Revised: 10/12/2023] [Accepted: 10/13/2023] [Indexed: 11/03/2023]
Abstract
INTRODUCTION Radiotherapy remains one of the main treatments for head and neck cancer; however, it is accompanied by acute and chronic adverse effects. Use of three-dimensional (3D) oral stents to modulate radiation intensity to specific target areas have been developed to minimize these adverse effects. This study aimed to present a scoping review of studies published on 3D printing of oral stents and their clinical applicability. METHODS MEDLINE/Pubmed, Scopus, Web of Science and CENTRAL Cochrane data bases were searched, studies selected, and data collected by three independent reviewers up to December 2022. The review was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-analysis-Extension for Scoping Reviews (PRISMA-ScR). RESULTS The search resulted in 404 studies and 5 articles fulfilled the eligibility criteria and were considered for this review. Three-dimensional printed intraoral stents were produced for 56 patients with indication for radiotherapy. 3D-printed stents were well-tolerated by all tested patients and demonstrated great reproducibility of maxillomandibular relation, required less time for production and lower cost to manufacture. Two studies showed great protection of healthy tissues with 3D-printed stents during radiotherapy. CONCLUSIONS Three-dimensional printing is promising for production of intraoral stents, however, more studies are needed to improve the technique and further investigate the safety and prevention of oral toxicities from radiotherapy.
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Affiliation(s)
- Maria Emília Mota
- Department of Stomatology, School of Dentistry, University of São Paulo, São Paulo, São Paulo, Brazil
| | - Gabriella Torres Schroter
- Department of Stomatology, School of Dentistry, University of São Paulo, São Paulo, São Paulo, Brazil
| | - Maria Stella Moreira
- Department of Stomatology, School of Dentistry, University of São Paulo, São Paulo, São Paulo, Brazil
- Department of Stomatology, AC Camargo Cancer Center, São Paulo, São Paulo, Brazil
| | - Fábio Abreu Alves
- Department of Stomatology, School of Dentistry, University of São Paulo, São Paulo, São Paulo, Brazil
- Department of Stomatology, AC Camargo Cancer Center, São Paulo, São Paulo, Brazil
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Zhang X, Heo GS, Li A, Lahad D, Detering L, Tao J, Gao X, Zhang X, Luehmann H, Sultan D, Lou L, Venkatesan R, Li R, Zheng J, Amrute J, Lin CY, Kopecky BJ, Gropler RJ, Bredemeyer A, Lavine K, Liu Y. Development of a CD163-Targeted PET Radiotracer That Images Resident Macrophages in Atherosclerosis. J Nucl Med 2024; 65:775-780. [PMID: 38548349 PMCID: PMC11064833 DOI: 10.2967/jnumed.123.266910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 02/26/2024] [Indexed: 05/03/2024] Open
Abstract
Tissue-resident macrophages are complementary to proinflammatory macrophages to promote the progression of atherosclerosis. The noninvasive detection of their presence and dynamic variation will be important to the understanding of their role in the pathogenesis of atherosclerosis. The goal of this study was to develop a targeted PET radiotracer for imaging CD163-positive (CD163+) macrophages in multiple mouse atherosclerosis models and assess the potential of CD163 as a biomarker for atherosclerosis in humans. Methods: CD163-binding peptide was identified using phage display and conjugated with a NODAGA chelator for 64Cu radiolabeling ([64Cu]Cu-ICT-01). CD163-overexpressing U87 cells were used to measure the binding affinity of [64Cu]Cu-ICT-01. Biodistribution studies were performed on wild-type C57BL/6 mice at multiple time points after tail vein injection. The sensitivity and specificity of [64Cu]Cu-ICT-01 in imaging CD163+ macrophages upregulated on the surface of atherosclerotic plaques were assessed in multiple mouse atherosclerosis models. Immunostaining, flow cytometry, and single-cell RNA sequencing were performed to characterize the expression of CD163 on tissue-resident macrophages. Human carotid atherosclerotic plaques were used to measure the expression of CD163+ resident macrophages and test the binding specificity of [64Cu]Cu-ICT-01. Results: [64Cu]Cu-ICT-01 showed high binding affinity to U87 cells. The biodistribution study showed rapid blood and renal clearance with low retention in all major organs at 1, 2, and 4 h after injection. In an ApoE-/- mouse model, [64Cu]Cu-ICT-01 demonstrated sensitive and specific detection of CD163+ macrophages and capability for tracking the progression of atherosclerotic lesions; these findings were further confirmed in Ldlr-/- and PCSK9 mouse models. Immunostaining showed elevated expression of CD163+ macrophages across the plaques. Flow cytometry and single-cell RNA sequencing confirmed the specific expression of CD163 on tissue-resident macrophages. Human tissue characterization demonstrated high expression of CD163+ macrophages on atherosclerotic lesions, and ex vivo autoradiography revealed specific binding of [64Cu]Cu-ICT-01 to human CD163. Conclusion: This work reported the development of a PET radiotracer binding CD163+ macrophages. The elevated expression of CD163+ resident macrophages on human plaques indicated the potential of CD163 as a biomarker for vulnerable plaques. The sensitivity and specificity of [64Cu]Cu-ICT-01 in imaging CD163+ macrophages warrant further investigation in translational settings.
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Affiliation(s)
- Xiuli Zhang
- Mallinckrodt Institute of Radiology, University of Missouri, Columbia, Missouri
| | - Gyu Seong Heo
- Mallinckrodt Institute of Radiology, University of Missouri, Columbia, Missouri
| | - Alexandria Li
- Mallinckrodt Institute of Radiology, University of Missouri, Columbia, Missouri
| | - Divangana Lahad
- Mallinckrodt Institute of Radiology, University of Missouri, Columbia, Missouri
| | - Lisa Detering
- Mallinckrodt Institute of Radiology, University of Missouri, Columbia, Missouri
| | - Joan Tao
- Department of Medicine, University of Missouri, Columbia, Missouri
| | - Xuefeng Gao
- Mallinckrodt Institute of Radiology, University of Missouri, Columbia, Missouri
| | - Xiaohui Zhang
- Mallinckrodt Institute of Radiology, University of Missouri, Columbia, Missouri
| | - Hannah Luehmann
- Mallinckrodt Institute of Radiology, University of Missouri, Columbia, Missouri
| | - Deborah Sultan
- Mallinckrodt Institute of Radiology, University of Missouri, Columbia, Missouri
| | - Lanlan Lou
- Mallinckrodt Institute of Radiology, University of Missouri, Columbia, Missouri
| | - Rajiu Venkatesan
- Mallinckrodt Institute of Radiology, University of Missouri, Columbia, Missouri
| | - Ran Li
- Mallinckrodt Institute of Radiology, University of Missouri, Columbia, Missouri
| | - Jie Zheng
- Mallinckrodt Institute of Radiology, University of Missouri, Columbia, Missouri
| | - Junedh Amrute
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri; and
| | - Chieh-Yu Lin
- Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, Missouri
| | - Benjamin J Kopecky
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri; and
| | - Robert J Gropler
- Mallinckrodt Institute of Radiology, University of Missouri, Columbia, Missouri
| | - Andrea Bredemeyer
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri; and
| | - Kory Lavine
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri; and
| | - Yongjian Liu
- Mallinckrodt Institute of Radiology, University of Missouri, Columbia, Missouri;
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Hu J, Peng J, Zhou Z, Zhao T, Zhong L, Yu K, Jiang K, Lau TS, Huang C, Lu L, Zhang X. Associating Knee Osteoarthritis Progression with Temporal-Regional Graph Convolutional Network Analysis on MR Images. J Magn Reson Imaging 2024. [PMID: 38686707 DOI: 10.1002/jmri.29412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 04/09/2024] [Accepted: 04/10/2024] [Indexed: 05/02/2024] Open
Abstract
BACKGROUND Artificial intelligence shows promise in assessing knee osteoarthritis (OA) progression on MR images, but faces challenges in accuracy and interpretability. PURPOSE To introduce a temporal-regional graph convolutional network (TRGCN) on MR images to study the association between knee OA progression status and network outcome. STUDY TYPE Retrospective. POPULATION 194 OA progressors (mean age, 62 ± 9 years) and 406 controls (mean age, 61 ± 9 years) from the OA Initiative were randomly divided into training (80%) and testing (20%) cohorts. FIELD STRENGTH/SEQUENCE Sagittal 2D IW-TSE-FS (IW) and 3D-DESS-WE (DESS) at 3T. ASSESSMENT Anatomical subregions of cartilage, subchondral bone, meniscus, and the infrapatellar fat pad at baseline, 12-month, and 24-month were automatically segmented and served as inputs to form compartment-based graphs for a TRGCN model, which containing both regional and temporal information. The performance of models based on (i) clinical variables alone, (ii) radiologist score alone, (iii) combined features (containing i and ii), (iv) composite TRGCN (combining TRGCN, i and ii), (v) radiomics features, (vi) convolutional neural network based on Densenet-169 were compared. STATISTICAL TESTS DeLong test was performed to compare the areas under the ROC curve (AUC) of all models. Additionally, interpretability analysis was done to evaluate the contributions of individual regions. A P value <0.05 was considered significant. RESULTS The composite TRGCN outperformed all other models with AUCs of 0.841 (DESS) and 0.856 (IW) in the testing cohort (all P < 0.05). Interpretability analysis highlighted cartilage's importance over other structures (42%-45%), tibiofemoral joint's (TFJ) dominance over patellofemoral joint (PFJ) (58%-67% vs. 12%-37%), and importance scores changes in compartments over time (TFJ vs. PFJ: baseline: 44% vs. 43%, 12-month: 52% vs. 39%, 24-month: 31% vs. 48%). DATA CONCLUSION The composite TRGCN, capturing temporal and regional information, demonstrated superior discriminative ability compared with other methods, providing interpretable insights for identifying knee OA progression. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY Stage 2.
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Affiliation(s)
- Jiaping Hu
- Department of Medical Imaging, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics· Guangdong Province), Guangzhou, China
| | - Junyi Peng
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Zidong Zhou
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Tianyun Zhao
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, Georgia, USA
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, New York, USA
| | - Lijie Zhong
- Department of Medical Imaging, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics· Guangdong Province), Guangzhou, China
| | - Keyan Yu
- Department of Radiology, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Kexin Jiang
- Department of Medical Imaging, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics· Guangdong Province), Guangzhou, China
| | - Tzak Sing Lau
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Chuan Huang
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, Georgia, USA
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Lijun Lu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
- Pazhou Lab, Guangzhou, China
| | - Xiaodong Zhang
- Department of Medical Imaging, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics· Guangdong Province), Guangzhou, China
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Wang ML, Tie CW, Wang JH, Zhu JQ, Chen BH, Li Y, Zhang S, Liu L, Guo L, Yang L, Yang LQ, Wei J, Jiang F, Zhao ZQ, Wang GQ, Zhang W, Zhang QM, Ni XG. Multi-instance learning based artificial intelligence model to assist vocal fold leukoplakia diagnosis: A multicentre diagnostic study. Am J Otolaryngol 2024; 45:104342. [PMID: 38703609 DOI: 10.1016/j.amjoto.2024.104342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 04/23/2024] [Indexed: 05/06/2024]
Abstract
OBJECTIVE To develop a multi-instance learning (MIL) based artificial intelligence (AI)-assisted diagnosis models by using laryngoscopic images to differentiate benign and malignant vocal fold leukoplakia (VFL). METHODS The AI system was developed, trained and validated on 5362 images of 551 patients from three hospitals. Automated regions of interest (ROI) segmentation algorithm was utilized to construct image-level features. MIL was used to fusion image level results to patient level features, then the extracted features were modeled by seven machine learning algorithms. Finally, we evaluated the image level and patient level results. Additionally, 50 videos of VFL were prospectively gathered to assess the system's real-time diagnostic capabilities. A human-machine comparison database was also constructed to compare the diagnostic performance of otolaryngologists with and without AI assistance. RESULTS In internal and external validation sets, the maximum area under the curve (AUC) for image level segmentation models was 0.775 (95 % CI 0.740-0.811) and 0.720 (95 % CI 0.684-0.756), respectively. Utilizing a MIL-based fusion strategy, the AUC at the patient level increased to 0.869 (95 % CI 0.798-0.940) and 0.851 (95 % CI 0.756-0.945). For real-time video diagnosis, the maximum AUC at the patient level reached 0.850 (95 % CI, 0.743-0.957). With AI assistance, the AUC improved from 0.720 (95 % CI 0.682-0.755) to 0.808 (95 % CI 0.775-0.839) for senior otolaryngologists and from 0.647 (95 % CI 0.608-0.686) to 0.807 (95 % CI 0.773-0.837) for junior otolaryngologists. CONCLUSIONS The MIL based AI-assisted diagnosis system can significantly improve the diagnostic performance of otolaryngologists for VFL and help to make proper clinical decisions.
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Affiliation(s)
- Mei-Ling Wang
- Department of Endoscopy, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Shenzhen, China
| | - Cheng-Wei Tie
- Department of Endoscopy, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Jian-Hui Wang
- Department of Endoscopy, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China
| | - Ji-Qing Zhu
- Department of Endoscopy, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Bing-Hong Chen
- Department of Endoscopy, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Shenzhen, China
| | - Ying Li
- Department of Endoscopy, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Shenzhen, China
| | - Sen Zhang
- Department of Otolaryngology Head and Neck Surgery, The First Hospital, Shanxi Medical University, Taiyuan, China
| | - Lin Liu
- Department of Otolaryngology Head and Neck Surgery, Dalian Friendship Hospital, Dalian, China
| | - Li Guo
- Department of Otolaryngology Head and Neck Surgery, the First Affiliated Hospital, College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
| | - Long Yang
- Department of Otolaryngology, The Second People's Hospital of Baoshan City, Baoshan, China
| | - Li-Qun Yang
- Department of Otolaryngology, The Second People's Hospital of Baoshan City, Baoshan, China
| | - Jiao Wei
- Department of Otolaryngology, Qujing Second People's Hospital of Yunnan Province, Qujing, China
| | - Feng Jiang
- Department of Otolaryngology, Kunming First People's Hospital, Kunming, China
| | - Zhi-Qiang Zhao
- Department of Otolaryngology, Baoshan People's Hospital, Baoshan, China
| | - Gui-Qi Wang
- Department of Endoscopy, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.
| | - Wei Zhang
- Department of Endoscopy, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Shenzhen, China.
| | - Quan-Mao Zhang
- Department of Endoscopy, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China.
| | - Xiao-Guang Ni
- Department of Endoscopy, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.
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Balogh ZA, Barna Z, Majoros E. Comparison of iterative reconstruction implementations for multislice helical CT. Z Med Phys 2024:S0939-3889(24)00046-1. [PMID: 38679541 DOI: 10.1016/j.zemedi.2024.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 02/20/2024] [Accepted: 04/03/2024] [Indexed: 05/01/2024]
Abstract
The most mature image reconstruction algorithms in multislice helical computed tomography are based on analytical and iterative methods. Over the past decades, several methods have been developed for iterative reconstructions that improve image quality by reducing noise and artifacts. In the regularization step of iterative reconstruction, noise can be significantly reduced, thereby making low-dose CT. The quality of the reconstructed image can be further improved by using model-based reconstructions. In these reconstructions, the main focus is on modeling the data acquisition process, including the behavior of the photon beams, the geometry of the system, etc. In this article, we propose two model-based reconstruction algorithms using a virtual detector for multislice helical CT. The aim of this study is to compare the effect of using a virtual detector on image quality for the two proposed algorithms with a model-based iterative reconstruction using the original detector model. Since the algorithms are implemented using multiple GPUs, the merging of separately reconstructed volumes can significantly affect image quality. This issue is often referred to as the "long object" problem, for which we also present a solution that plays an important role in the proposed reconstruction processes. The algorithms were evaluated using mathematical and physical phantoms, as well as patient cases. The SSIM, MS-SSIM and L1 metrics were utilized to evaluate the image quality of the mathematical phantom case. To demonstrate the effectiveness of the algorithms, we used the CatPhan 600 phantom. Additionally, anonymized patient scans were used to showcase the improvements in image quality on real scan data.
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Affiliation(s)
- Zsolt Adam Balogh
- Department of Mathematical Sciences, United Arab Emirates University, Al Ain P.O.Box: 15551, United Arab Emirates.
| | | | - Eva Majoros
- Marton Varga Technical College, Budapest H-1149, Hungary
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Tanner IL, Ye K, Moore MS, Rechenmacher AJ, Ramirez MM, George SZ, Bolognesi MP, Horn ME. Developing a Computer Vision Model to Automate Quantitative Measurement of Hip-Knee-Ankle Angle in Total Hip and Knee Arthroplasty Patients. J Arthroplasty 2024:S0883-5403(24)00410-8. [PMID: 38679347 DOI: 10.1016/j.arth.2024.04.062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 04/19/2024] [Accepted: 04/21/2024] [Indexed: 05/01/2024] Open
Abstract
BACKGROUND Increasing deformity of the lower extremities, as measured by the hip-knee-ankle angle (HKAA), is associated with poor patient outcomes after total hip and knee arthroplasty (THA, TKA). Automated calculation of HKAA is imperative to reduce the burden on orthopaedic surgeons. We proposed a detection-based deep learning (DL) model to calculate HKAA in THA and TKA patients and assessed the agreement between DL-derived HKAAs and manual measurement. METHODS We retrospectively identified 1,379 long-leg radiographs (LLRs) from patients scheduled for THA or TKA within an academic medical center. There were 1,221 LLRs used to develop the model (randomly split into 70% training, 20% validation, and 10% held-out test sets); 158 LLRs were considered "difficult," as the femoral head was difficult to distinguish from surrounding tissue. There were 2 raters who annotated the HKAA of both lower extremities, and inter-rater reliability was calculated to compare the DL-derived HKAAs with manual measurement within the test set. RESULTS The DL model achieved a mean average precision of 0.985 on the test set. The average HKAA of the operative leg was 173.05 ± 4.54°; the nonoperative leg was 175.55 ± 3.56°. The inter-rater reliability between manual and DL-derived HKAA measurements on the operative leg and nonoperative leg indicated excellent reliability (intraclass correlation (2,k) = 0.987 [0.96, 0.99], intraclass correlation (2, k) = 0.987 [0.98, 0.99, respectively]). The standard error of measurement for the DL-derived HKAA for the operative and nonoperative legs was 0.515° and 0.403°, respectively. CONCLUSIONS A detection-based DL algorithm can calculate the HKAA in LLRs and is comparable to that calculated by manual measurement. The algorithm can detect the bilateral femoral head, knee, and ankle joints with high precision, even in patients where the femoral head is difficult to visualize.
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Affiliation(s)
- Irene L Tanner
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina
| | - Ken Ye
- Trinity College of Arts & Sciences, Duke University, Durham, North Carolina
| | - Miles S Moore
- Physical Therapy Division, Duke University School of Medicine, Durham, North Carolina
| | - Albert J Rechenmacher
- Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, North Carolina
| | - Michelle M Ramirez
- Department of Population Health Sciences, Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, North Carolina
| | - Steven Z George
- Department of Orthopaedic Surgery, Department of Population Health Sciences, Duke Clinical Research Institute, Duke University, Durham, North Carolina
| | | | - Maggie E Horn
- Department of Population Health Sciences, Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, North Carolina
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Calvente I, Núñez MI. Is the sustainability of exposure to non-ionizing electromagnetic radiation possible? Med Clin (Barc) 2024; 162:387-393. [PMID: 38151370 DOI: 10.1016/j.medcli.2023.11.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 11/07/2023] [Accepted: 11/08/2023] [Indexed: 12/29/2023]
Abstract
Technological advances imply an increase in artificially generating sources of electromagnetic fields (EMF), therefore, resulting in a permanent exposure of people and the environment (electromagnetic pollution). Inconsistent results have been published considering the evaluated health effects. The purpose of this study was to review scientific literature on EMF to provide a global and retrospective perspective, on the association between human exposure to non-ionizing radiation (NIR, mainly radiofrequency-EMF) and health and environmental effects. Studies on the health effects of 5G radiation exposure have not yet been performed with sufficient statistical power, as the exposure time is still relatively short and also the latency and intensity of exposure to 5G. The safety standards only consider thermal effects, do not contemplate non-thermal effects. We consider relevant to communicate this knowledge to the general public to improve education in this field, and to healthcare professionals to prevent diseases that may result from RF-EMF exposures.
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Affiliation(s)
- Irene Calvente
- Research Support Unit, Biosanitary Institute of Granada (ibs.GRANADA), University Hospital Complex of Granada, Spain
| | - María Isabel Núñez
- Research Support Unit, Biosanitary Institute of Granada (ibs.GRANADA), University Hospital Complex of Granada, Spain; Department of Radiology and Physical Medicine, School of Medicine, University of Granada, Granada, Spain; Biopathology and Regenerative Medicine Institute (IBIMER), University of Granada, Spain.
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81
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Liu Y, Li X, Liu S, Liang T, Wu Y, Wang X, Li Y, Xu Y. Study on Gamma sensory flicker for Insomnia. Int J Neurosci 2024:1-11. [PMID: 38629395 DOI: 10.1080/00207454.2024.2342974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 04/09/2024] [Indexed: 04/26/2024]
Abstract
OBJECTIVES Insomnia has been the subject of much systematic research because it is a risk factor for a variety of diseases. There is some evidence that gamma sensory stimulation therapy has also been demonstrated to improve sleep quality for people with Alzheimer's disease. However, it is unclear whether this method is effective for treating insomnia. The principal objective of this project was to investigate the efficacy and safety of gamma sensory flicker in improving the sleep quality of insomnia patients. METHODS Thirty-seven participants with insomnia were recruited for this prospective observational study. For a duration of 8 weeks, participants were exposed to flicker stimulation through a light and sound device. RESULTS During the main phase of the study, adherence rates averaged 92.21%. Additionally, no severe adverse events were reported for flicker treatment. Analysis of sleep diaries indicated that 40 Hz flickers can enhance sleep quality by reducing sleep onset latencies, and arousals, and increasing total sleep duration. CONCLUSIONS Gamma sensory flicker improves sleep quality in people suffering from insomnia.
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Affiliation(s)
- Yakun Liu
- Department of Psychiatry, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
- First Clinical Medical College, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Xinrong Li
- Department of Psychiatry, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
- Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, Taiyuan, Shanxi, China
| | - Sha Liu
- Department of Psychiatry, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
- Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, Taiyuan, Shanxi, China
| | - Tailing Liang
- Department of Psychiatry, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Yan Wu
- Department of Psychiatry, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Xiaopan Wang
- Department of Psychiatry, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Ying Li
- Department of Psychiatry, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Yong Xu
- Department of Psychiatry, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
- First Clinical Medical College, Shanxi Medical University, Taiyuan, Shanxi, China
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82
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Sun S, Li L, Xu M, Wei Y, Shi F, Liu S. Epstein-Barr virus positive gastric cancer: the pathological basis of CT findings and radiomics models prediction. Abdom Radiol (NY) 2024:10.1007/s00261-024-04306-8. [PMID: 38656367 DOI: 10.1007/s00261-024-04306-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Revised: 03/17/2024] [Accepted: 03/20/2024] [Indexed: 04/26/2024]
Abstract
PURPOSE To analyze the clinicopathologic information and CT imaging features of Epstein-Barr virus (EBV)-positive gastric cancer (GC) and establish CT-based radiomics models to predict the EBV status of GC. METHODS This retrospective study included 144 GC cases, including 48 EBV-positive cases. Pathological and immunohistochemical information was collected. CT enlarged LN and morphological characteristics were also assessed. Radiomics models were constructed to predict the EBV status, including decision tree (DT), logistic regression (LR), random forest (RF), and support vector machine (SVM). RESULTS T stage, Lauren classification, histological differentiation, nerve invasion, VEGFR2, E-cadherin, PD-L1, and Ki67 differed significantly between the EBV-positive and -negative groups (p = 0.015, 0.030, 0.006, 0.022, 0.028, 0.030, < 0.001, and < 0.001, respectively). CT enlarged LN and large ulceration differed significantly between the two groups (p = 0.019 and 0.043, respectively). The number of patients in the training and validation cohorts was 100 (with 33 EBV-positive cases) and 44 (with 15 EBV-positive cases). In the training cohort, the radiomics models using DT, LR, RF, and SVM yielded areas under the curve (AUCs) of 0.905, 0.771, 0.836, and 0.886, respectively. In the validation cohort, the diagnostic efficacy of radiomics models using the four classifiers were 0.737, 0.722, 0.751, and 0.713, respectively. CONCLUSION A significantly higher proportion of CT enlarged LN and a significantly lower proportion of large ulceration were found in EBV-positive GC. The prediction efficiency of radiomics models with different classifiers to predict EBV status in GC was good.
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Affiliation(s)
- Shuangshuang Sun
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China
| | - Lin Li
- Department of Pathology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210008, China
| | - Mengying Xu
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China
| | - Ying Wei
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, 200000, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, 200000, China
| | - Song Liu
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China.
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83
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Mostafavi M, Ko SB, Shokouhi SB, Ayatollahi A. Transfer learning and self-distillation for automated detection of schizophrenia using single-channel EEG and scalogram images. Phys Eng Sci Med 2024:10.1007/s13246-024-01420-1. [PMID: 38652347 DOI: 10.1007/s13246-024-01420-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 03/25/2024] [Indexed: 04/25/2024]
Abstract
Schizophrenia (SZ) has been acknowledged as a highly intricate mental disorder for a long time. In fact, individuals with SZ experience a blurred line between fantasy and reality, leading to a lack of awareness about their condition, which can pose significant challenges during the treatment process. Due to the importance of the issue, timely diagnosis of this illness can not only assist patients and their families in managing the condition but also enable early intervention, which may help prevent its advancement. EEG is a widely utilized technique for investigating mental disorders like SZ due to its non-invasive nature, affordability, and wide accessibility. In this study, our main goal is to develop an optimized system that can achieve automatic diagnosis of SZ with minimal input information. To optimize the system, we adopted a strategy of using single-channel EEG signals and integrated knowledge distillation and transfer learning techniques into the model. This approach was designed to improve the performance and efficiency of our proposed method for SZ diagnosis. Additionally, to leverage the pre-trained models effectively, we converted the EEG signals into images using Continuous Wavelet Transform (CWT). This transformation allowed us to harness the capabilities of pre-trained models in the image domain, enabling automatic SZ detection with enhanced efficiency. To achieve a more robust estimate of the model's performance, we employed fivefold cross-validation. The accuracy achieved from the 5-s records of the EEG signal, along with the combination of self-distillation and VGG16 for the P4 channel, is 97.81. This indicates a high level of accuracy in diagnosing SZ using the proposed method.
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Affiliation(s)
- Mohammadreza Mostafavi
- Department of Electrical Engineering, Electronic Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Seok-Bum Ko
- Division of Biomedical Engineering, Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, Canada.
| | - Shahriar Baradaran Shokouhi
- Department of Electrical Engineering, Electronic Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Ahmad Ayatollahi
- Department of Electrical Engineering, Electronic Engineering, Iran University of Science and Technology, Tehran, Iran
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Driban M, Yan A, Selvam A, Ong J, Vupparaboina KK, Chhablani J. Artificial intelligence in chorioretinal pathology through fundoscopy: a comprehensive review. Int J Retina Vitreous 2024; 10:36. [PMID: 38654344 PMCID: PMC11036694 DOI: 10.1186/s40942-024-00554-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 04/02/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND Applications for artificial intelligence (AI) in ophthalmology are continually evolving. Fundoscopy is one of the oldest ocular imaging techniques but remains a mainstay in posterior segment imaging due to its prevalence, ease of use, and ongoing technological advancement. AI has been leveraged for fundoscopy to accomplish core tasks including segmentation, classification, and prediction. MAIN BODY In this article we provide a review of AI in fundoscopy applied to representative chorioretinal pathologies, including diabetic retinopathy and age-related macular degeneration, among others. We conclude with a discussion of future directions and current limitations. SHORT CONCLUSION As AI evolves, it will become increasingly essential for the modern ophthalmologist to understand its applications and limitations to improve patient outcomes and continue to innovate.
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Affiliation(s)
- Matthew Driban
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Audrey Yan
- Department of Medicine, West Virginia School of Osteopathic Medicine, Lewisburg, WV, USA
| | - Amrish Selvam
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Joshua Ong
- Michigan Medicine, University of Michigan, Ann Arbor, USA
| | | | - Jay Chhablani
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
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85
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Safari M, Shalbaf R, Bagherzadeh S, Shalbaf A. Classification of mental workload using brain connectivity and machine learning on electroencephalogram data. Sci Rep 2024; 14:9153. [PMID: 38644365 PMCID: PMC11033270 DOI: 10.1038/s41598-024-59652-w] [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: 02/10/2024] [Accepted: 04/12/2024] [Indexed: 04/23/2024] Open
Abstract
Mental workload refers to the cognitive effort required to perform tasks, and it is an important factor in various fields, including system design, clinical medicine, and industrial applications. In this paper, we propose innovative methods to assess mental workload from EEG data that use effective brain connectivity for the purpose of extracting features, a hierarchical feature selection algorithm to select the most significant features, and finally machine learning models. We have used the Simultaneous Task EEG Workload (STEW) dataset, an open-access collection of raw EEG data from 48 subjects. We extracted brain-effective connectivities by the direct directed transfer function and then selected the top 30 connectivities for each standard frequency band. Then we applied three feature selection algorithms (forward feature selection, Relief-F, and minimum-redundancy-maximum-relevance) on the top 150 features from all frequencies. Finally, we applied sevenfold cross-validation on four machine learning models (support vector machine (SVM), linear discriminant analysis, random forest, and decision tree). The results revealed that SVM as the machine learning model and forward feature selection as the feature selection method work better than others and could classify the mental workload levels with accuracy equal to 89.53% (± 1.36).
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Affiliation(s)
| | - Reza Shalbaf
- Institute for Cognitive Science Studies, Tehran, Iran.
| | - Sara Bagherzadeh
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Ahmad Shalbaf
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Nowroozi A, Salehi MA, Shobeiri P, Agahi S, Momtazmanesh S, Kaviani P, Kalra MK. Artificial intelligence diagnostic accuracy in fracture detection from plain radiographs and comparing it with clinicians: a systematic review and meta-analysis. Clin Radiol 2024:S0009-9260(24)00200-9. [PMID: 38772766 DOI: 10.1016/j.crad.2024.04.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 04/09/2024] [Accepted: 04/15/2024] [Indexed: 05/23/2024]
Abstract
PURPOSE Fracture detection is one of the most commonly used and studied aspects of artificial intelligence (AI) in medicine. In this systematic review and meta-analysis, we aimed to summarize available literature and data regarding AI performance in fracture detection on plain radiographs and various factors affecting it. METHODS We systematically reviewed studies evaluating AI algorithms in detecting bone fractures in plain radiographs, combined their performance using meta-analysis (a bivariate regression approach), and compared it with that of clinicians. We also analyzed the factors potentially affecting algorithm performance using meta-regression. RESULTS Our analysis included 100 studies. In 83 studies with confusion matrices, AI algorithms showed a sensitivity of 91.43% and a specificity of 92.12% (Area under the summary receiver operator curve = 0.968). After adjustment and false discovery rate correction, tibia/fibula (excluding ankle) fractures were associated with higher (7.0%, p=0.004) AI sensitivity, while more recent publications (5.5%, p=0.003) and Xception architecture (6.6%, p<0.001) were associated with higher specificity. Clinicians and AI showed similar specificity in fracture identification, although AI leaned to higher sensitivity (7.6%, p=0.07). Radiologists, on the other hand, were more specific than AI overall and in several subgroups, and more sensitive to hip fractures before FDR correction. CONCLUSIONS Currently available AI aids could result in a significant improvement in care where radiologists are not readily available. Moreover, identifying factors affecting algorithm performance could guide AI development teams in their process of optimizing their products.
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Affiliation(s)
- A Nowroozi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - M A Salehi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - P Shobeiri
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - S Agahi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - S Momtazmanesh
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - P Kaviani
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - M K Kalra
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.
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Hossain T, Shamrat FMJM, Zhou X, Mahmud I, Mazumder MSA, Sharmin S, Gururajan R. Development of a multi-fusion convolutional neural network (MF-CNN) for enhanced gastrointestinal disease diagnosis in endoscopy image analysis. PeerJ Comput Sci 2024; 10:e1950. [PMID: 38660192 PMCID: PMC11041948 DOI: 10.7717/peerj-cs.1950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 02/29/2024] [Indexed: 04/26/2024]
Abstract
Gastrointestinal (GI) diseases are prevalent medical conditions that require accurate and timely diagnosis for effective treatment. To address this, we developed the Multi-Fusion Convolutional Neural Network (MF-CNN), a deep learning framework that strategically integrates and adapts elements from six deep learning models, enhancing feature extraction and classification of GI diseases from endoscopic images. The MF-CNN architecture leverages truncated and partially frozen layers from existing models, augmented with novel components such as Auxiliary Fusing Layers (AuxFL), Fusion Residual Block (FuRB), and Alpha Dropouts (αDO) to improve precision and robustness. This design facilitates the precise identification of conditions such as ulcerative colitis, polyps, esophagitis, and healthy colons. Our methodology involved preprocessing endoscopic images sourced from open databases, including KVASIR and ETIS-Larib Polyp DB, using adaptive histogram equalization (AHE) to enhance their quality. The MF-CNN framework supports detailed feature mapping for improved interpretability of the model's internal workings. An ablation study was conducted to validate the contribution of each component, demonstrating that the integration of AuxFL, αDO, and FuRB played a crucial part in reducing overfitting and efficiency saturation and enhancing overall model performance. The MF-CNN demonstrated outstanding performance in terms of efficacy, achieving an accuracy rate of 99.25%. It also excelled in other key performance metrics with a precision of 99.27%, a recall of 99.25%, and an F1-score of 99.25%. These metrics confirmed the model's proficiency in accurate classification and its capability to minimize false positives and negatives across all tested GI disease categories. Furthermore, the AUC values were exceptional, averaging 1.00 for both test and validation sets, indicating perfect discriminative ability. The findings of the P-R curve analysis and confusion matrix further confirmed the robust classification performance of the MF-CNN. This research introduces a technique for medical imaging that can potentially transform diagnostics in gastrointestinal healthcare facilities worldwide.
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Affiliation(s)
- Tanzim Hossain
- Department of Software Engineering, Daffodil International University, Dhaka, Bangladesh
| | | | - Xujuan Zhou
- School of Business, University of Southern Queensland, Springfield, Australia
| | - Imran Mahmud
- Department of Software Engineering, Daffodil International University, Dhaka, Bangladesh
| | - Md. Sakib Ali Mazumder
- Department of Software Engineering, Daffodil International University, Dhaka, Bangladesh
| | - Sharmin Sharmin
- Department of Computer System and Technology, University of Malaya, Kuala Lumpur, Malaysia
| | - Raj Gururajan
- School of Business, University of Southern Queensland, Springfield, Australia
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Anand V, Koundal D, Alghamdi WY, Alsharbi BM. Smart grading of diabetic retinopathy: an intelligent recommendation-based fine-tuned EfficientNetB0 framework. Front Artif Intell 2024; 7:1396160. [PMID: 38694880 PMCID: PMC11062181 DOI: 10.3389/frai.2024.1396160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 03/27/2024] [Indexed: 05/04/2024] Open
Abstract
Diabetic retinopathy is a condition that affects the retina and causes vision loss due to blood vessel destruction. The retina is the layer of the eye responsible for visual processing and nerve signaling. Diabetic retinopathy causes vision loss, floaters, and sometimes blindness; however, it often shows no warning signals in the early stages. Deep learning-based techniques have emerged as viable options for automated illness classification as large-scale medical imaging datasets have become more widely available. To adapt to medical image analysis tasks, transfer learning makes use of pre-trained models to extract high-level characteristics from natural images. In this research, an intelligent recommendation-based fine-tuned EfficientNetB0 model has been proposed for quick and precise assessment for the diagnosis of diabetic retinopathy from fundus images, which will help ophthalmologists in early diagnosis and detection. The proposed EfficientNetB0 model is compared with three transfer learning-based models, namely, ResNet152, VGG16, and DenseNet169. The experimental work is carried out using publicly available datasets from Kaggle consisting of 3,200 fundus images. Out of all the transfer learning models, the EfficientNetB0 model has outperformed with an accuracy of 0.91, followed by DenseNet169 with an accuracy of 0.90. In comparison to other approaches, the proposed intelligent recommendation-based fine-tuned EfficientNetB0 approach delivers state-of-the-art performance on the accuracy, recall, precision, and F1-score criteria. The system aims to assist ophthalmologists in early detection, potentially alleviating the burden on healthcare units.
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Affiliation(s)
- Vatsala Anand
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India
| | - Deepika Koundal
- School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India
- Ho Chi Minh City Open University, Ho Chi Minh City, Vietnam
| | - Wael Y. Alghamdi
- Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia
| | - Bayan M. Alsharbi
- Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia
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89
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Fan M, Cao X, Lü F, Xie S, Yu Z, Chen Y, Lü Z, Li L. Generative adversarial network-based synthesis of contrast-enhanced MR images from precontrast images for predicting histological characteristics in breast cancer. Phys Med Biol 2024; 69:095002. [PMID: 38537294 DOI: 10.1088/1361-6560/ad3889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 03/27/2024] [Indexed: 04/16/2024]
Abstract
Objective. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a sensitive tool for assessing breast cancer by analyzing tumor blood flow, but it requires gadolinium-based contrast agents, which carry risks such as brain retention and astrocyte migration. Contrast-free MRI is thus preferable for patients with renal impairment or who are pregnant. This study aimed to investigate the feasibility of generating contrast-enhanced MR images from precontrast images and to evaluate the potential use of synthetic images in diagnosing breast cancer.Approach. This retrospective study included 322 women with invasive breast cancer who underwent preoperative DCE-MRI. A generative adversarial network (GAN) based postcontrast image synthesis (GANPIS) model with perceptual loss was proposed to generate contrast-enhanced MR images from precontrast images. The quality of the synthesized images was evaluated using the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The diagnostic performance of the generated images was assessed using a convolutional neural network to predict Ki-67, luminal A and histological grade with the area under the receiver operating characteristic curve (AUC). The patients were divided into training (n= 200), validation (n= 60), and testing sets (n= 62).Main results. Quantitative analysis revealed strong agreement between the generated and real postcontrast images in the test set, with PSNR and SSIM values of 36.210 ± 2.670 and 0.988 ± 0.006, respectively. The generated postcontrast images achieved AUCs of 0.918 ± 0.018, 0.842 ± 0.028 and 0.815 ± 0.019 for predicting the Ki-67 expression level, histological grade, and luminal A subtype, respectively. These results showed a significant improvement compared to the use of precontrast images alone, which achieved AUCs of 0.764 ± 0.031, 0.741 ± 0.035, and 0.797 ± 0.021, respectively.Significance. This study proposed a GAN-based MR image synthesis method for breast cancer that aims to generate postcontrast images from precontrast images, allowing the use of contrast-free images to simulate kinetic features for improved diagnosis.
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Affiliation(s)
- Ming Fan
- Institute of Intelligent Biomedicine, Hangzhou Dianzi University,Hangzhou 310018, Zhejiang, People's Republic of China
| | - Xuan Cao
- Institute of Intelligent Biomedicine, Hangzhou Dianzi University,Hangzhou 310018, Zhejiang, People's Republic of China
| | - Fuqing Lü
- Institute of Intelligent Biomedicine, Hangzhou Dianzi University,Hangzhou 310018, Zhejiang, People's Republic of China
| | - Sangma Xie
- Institute of Intelligent Biomedicine, Hangzhou Dianzi University,Hangzhou 310018, Zhejiang, People's Republic of China
| | - Zhou Yu
- Institute of Intelligent Biomedicine, Hangzhou Dianzi University,Hangzhou 310018, Zhejiang, People's Republic of China
| | - Yuanlin Chen
- Institute of Intelligent Biomedicine, Hangzhou Dianzi University,Hangzhou 310018, Zhejiang, People's Republic of China
| | - Zhong Lü
- Affiliated Dongyang Hospital of Wenzhou Medical University,People's Republic of China
| | - Lihua Li
- Institute of Intelligent Biomedicine, Hangzhou Dianzi University,Hangzhou 310018, Zhejiang, People's Republic of China
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Xu Q, Zhou LL, Xing C, Xu X, Feng Y, Lv H, Zhao F, Chen YC, Cai Y. Tinnitus classification based on resting-state functional connectivity using a convolutional neural network architecture. Neuroimage 2024; 290:120566. [PMID: 38467345 DOI: 10.1016/j.neuroimage.2024.120566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 03/02/2024] [Accepted: 03/04/2024] [Indexed: 03/13/2024] Open
Abstract
OBJECTIVES Many studies have investigated aberrant functional connectivity (FC) using resting-state functional MRI (rs-fMRI) in subjective tinnitus patients. However, no studies have verified the efficacy of resting-state FC as a diagnostic imaging marker. We established a convolutional neural network (CNN) model based on rs-fMRI FC to distinguish tinnitus patients from healthy controls, providing guidance and fast diagnostic tools for the clinical diagnosis of subjective tinnitus. METHODS A CNN architecture was trained on rs-fMRI data from 100 tinnitus patients and 100 healthy controls using an asymmetric convolutional layer. Additionally, a traditional machine learning model and a transfer learning model were included for comparison with the CNN, and each of the three models was tested on three different brain atlases. RESULTS Of the three models, the CNN model outperformed the other two models with the highest area under the curve, especially on the Dos_160 atlas (AUC = 0.944). Meanwhile, the model with the best classification performance highlights the crucial role of the default mode network, salience network, and sensorimotor network in distinguishing between normal controls and patients with subjective tinnitus. CONCLUSION Our CNN model could appropriately tackle the diagnosis of tinnitus patients using rs-fMRI and confirmed the diagnostic value of FC as measured by rs-fMRI.
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Affiliation(s)
- Qianhui Xu
- Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107 West Yanjiang Road, Guangzhou, Guangdong Province 510120, China
| | - Lei-Lei Zhou
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing 210006, China
| | - Chunhua Xing
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing 210006, China
| | - Xiaomin Xu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing 210006, China
| | - Yuan Feng
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing 210006, China
| | - Han Lv
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Fei Zhao
- Department of Speech and Language Therapy and Hearing Science, Cardiff Metropolitan University, Cardiff, UK
| | - Yu-Chen Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing 210006, China.
| | - Yuexin Cai
- Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107 West Yanjiang Road, Guangzhou, Guangdong Province 510120, China.
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Nair SS, Devi VM, Bhasi S. Enhanced lung cancer detection: Integrating improved random walker segmentation with artificial neural network and random forest classifier. Heliyon 2024; 10:e29032. [PMID: 38617949 PMCID: PMC11015404 DOI: 10.1016/j.heliyon.2024.e29032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 03/22/2024] [Accepted: 03/28/2024] [Indexed: 04/16/2024] Open
Abstract
Background Medical image segmentation is a vital yet difficult job because of the multimodality of the acquired images. It is difficult to locate the polluted area before it spreads. Methods This research makes use of several machine learning tools, including an artificial neural network as well as a random forest classifier, to increase the system's reliability of pulmonary nodule classification. Anisotropic diffusion filtering is initially used to remove noise from a picture. After that, a modified random walk method is used to get the region of interest inside the lung parenchyma. Finally, the features corresponding to the consistency of the picture segments are extracted using texture-based feature extraction for pulmonary nodules. The final stage is to identify and classify the pulmonary nodules using a classifier algorithm. Results The studies employ cross-validation to demonstrate the validity of the diagnosis framework. In this instance, the proposed method is tested using CT scan information provided by the Lung Image Database Consortium. A random forest classifier showed 99.6 percent accuracy rate for detecting lung cancer, compared to a artificial neural network's 94.8 percent accuracy rate. Conclusions Due to this, current research is now primarily concerned with identifying lung nodules and classifying them as benign or malignant. The diagnostic potential of machine learning as well as image processing approaches are enormous for the categorization of lung cancer.
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Affiliation(s)
- Sneha S. Nair
- Department of Physics, Noorul Islam Centre for Higher Education, Kumarakovil, Kanyakumari District, Tamil Nadu, India
| | - V.N. Meena Devi
- Department of Physics, Noorul Islam Centre for Higher Education, Kumarakovil, Kanyakumari District, Tamil Nadu, India
| | - Saju Bhasi
- Department of Radiation Physics, Regional Cancer Centre, Thiruvananthapuram, Kerala, India
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Yang J, Ren P, Xin P, Wang Y, Ma Y, Liu W, Wang Y, Wang Y, Zhang G. Automatic measurement of lower limb alignment in portable devices based on deep learning for knee osteoarthritis. J Orthop Surg Res 2024; 19:232. [PMID: 38594698 PMCID: PMC11005281 DOI: 10.1186/s13018-024-04658-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 03/02/2024] [Indexed: 04/11/2024] Open
Abstract
BACKGROUND For knee osteoarthritis patients, analyzing alignment of lower limbs is essential for therapy, which is currently measured from standing long-leg radiographs of anteroposterior X-ray (LLR) manually. To address the time wasting, poor reproducibility and inconvenience of use caused by existing methods, we present an automated measurement model in portable devices for assessing knee alignment from LLRs. METHOD We created a model and trained it with 837 conforming LLRs, and tested it using 204 LLRs without duplicates in a portable device. Both manual and model measurements were conducted independently, then we recorded knee alignment parameters such as Hip knee ankle angle (HKA), Joint line convergence angle (JCLA), Anatomical mechanical angle (AMA), mechanical Lateral distal femoral angle (mLDFA), mechanical Medial proximal tibial angle (mMPTA), and the time required. We evaluated the model's performance compared with manual results in various metrics. RESULT In both the validation and test sets, the average mean radial errors were 2.778 and 2.447 (P<0.05). The test results for native knee joints showed that 92.22%, 79.38%, 87.94%, 79.82%, and 80.16% of the joints reached angle deviation<1° for HKA, JCLA, AMA, mLDFA, and mMPTA. Additionally, for joints with prostheses, 90.14%, 93.66%, 86.62%, 83.80%, and 85.92% of the joints reached that. The Chi-square test did not reveal any significant differences between the manual and model measurements in subgroups (P>0.05). Furthermore, the Bland-Altman 95% limits of agreement were less than ± 2° for HKA, JCLA, AMA, and mLDFA, and slightly more than ± 2 degrees for mMPTA. CONCLUSION The automatic measurement tool can assess the alignment of lower limbs in portable devices for knee osteoarthritis patients. The results are reliable, reproducible, and time-saving.
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Affiliation(s)
- Jianfeng Yang
- Department of Orthopedics, the First Medical Center of Chinese PLA General Hospital, Beijing, China
- Senior Department of Orthopedics, the Fourth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Peng Ren
- Senior Department of Orthopedics, the Fourth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Peng Xin
- Department of Orthopedics, Chinese PLA Southern Theater Command General Hospital, Guangzhou, China
| | - Yiming Wang
- Department of Orthopedics, the First Medical Center of Chinese PLA General Hospital, Beijing, China
- Senior Department of Orthopedics, the Fourth Medical Center of Chinese PLA General Hospital, Beijing, China
- Medical School of Chinese People's Liberation Army, Beijing, China
| | - Yonglei Ma
- Department of Anesthesiology, Guangzhou First People's Hospital, Guangzhou, China
| | - Wei Liu
- Damo Academy, Alibaba Group, Hangzhou, China
| | - Yu Wang
- Damo Academy, Alibaba Group, Hangzhou, China
| | - Yan Wang
- Department of Orthopedics, the First Medical Center of Chinese PLA General Hospital, Beijing, China.
- Senior Department of Orthopedics, the Fourth Medical Center of Chinese PLA General Hospital, Beijing, China.
- Department of Orthopedics, the First Medical Center, PLA General Hospital, Fuxing Road, Haidian District, Beijing, China.
| | - Guoqiang Zhang
- Department of Orthopedics, the First Medical Center of Chinese PLA General Hospital, Beijing, China.
- Senior Department of Orthopedics, the Fourth Medical Center of Chinese PLA General Hospital, Beijing, China.
- Department of Orthopedics, the First Medical Center, PLA General Hospital, Fuxing Road, Haidian District, Beijing, China.
- Department of Orthopedic Surgery, The First Medical Center, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, People's Republic of China.
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Yenikaya MA, Kerse G, Oktaysoy O. Artificial intelligence in the healthcare sector: comparison of deep learning networks using chest X-ray images. Front Public Health 2024; 12:1386110. [PMID: 38660365 PMCID: PMC11039909 DOI: 10.3389/fpubh.2024.1386110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 04/02/2024] [Indexed: 04/26/2024] Open
Abstract
Purpose Artificial intelligence has led to significant developments in the healthcare sector, as in other sectors and fields. In light of its significance, the present study delves into exploring deep learning, a branch of artificial intelligence. Methods In the study, deep learning networks ResNet101, AlexNet, GoogLeNet, and Xception were considered, and it was aimed to determine the success of these networks in disease diagnosis. For this purpose, a dataset of 1,680 chest X-ray images was utilized, consisting of cases of COVID-19, viral pneumonia, and individuals without these diseases. These images were obtained by employing a rotation method to generate replicated data, wherein a split of 70 and 30% was adopted for training and validation, respectively. Results The analysis findings revealed that the deep learning networks were successful in classifying COVID-19, Viral Pneumonia, and Normal (disease-free) images. Moreover, an examination of the success levels revealed that the ResNet101 deep learning network was more successful than the others with a 96.32% success rate. Conclusion In the study, it was seen that deep learning can be used in disease diagnosis and can help experts in the relevant field, ultimately contributing to healthcare organizations and the practices of country managers.
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Affiliation(s)
| | - Gökhan Kerse
- Faculty of Economics and Administrative Sciences, Department of Management Information Systems, Kafkas University, Kars, Türkiye
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Miyama K, Akiyama T, Bise R, Nakamura S, Nakashima Y, Uchida S. Development of an automatic surgical planning system for high tibial osteotomy using artificial intelligence. Knee 2024; 48:128-137. [PMID: 38599029 DOI: 10.1016/j.knee.2024.03.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 03/06/2024] [Accepted: 03/19/2024] [Indexed: 04/12/2024]
Abstract
BACKGROUND This study proposed an automatic surgical planning system for high tibial osteotomy (HTO) using deep learning-based artificial intelligence and validated its accuracy. The system simulates osteotomy and measures lower-limb alignment parameters in pre- and post-osteotomy simulations. METHODS A total of 107 whole-leg standing radiographs were obtained from 107 patients who underwent HTO. First, the system detected anatomical landmarks on radiographs. Then, it simulated osteotomy and automatically measured five parameters in pre- and post-osteotomy simulation (hip knee angle [HKA], weight-bearing line ratio [WBL ratio], mechanical lateral distal femoral angle [mLDFA], mechanical medial proximal tibial angle [mMPTA], and mechanical lateral distal tibial angle [mLDTA]). The accuracy of the measured parameters was validated by comparing them with the ground truth (GT) values given by two orthopaedic surgeons. RESULTS All absolute errors of the system were within 1.5° or 1.5%. All inter-rater correlation confidence (ICC) values between the system and GT showed good reliability (>0.80). Excellent reliability was observed in the HKA (0.99) and WBL ratios (>0.99) for the pre-osteotomy simulation. The intra-rater difference of the system exhibited excellent reliability with an ICC value of 1.00 for all lower-limb alignment parameters in pre- and post-osteotomy simulations. In addition, the measurement time per radiograph (0.24 s) was considerably shorter than that of an orthopaedic surgeon (118 s). CONCLUSION The proposed system is practically applicable because it can measure lower-limb alignment parameters accurately and quickly in pre- and post-osteotomy simulations. The system has potential applications in surgical planning systems.
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Affiliation(s)
- Kazuki Miyama
- Department of Orthopaedic Surgery, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka 812-8582, Japan; Department of Advanced Information Technology, Kyushu University, 744 Motooka, Nishi-Ku, Fukuoka 819-0395, Japan; Akiyama Clinic, 2-28-39, Noke, Sawaraku, Fukuoka City, Fukuoka 814-0171, Japan.
| | - Takenori Akiyama
- Department of Orthopaedic Surgery, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka 812-8582, Japan; Akiyama Clinic, 2-28-39, Noke, Sawaraku, Fukuoka City, Fukuoka 814-0171, Japan
| | - Ryoma Bise
- Department of Advanced Information Technology, Kyushu University, 744 Motooka, Nishi-Ku, Fukuoka 819-0395, Japan
| | - Shunsuke Nakamura
- Akiyama Clinic, 2-28-39, Noke, Sawaraku, Fukuoka City, Fukuoka 814-0171, Japan
| | - Yasuharu Nakashima
- Department of Orthopaedic Surgery, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka 812-8582, Japan
| | - Seiichi Uchida
- Department of Advanced Information Technology, Kyushu University, 744 Motooka, Nishi-Ku, Fukuoka 819-0395, Japan
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Nair SSK, David LR, Shariff A, Maskari SA, Mawali AA, Weis S, Fouad T, Ozsahin DU, Alshuweihi A, Obaideen A, Elshami W. CovMediScanX: A medical imaging solution for COVID-19 diagnosis from chest X-ray images. J Med Imaging Radiat Sci 2024:S1939-8654(24)00100-0. [PMID: 38594085 DOI: 10.1016/j.jmir.2024.03.046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 02/17/2024] [Accepted: 03/19/2024] [Indexed: 04/11/2024]
Abstract
INTRODUCTION Radiologists have extensively employed the interpretation of chest X-rays (CXR) to identify visual markers indicative of COVID-19 infection, offering an alternative approach for the screening of infected individuals. This research article presents CovMediScanX, a deep learning-based framework designed for a rapid and automated diagnosis of COVID-19 from CXR scan images. METHODS The proposed approach encompasses gathering and preprocessing CXR image datasets, training deep learning-based custom-made Convolutional Neural Network (CNN), pre-trained and hybrid transfer learning models, identifying the highest-performing model based on key evaluation metrics, and embedding this model into a web interface called CovMediScanX, designed for radiologists to detect the COVID-19 status in new CXR images. RESULTS The custom-made CNN model obtained a remarkable testing accuracy of 94.32% outperforming other models. CovMediScanX, employing the custom-made CNN underwent evaluation with an independent dataset also. The images in the independent dataset are sourced from a scanning machine that is entirely different from those used for the training dataset, highlighting a clear distinction of datasets in their origins. The evaluation outcome highlighted the framework's capability to accurately detect COVID-19 cases, showcasing encouraging results with a precision of 73% and a recall of 84% for positive cases. However, the model requires further enhancement, particularly in improving its detection of normal cases, as evidenced by lower precision and recall rates. CONCLUSION The research proposes CovMediScanX framework that demonstrates promising potential in automatically identifying COVID-19 cases from CXR images. While the model's overall performance on independent data needs improvement, it is evident that addressing bias through the inclusion of diverse data sources during training could further enhance accuracy and reliability.
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Affiliation(s)
| | - Leena R David
- Department of Medical Diagnostic Imaging, College of Health Sciences, University of Sharjah, United Arab Emirates
| | - Abdulwahid Shariff
- Department of Postgraduate Studies, University of Dar es Salaam, Tanzania
| | - Saqar Al Maskari
- Department of Computing and Electronics Engineering, Middle East College, Sultanate of Oman
| | - Adhra Al Mawali
- Quality Assurance and Planning, German University of Technology (GUtech), Sultanate of Oman
| | - Sammy Weis
- University Hospital, Sharjah, United Arab Emirates
| | - Taha Fouad
- University Hospital, Sharjah, United Arab Emirates
| | - Dilber Uzun Ozsahin
- Department of Medical Diagnostic Imaging, College of Health Sciences, University of Sharjah, United Arab Emirates
| | | | | | - Wiam Elshami
- Department of Medical Diagnostic Imaging, College of Health Sciences, University of Sharjah, United Arab Emirates
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Mira JJ, Matarredona V, Tella S, Sousa P, Ribeiro Neves V, Strametz R, López-Pineda A. Unveiling the hidden struggle of healthcare students as second victims through a systematic review. BMC MEDICAL EDUCATION 2024; 24:378. [PMID: 38589877 PMCID: PMC11000311 DOI: 10.1186/s12909-024-05336-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 03/21/2024] [Indexed: 04/10/2024]
Abstract
BACKGROUND When healthcare students witness, engage in, or are involved in an adverse event, it often leads to a second victim experience, impacting their mental well-being and influencing their future professional practice. This study aimed to describe the efforts, methods, and outcomes of interventions to help students in healthcare disciplines cope with the emotional experience of being involved in or witnessing a mistake causing harm to a patient during their clerkships or training. METHODS This systematic review followed the PRISMA guidelines and includes the synthesis of eighteen studies, published in diverse languages from 2011 to 2023, identified from the databases MEDLINE, EMBASE, SCOPUS and APS PsycInfo. PICO method was used for constructing a research question and formulating eligibility criteria. The selection process was conducted through Rayyan. Titles and abstracts of were independently screened by two authors. The critical appraisal tools of the Joanna Briggs Institute was used to assess the risk of bias of the included studies. RESULTS A total of 1354 studies were retrieved, 18 met the eligibility criteria. Most studies were conducted in the USA. Various educational interventions along with learning how to prevent mistakes, and resilience training were described. In some cases, this experience contributed to the student personal growth. Psychological support in the aftermath of adverse events was scattered. CONCLUSION Ensuring healthcare students' resilience should be a fundamental part of their training. Interventions to train them to address the second victim phenomenon during their clerkships are scarce, scattered, and do not yield conclusive results on identifying what is most effective and what is not.
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Affiliation(s)
- José Joaquín Mira
- Atenea Research. FISABIO, Alicante, Spain.
- Universidad Miguel Hernández, Elche, Spain.
| | | | - Susanna Tella
- Faculty of Health and Social Care, LAB University of Applied Sciences, Lappeenranta, Finland
| | - Paulo Sousa
- NOVA National School of Public Health, Public Health Research Centre, Comprehensive Health Research Center, CHRC, NOVA University Lisbon, Lisbon, Portugal
| | | | - Reinhard Strametz
- Wiesbaden Institute for Healthcare Economics and Patient Safety (WiHelP), RheinMain UAS, Wiesbaden, Germany
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Li Y, Zhang Y, Tian L, Li J, Li H, Wang X, Wang C. 3D amide proton transfer-weighted imaging may be useful for diagnosing early-stage breast cancer: a prospective monocentric study. Eur Radiol Exp 2024; 8:41. [PMID: 38584248 PMCID: PMC10999404 DOI: 10.1186/s41747-024-00439-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 01/17/2024] [Indexed: 04/09/2024] Open
Abstract
BACKGROUND We investigated the value of three-dimensional amide proton transfer-weighted imaging (3D-APTWI) in the diagnosis of early-stage breast cancer (BC) and its correlation with the immunohistochemical characteristics of malignant lesions. METHODS Seventy-eight women underwent APTWI and dynamic contrast-enhanced (DCE)-MRI. Pathological results were categorized as either benign (n = 43) or malignant (n = 37) lesions. The parameters of APTWI and DCE-MRI were compared between the benign and malignant groups. The diagnostic value of 3D-APTWI was evaluated using the area under the receiver operating characteristic curve (ROC-AUC) to establish a diagnostic threshold. Pearson's correlation was used to analyze the correlation between the magnetization transfer asymmetry (MTRasym) and immunohistochemical characteristics. RESULTS The MTRasym and time-to-peak of malignancies were significantly lower than those of benign lesions (all p < 0.010). The volume transfer constant, rate constant, and wash-in and wash-out rates of malignancies were all significantly greater than those of benign lesions (all p < 0.010). ROC-AUCs of 3D-APTWI, DCE-MRI, and 3D-APTWI+DCE to differential diagnosis between early-stage BC and benign lesions were 0.816, 0.745, and 0.858, respectively. Only the difference between AUCAPT+DCE and AUCDCE was significant (p < 0.010). When a threshold of MTRasym for malignancy for 2.42%, the sensitivity and specificity of 3D-APTWI for BC diagnosis were 86.5% and 67.6%, respectively; MTRasym was modestly positively correlated with pathological grade (r = 0.476, p = 0.003) and Ki-67 (r = 0.419, p = 0.020). CONCLUSIONS 3D-APTWI may be used as a supplementary method for patients with contraindications of DCE-MRI. MTRasym can imply the proliferation activities of early-stage BC. RELEVANCE STATEMENT 3D-APTWI can be an alternative diagnostic method for patients with early-stage BC who are not suitable for contrast injection. KEY POINTS • 3D-APTWI reflects the changes in the microenvironment of early-stage breast cancer. • Combined 3D-APTWI is superior to DCE-MRI alone for early-stage breast cancer diagnosis. • 3D-APTWI improves the diagnostic accuracy of early-stage breast cancer.
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Affiliation(s)
- Yeqin Li
- Department of Radiology, Shandong Province Hospital of Traditional Chinese Medicine, Jinan, 250014, China
| | - Yan Zhang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medcial University, Jinan, 250021, China
| | - Liwen Tian
- Department of Radiology, Shandong Public Health Clinical Center, Jinan, 250100, China
| | - Ju Li
- Department of Radiology, Shandong Public Health Clinical Center, Jinan, 250100, China
- Binzhou Medical University, Yantai, 264003, China
| | - Huihua Li
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medcial University, Jinan, 250021, China
| | - Ximing Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medcial University, Jinan, 250021, China
| | - Cuiyan Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medcial University, Jinan, 250021, China.
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Ajjan RA, Battelino T, Cos X, Del Prato S, Philips JC, Meyer L, Seufert J, Seidu S. Continuous glucose monitoring for the routine care of type 2 diabetes mellitus. Nat Rev Endocrinol 2024:10.1038/s41574-024-00973-1. [PMID: 38589493 DOI: 10.1038/s41574-024-00973-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/29/2024] [Indexed: 04/10/2024]
Abstract
Although continuous glucose monitoring (CGM) devices are now considered the standard of care for people with type 1 diabetes mellitus, the uptake among people with type 2 diabetes mellitus (T2DM) has been slower and is focused on those receiving intensive insulin therapy. However, increasing evidence now supports the inclusion of CGM in the routine care of people with T2DM who are on basal insulin-only regimens or are managed with other medications. Expanding CGM to these groups could minimize hypoglycaemia while allowing efficient adaptation and escalation of therapies. Increasing evidence from randomized controlled trials and observational studies indicates that CGM is of clinical value in people with T2DM on non-intensive treatment regimens. If further studies confirm this finding, CGM could soon become a part of routine care for T2DM. In this Perspective we explore the potential benefits of widening the application of CGM in T2DM, along with the challenges that must be overcome for the evidence-based benefits of this technology to be delivered for all people with T2DM.
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Affiliation(s)
- Ramzi A Ajjan
- The LIGHT Laboratories, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
| | - Tadej Battelino
- Faculty of Medicine, University of Ljubljana Medical Centre, Ljubljana, Slovenia
| | - Xavier Cos
- DAP Cat Research Group, Foundation University Institute for Primary Health Care Research Jordi Gol i Gorina, Barcelona, Spain
| | - Stefano Del Prato
- Section of Diabetes and Metabolic Diseases, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | | | - Laurent Meyer
- Department of Endocrinology, Diabetes and Nutrition, University Hospital, Strasbourg, France
| | - Jochen Seufert
- Division of Endocrinology and Diabetology, Department of Medicine II, Medical Centre, University of Freiburg, Freiburg, Germany
| | - Samuel Seidu
- Leicester Real World Evidence Unit, Diabetes Research Centre, University of Leicester, Leicester, UK.
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Xu K, Zhang F, Huang Y, Huang X. 2.5D UNet with context-aware feature sequence fusion for accurate esophageal tumor semantic segmentation. Phys Med Biol 2024; 69:085002. [PMID: 38484399 DOI: 10.1088/1361-6560/ad3419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 03/14/2024] [Indexed: 04/04/2024]
Abstract
Segmenting esophageal tumor from computed tomography (CT) sequence images can assist doctors in diagnosing and treating patients with this malignancy. However, accurately extracting esophageal tumor features from CT images often present challenges due to their small area, variable position, and shape, as well as the low contrast with surrounding tissues. This results in not achieving the level of accuracy required for practical applications in current methods. To address this problem, we propose a 2.5D context-aware feature sequence fusion UNet (2.5D CFSF-UNet) model for esophageal tumor segmentation in CT sequence images. Specifically, we embed intra-slice multiscale attention feature fusion (Intra-slice MAFF) in each skip connection of UNet to improve feature learning capabilities, better expressing the differences between anatomical structures within CT sequence images. Additionally, the inter-slice context fusion block (Inter-slice CFB) is utilized in the center bridge of UNet to enhance the depiction of context features between CT slices, thereby preventing the loss of structural information between slices. Experiments are conducted on a dataset of 430 esophageal tumor patients. The results show an 87.13% dice similarity coefficient, a 79.71% intersection over union and a 2.4758 mm Hausdorff distance, which demonstrates that our approach can improve contouring consistency and can be applied to clinical applications.
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Affiliation(s)
- Kai Xu
- Scholl of the Internet, Anhui university, Anhui, 230039, People's Republic of China
| | - Feixiang Zhang
- Scholl of the Internet, Anhui university, Anhui, 230039, People's Republic of China
| | - Yong Huang
- Department of Medical Oncology, The Second People's Hospital of Hefei, Hefei, 230011, People's Republic of China
| | - Xiaoyu Huang
- Department of Chinese Integrative Medicine Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, People's Republic of China
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Matilainen N, Kataja J, Laakso I. Verification of neuronavigated TMS accuracy using structured-light 3D scans. Phys Med Biol 2024; 69:085004. [PMID: 38479018 DOI: 10.1088/1361-6560/ad33b8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 03/13/2024] [Indexed: 04/04/2024]
Abstract
Objective.To investigate the reliability and accuracy of the manual three-point co-registration in neuronavigated transcranial magnetic stimulation (TMS). The effect of the error in landmark pointing on the coil placement and on the induced electric and magnetic fields was examined.Approach.The position of the TMS coil on the head was recorded by the neuronavigation system and by 3D scanning for ten healthy participants. The differences in the coil locations and orientations and the theoretical error values for electric and magnetic fields between the neuronavigated and 3D scanned coil positions were calculated. In addition, the sensitivity of the coil location on landmark accuracy was calculated.Main results.The measured distances between the neuronavigated and 3D scanned coil locations were on average 10.2 mm, ranging from 3.1 to 18.7 mm. The error in angles were on average from two to three degrees. The coil misplacement caused on average a 29% relative error in the electric field with a range from 9% to 51%. In the magnetic field, the same error was on average 33%, ranging from 10% to 58%. The misplacement of landmark points could cause a 1.8-fold error for the coil location.Significance.TMS neuronavigation with three landmark points can cause a significant error in the coil position, hampering research using highly accurate electric field calculations. Including 3D scanning to the process provides an efficient method to achieve a more accurate coil position.
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Affiliation(s)
- Noora Matilainen
- Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland
| | - Juhani Kataja
- Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland
| | - Ilkka Laakso
- Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland
- Aalto Neuroimaging, Aalto University, Espoo, Finland
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