The total replacement of wrists affected by rheumatoid arthritis (RA) has had mixed outcomes in terms of failure rates. This study was therefore conducted to analyse the biomechanics of wrist arthroplasty using recently reported implants that have shown encouraging results with the aim of providing some insights for the future development of wrist implants. A model of a healthy wrist was developed using computed tomography images from a healthy volunteer. An RA model was simulated based on all ten general characteristics of the disease. The ReMotion ™ total wrist system was then modelled to simulate total wrist arthroplasty (TWA). Finite element analysis was performed with loads simulating the static hand grip action. The results show that the RA model produced distorted patterns of stress distribution with tenfold higher contact pressure than the healthy model. For the TWA model, contact pressure was found to be approximately fivefold lower than the RA model. Compared to the healthy model, significant improvements were observed for the TWA model with minor variations in the stress distribution. In conclusion, the modelled TWA reduced contact pressure between bones but did not restore the stress distribution to the normal healthy condition.
The relationship between microarchitecture to the failure mechanism and mechanical properties can be assessed through experimental and computational methods. In this study, both methods were utilised using bovine cadavers. Twenty four samples of cancellous bone were extracted from fresh bovine and the samples were cleaned from excessive marrow. Uniaxial compression testing was performed with displacement control. After mechanical testing, each specimen was ashed in a furnace. Four of the samples were exemplarily scanned using micro-computed tomography (μCT) and three dimensional models of the cancellous bones were reconstructed for finite element simulation. The mechanical properties and the failure modes obtained from numerical simulations were then compared to the experiments. Correlations between microarchitectural parameters to the mechanical properties and failure modes were then made. The Young's modulus correlates well with the bone volume fraction with R² = 0.615 and P value 0.013. Three different types of failure modes of cancellous bone were observed: oblique fracture (21.7%), perpendicular global fracture (47.8%), and scattered localised fracture (30.4%). However, no correlations were found between the failure modes to the morphological parameters. The percentage of error between computer predictions and the actual experimental test was from 6 to 12%. These mechanical properties and information on failure modes can be used for the development of synthetic cancellous bone.
Diabetic retinopathy (DR) is a sight threatening complication due to diabetes mellitus that affects the retina. In this article, a computerised DR grading system, which digitally analyses retinal fundus image, is used to measure foveal avascular zone. A v-fold cross-validation method is applied to the FINDeRS database to evaluate the performance of the DR system. It is shown that the system achieved sensitivity of >84%, specificity of >97% and accuracy of >95% for all DR stages. At high values of sensitivity (>95%), specificity (>97%) and accuracy (>98%) obtained for No DR and severe NPDR/PDR stages, the computerised DR grading system is suitable for early detection of DR and for effective treatment of severe cases.
This paper presents a new approach to diagnose and classify early risk in dengue patients using bioelectrical impedance analysis (BIA) and artificial neural network (ANN). A total of 223 healthy subjects and 207 hospitalized dengue patients were prospectively studied. The dengue risk severity criteria was determined and grouped based on three blood investigations, namely, platelet (PLT) count (less than or equal to 30,000 cells per mm(3)), hematocrit (HCT) (increase by more than or equal to 20%), and either aspartate aminotransferase (AST) level (raised by fivefold the normal upper limit) or alanine aminotransferase (ALT) level (raised by fivefold the normal upper limit). The dengue patients were classified according to their risk groups and the corresponding BIA parameters were subsequently obtained and quantified. Four parameters were used for training and testing the ANN which are day of fever, reactance, gender, and risk group's quantification. Day of fever was defined as the day of fever subsided, i.e., when the body temperature fell below 37.5°C. The blood investigation and the BIA data were taken for 5 days. The ANN was trained via the steepest descent back propagation with momentum algorithm using the log-sigmoid transfer function while the sum-squared error was used as the network's performance indicator. The best ANN architecture of 3-6-1 (3 inputs, 6 neurons in the hidden layer, and 1 output), learning rate of 0.1, momentum constant of 0.2, and iteration rate of 20,000 was pruned using a weight-eliminating method. Eliminating a weight of 0.05 enhances the dengue's prediction risk classification accuracy of 95.88% for high risk and 96.83% for low risk groups. As a result, the system is able to classify and diagnose the risk in the dengue patients with an overall prediction accuracy of 96.27%.
Even though the World Health Organization criteria's for classifying the dengue infection have been used for long time, recent studies declare that several difficulties have been faced by the clinicians to apply these criteria. Accordingly, many studies have proposed modified criteria to identify the risk in dengue patients based on statistical analysis techniques. None of these studies utilized the powerfulness of the self-organized map (SOM) in visualizing, understanding, and exploring the complexity in multivariable data. Therefore, this study utilized the clustering of the SOM technique to identify the risk criteria in 195 dengue patients. The new risk criteria were defined as: platelet count less than or equal 40,000 cells per mm(3), hematocrit concentration great than or equal 25% and aspartate aminotransferase (AST) rose by fivefold the normal upper limit for AST/alanine aminotransfansferase (ALT) rose by fivefold the normal upper limit for ALT. The clusters analysis indicated that any dengue patient fulfills any two of the risk criteria is consider as high risk dengue patient.
Human bones can be categorised into one of two types--the compact cortical and the porous cancellous. Whilst the cortical is a solid structure macroscopically, the structure of cancellous bone is highly complex with plate-like and strut-like structures of various sizes and shapes depending on the anatomical site. Reconstructing the actual structure of cancellous bone for defect filling is highly unfeasible. However, the complex structure can be simplified into an idealised structure with similar properties. In this study, two idealised architectures were developed based on morphological indices of cancellous bone: the tetrakaidecahedral and the prismatic. The two architectures were further subdivided into two types of microstructure, the first consists of struts only and the second consists of a combination of plates and struts. The microstructures were transformed into finite element models and displacement boundary condition was applied to all four idealised cancellous models with periodic boundary conditions. Eight unit cells extracted from the actual cancellous bone obtained from micro-computed tomography were also analysed with the same boundary conditions. Young's modulus values were calculated and comparison was made between the idealised and real cancellous structures. Results showed that all models with a combination of plates and struts have higher rigidity compared to the one with struts only. Values of Young's modulus from eight unit cells of cancellous bone varied from 42 to 479 MPa with an average of 234 MPa. The prismatic architecture with plates and rods closely resemble the average stiffness of a unit cell of cancellous bone.
This paper proposes a detection scheme for identifying stones in the biliary tract of the body, which is examined using magnetic resonance cholangiopancreatography (MRCP), a sequence of magnetic resonance imaging targeted at the pancreatobiliary region of the abdomen. The scheme enhances the raw 2D thick slab MRCP images and extracts the biliary structure in the images using a segment-based region-growing approach. Detection of stones is scoped within this extracted structure, by highlighting possible stones. A trained feedforward artificial neural network uses selected features of size and average segment intensity as its input to detect possible stones in MRCP images and eliminate false stone-like objects. The proposed scheme achieved satisfactory results in tests of clinical MRCP thick slab images, indicating potential for implementation in computer-aided diagnosis systems for the liver.
This paper proposes a novel hybrid magnetoacoustic measurement (HMM) system aiming at breast cancer detection. HMM combines ultrasound and magnetism for the simultaneous assessment of bioelectric and acoustic profiles of breast tissue. HMM is demonstrated on breast tissue samples, which are exposed to 9.8 MHz ultrasound wave with the presence of a 0.25 Tesla static magnetic field. The interaction between the ultrasound wave and the magnetic field in the breast tissue results in Lorentz Force that produces a magnetoacoustic voltage output, proportional to breast tissue conductivity. Simultaneously, the ultrasound wave is sensed back by the ultrasound receiver for tissue acoustic evaluation. Experiments are performed on gel phantoms and real breast tissue samples harvested from laboratory mice. Ultrasound wave characterization results show that normal breast tissue experiences higher attenuation compared with cancerous tissue. The mean magnetoacoustic voltage results for normal tissue are lower than that for the cancerous tissue group. In conclusion, the combination of acoustic and bioelectric measurements is a promising approach for breast cancer diagnosis.
This paper presents a theoretical development and critical analysis of the burst frequency equations for capillary valves on a microfluidic compact disc (CD) platform. This analysis includes background on passive capillary valves and the governing models/equations that have been developed to date. The implicit assumptions and limitations of these models are discussed. The fluid meniscus dynamics before bursting is broken up into a multi-stage model and a more accurate version of the burst frequency equation for the capillary valves is proposed. The modified equations are used to evaluate the effects of various CD design parameters such as the hydraulic diameter, the height to width aspect ratio, and the opening wedge angle of the channel on the burst pressure.
Breast cancer is the most common cancer among women globally, and the number of young women diagnosed with this disease is gradually increasing over the years. Mammography is the current gold-standard technique although it is known to be less sensitive in detecting tumors in woman with dense breast tissue. Detecting an early-stage tumor in young women is very crucial for better survival chance and treatment. The thermography technique has the capability to provide an additional functional information on physiological changes to mammography by describing thermal and vascular properties of the tissues. Studies on breast thermography have been carried out to improve the accuracy level of the thermography technique in various perspectives. However, the limitation of gathering women affected by cancer in different age groups had necessitated this comprehensive study which is aimed to investigate the effect of different density levels on the surface temperature distribution profile of the breast models. These models, namely extremely dense (ED), heterogeneously dense (HD), scattered fibroglandular (SF), and predominantly fatty (PF), with embedded tumors were developed using the finite element method. A conventional Pennes' bioheat model was used to perform the numerical simulation on different case studies, and the results obtained were then compared using a hypothesis statistical analysis method to the reference breast model developed previously. The results obtained show that ED, SF, and PF breast models had significant mean differences in surface temperature profile with a p value <0.025, while HD breast model data pair agreed with the null hypothesis formulated due to the comparable tissue composition percentage to the reference model. The findings suggested that various breast density levels should be considered as a contributing factor to the surface thermal distribution profile alteration in both breast cancer detection and analysis when using the thermography technique.
Recently, there is an increasing interest in the use of local hyperthermia treatment for a variety of clinical applications. The desired therapeutic outcome in local hyperthermia treatment is achieved by raising the local temperature to surpass the tissue coagulation threshold, resulting in tissue necrosis. In oncology, local hyperthermia is used as an effective way to destroy cancerous tissues and is said to have the potential to replace conventional treatment regime like surgery, chemotherapy or radiotherapy. However, the inability to closely monitor temperature elevations from hyperthermia treatment in real time with high accuracy continues to limit its clinical applicability. Local hyperthermia treatment requires real-time monitoring system to observe the progression of the destroyed tissue during and after the treatment. Ultrasound is one of the modalities that have great potential for local hyperthermia monitoring, as it is non-ionizing, convenient and has relatively simple signal processing requirement compared to magnetic resonance imaging and computed tomography. In a two-dimensional ultrasound imaging system, changes in tissue microstructure during local hyperthermia treatment are observed in terms of pixel value analysis extracted from the ultrasound image itself. Although 2D ultrasound has shown to be the most widely used system for monitoring hyperthermia in ultrasound imaging family, 1D ultrasound on the other hand could offer a real-time monitoring and the method enables quantitative measurement to be conducted faster and with simpler measurement instrument. Therefore, this paper proposes a new local hyperthermia monitoring method that is based on one-dimensional ultrasound. Specifically, the study investigates the effect of ultrasound attenuation in normal and pathological breast tissue when the temperature in tissue is varied between 37 and 65 °C during local hyperthermia treatment. Besides that, the total protein content measurement was also conducted to investigate the relationship between attenuation and tissue denaturation level at different temperature ranges. The tissues were grouped according to their histology results, namely normal tissue with large predominance of cells (NPC), cancer tissue with large predominance of cells (CPC) and cancer with high collagen fiber content (CHF). The result shows that the attenuation coefficient of ultrasound measured following the local hyperthermia treatment increases with the increment of collagen fiber content in tissue as the CHF attenuated ultrasound at the highest rate, followed by NPC and CPC. Additionally, the attenuation increment is more pronounced at the temperature over 55 °C. This describes that the ultrasound wave experienced more energy loss when it propagates through a heated tissue as the tissue structure changes due to protein coagulation effect. Additionally, a significant increase in the sensitivity of attenuation to protein denaturation is also observed with the highest sensitivity obtained in monitoring NPC. Overall, it is concluded that one-dimensional ultrasound can be used as a monitoring method of local hyperthermia since its attenuation is very sensitive to the changes in tissue microstructure during hyperthermia.
The present study was conducted to compare the stability of four commercially available implants by investigating the focal stress distributions and relative micromotion using finite element analysis. Variations in the numbers of pegs between the implant designs were tested. A load of 750 N was applied at three different glenoid positions (SA: superior-anterior; SP: superior-posterior; C: central) to mimic off-center and central loadings during activities of daily living. Focal stress distributions and relative micromotion were measured using Marc Mentat software. The results demonstrated that by increasing the number of pegs from two to five, the total focal stress volumes exceeding 5 MPa, reflecting the stress critical volume (SCV) as the threshold for occurrence of cement microfractures, decreased from 8.41 to 5.21 % in the SA position and from 9.59 to 6.69 % in the SP position. However, in the C position, this change in peg number increased the SCV from 1.37 to 5.86 %. Meanwhile, micromotion appeared to remain within 19-25 µm irrespective of the number of pegs used. In conclusion, four-peg glenoid implants provide the best configuration because they had lower SCV values compared with lesser-peg implants, preserved more bone stock, and reduced PMMA cement usage compared with five-peg implants.
Tuberculosis is a major global health problem that has been ranked as the second leading cause of death from an infectious disease worldwide, after the human immunodeficiency virus. Diagnosis based on cultured specimens is the reference standard; however, results take weeks to obtain. Slow and insensitive diagnostic methods hampered the global control of tuberculosis, and scientists are looking for early detection strategies, which remain the foundation of tuberculosis control. Consequently, there is a need to develop an expert system that helps medical professionals to accurately diagnose the disease. The objective of this study is to diagnose tuberculosis using a machine learning method. Artificial immune recognition system (AIRS) has been used successfully for diagnosing various diseases. However, little effort has been undertaken to improve its classification accuracy. In order to increase the classification accuracy, this study introduces a new hybrid system that incorporates real tournament selection mechanism into the AIRS. This mechanism is used to control the population size of the model and to overcome the existing selection pressure. Patient epacris reports obtained from the Pasteur laboratory in northern Iran were used as the benchmark data set. The sample consisted of 175 records, from which 114 (65 %) were positive for TB, and the remaining 61 (35 %) were negative. The classification performance was measured through tenfold cross-validation, root-mean-square error, sensitivity, and specificity. With an accuracy of 100 %, RMSE of 0, sensitivity of 100 %, and specificity of 100 %, the proposed method was able to successfully classify tuberculosis cases. In addition, the proposed method is comparable with top classifiers used in this research.
Diabetic macular edema (DME) is one of the most common causes of visual loss among diabetes mellitus patients. Early detection and successive treatment may improve the visual acuity. DME is mainly graded into non-clinically significant macular edema (NCSME) and clinically significant macular edema according to the location of hard exudates in the macula region. DME can be identified by manual examination of fundus images. It is laborious and resource intensive. Hence, in this work, automated grading of DME is proposed using higher-order spectra (HOS) of Radon transform projections of the fundus images. We have used third-order cumulants and bispectrum magnitude, in this work, as features, and compared their performance. They can capture subtle changes in the fundus image. Spectral regression discriminant analysis (SRDA) reduces feature dimension, and minimum redundancy maximum relevance method is used to rank the significant SRDA components. Ranked features are fed to various supervised classifiers, viz. Naive Bayes, AdaBoost and support vector machine, to discriminate No DME, NCSME and clinically significant macular edema classes. The performance of our system is evaluated using the publicly available MESSIDOR dataset (300 images) and also verified with a local dataset (300 images). Our results show that HOS cumulants and bispectrum magnitude obtained an average accuracy of 95.56 and 94.39% for MESSIDOR dataset and 95.93 and 93.33% for local dataset, respectively.
Evaluation of binding between analytes and its relevant ligands on surface plasmon resonance (SPR) biosensor is of considerable importance for accurate determination and screening of an interference in immunosensors. Dengue virus serotype 2 was used as a case study in this investigation. This research work compares and interprets the results obtained from analytical analysis with the experimental ones. Both the theoretical calculations and experimental results are verified with one sample from each category of dengue serotypes 2 (low, mid, and high positive), which have been examined in the database of established laboratorial diagnosis. In order to perform this investigation, the SPR angle variations are calculated, analyzed, and then validated via experimental SPR angle variations. Accordingly, the error ratios of 5.35, 6.54, and 3.72% were obtained for the low-, mid-, and high-positive-specific immune globulins of patient serums, respectively. In addition, the magnetic fields of the biosensor are numerically simulated to show the effect of different binding mediums.
Electrical cell-substrate impedance sensing (ECIS) is a powerful technique to monitor real-time cell behavior. In this study, an ECIS biosensor formed using two interdigitated electrode structures (IDEs) was used to monitor cell behavior and its response to toxicants. Three different sensors with varied electrode spacing were first modeled using COMSOL Multiphysics and then fabricated and tested. The silver/silver chloride IDEs were fabricated using a screen-printing technique and incorporated with polydimethylsiloxane (PDMS) cell culture wells. To study the effectiveness of the biosensor, A549 lung carcinoma cells were seeded in the culture wells together with collagen as an extracellular matrix (ECM) to promote cell attachment on electrodes. A549 cells were cultured in the chambers and impedance measurements were taken at 12-h intervals for 120 h. Cell index (CI) for both designs were calculated from the impedance measurement and plotted in comparison with the growth profile of the cells in T-flasks. To verify that the ECIS biosensor can also be used to study cell response to toxicants, the A549 cells were also treated with anti-cancer drug, paclitaxel, and its responses were monitored over 5 days. Both simulation and experimental results show better sensitivity for smaller spacing between electrodes. Graphical abstract The fabricated impedance biosensor used screen-printed silver/silver chloride IDEs. Simulation and experimental results show better sensitivity for smaller between electrodes.
Degenerative disc disease (DDD) is a common condition in elderly population that can be painful and can significantly affect individual's quality of life. Diagnosis of DDD allows prompt corrective actions but it is challenging due to the absence of any symptoms at early stages. In studying disc degeneration, measurement of the range of motion (RoM) and loads acting on the spine are crucial factors. However, direct measurement of RoM involves increased instrumentation and risk. In this paper, an innovative method is proposed for calculating RoM, emphasizing repeatability and reliability by considering the posterior thickness of the spine. This is achieved by offsetting the position of markers in relation to the actual vertebral loci. Three geometrically identical finite element models of L3-L4 are developed from a CT scan with different types of elements, and thereafter, mesh element-related metrics are provided for the assessment of the quality of models. The model with the best mesh quality is used for further analysis, where RoM are within ranges as reported in literature and in vivo experiment results. Various kinds of stresses acting on individual components including facet joints are analysed for normal and abnormal loading conditions. The results showed that the stresses in abnormal load conditions for all components including cortical (76.67 MPa), cancellous (69.18 MPa), annulus (6.30 MPa) and nucleus (0.343 MPa) are significantly greater as compared to normal loads (49.96 MPa, 44.2 MPa, 4.28 MPa and 0.23 MPa respectively). However, stress levels for both conditions are within safe limits (167-215 MPa for cortical, 46 MPa for the annulus and 3 MPa for facets). The results obtained could be used as a baseline motion and stresses of healthy subjects based on their respective lifestyles, which could benefit clinicians to suggest corrective actions for those affected by DDD.
Patients with spinal cord injury (SCI) benefit from muscle training with functional electrical stimulation (FES). For safety reasons and to optimize training outcome, the fatigue state of the target muscle must be monitored. Detection of muscle fatigue from mel frequency cepstral coefficient (MFCC) feature of mechanomyographic (MMG) signal using support vector machine (SVM) classifier is a promising new approach. Five individuals with SCI performed FES cycling exercises for 30 min. MMG signals were recorded on the quadriceps muscle group (rectus femoris (RF), vastus lateralis (VL), vastus medialis (VM)) and categorized into non-fatigued and fatigued muscle contractions for the first and last 10 min of the cycling session. For each subject, a total of 1800 contraction-related MMG signals were used to train the SVM classifier and another 300 signals were used for testing. The average classification accuracy (4-fold) of non-fatigued and fatigued state was 90.7% using MFCC feature, 74.5% using root mean square (RMS), and 88.8% with combined MFCC and RMS features. Inter-subject prediction accuracy suggested training and testing data to be based on a particular subject or large collection of subjects to improve fatigue prediction capacity. Graphical abstract ᅟ.
The study and applications of in vivo skin optics have been openly documented as early as the year 1954, or possibly earlier. To date, challenges in analyzing the complexities of this field remain, with wide scopes requiring more scrutiny. Recent advances in spectroscopic research and multivariate analytics allow a closer look into applications potentially for detecting or monitoring diseases. One of the challenges in this field is in establishing a reference for applications which correspond to certain bandwidths. This article reviews the scope on past research on skin spectroscopy, and the clinical aspects which have or may have applications on disease detection or enhancing diagnostics. A summary is supplied on the technicalities surrounding the measurements reported in literature, focused towards the wavelength-dependent applications in themes central to the respective research. Analytics on the topology of the papers' data cited in this work is also provided for a statistical perspective. In short, this paper strives to immediately inform the reader with possible applications via the spectroscopic devices at hand. Graphical Abstract .
Position tracking has been widely used in medical applications, especially in 3D ultrasound imaging, where it has transformed the 2D slice limitation into 3D volume with bigger clinical impacts. As a game controller can also produce position tracking information, it has the potential to act as a low-cost and portable position tracker for ultrasound probes. This paper aims to investigate the feasibility of a game controller to perform as a position tracker and to design its implementation in 3D ultrasound imaging. The study consists of data acquisition and 3D ultrasound reconstruction for visualization. The data acquisition is accomplished by capturing the 2D ultrasound frame and its relative positional and orientation data by using an ultrasound probe and game controller respectively. These data are further reconstructed to produce 3D ultrasound volume for visualization. Our experiments include game controller position tracker testing and 3D ultrasound reconstruction on baby phantom. The results have confirmed that the game controller performance was closely aligned with that of in a robot arm. Also, the 3D ultrasound reconstruction implementation has revealed promising outcomes. With these features, the function of the currently available ultrasound probes can be prospectively improved using a game controller position tracker effectively. Graphical Abstract.