Application of Deep Learning Models for Automated Identification of Parkinson's Disease: A Review (2011-2021).

Parkinson's disease (PD) is the second most common neurodegenerative disorder affecting over 6 million people globally. Although there are symptomatic treatments that can increase the survivability of the disease, there are no curative treatments. The prevalence of PD and disability-adjusted life years continue to increase steadily, leading to a growing burden on patients, their families, society and the economy. Dopaminergic medications can significantly slow down the progression of PD when applied during the early stages. However, these treatments often become less effective with the disease progression. Early diagnosis of PD is crucial for immediate interventions so that the patients can remain self-sufficient for the longest period of time possible. Unfortunately, diagnoses are often late, due to factors such as a global shortage of neurologists skilled in early PD diagnosis. Computer-aided diagnostic (CAD) tools, based on artificial intelligence methods, that can perform automated diagnosis of PD, are gaining attention from healthcare services. In this review, we have identified 63 studies published between January 2011 and July 2021, that proposed deep learning models for an automated diagnosis of PD, using various types of modalities like brain analysis (SPECT, PET, MRI and EEG), and motion symptoms (gait, handwriting, speech and EMG). From these studies, we identify the best performing deep learning model reported for each modality and highlight the current limitations that are hindering the adoption of such CAD tools in healthcare. Finally, we propose new directions to further the studies on deep learning in the automated detection of PD, in the hopes of improving the utility, applicability and impact of such tools to improve early detection of PD globally.

Role of Artificial Intelligence in COVID-19 Detection.

The global pandemic of coronavirus disease (COVID-19) has caused millions of deaths and affected the livelihood of many more people. Early and rapid detection of COVID-19 is a challenging task for the medical community, but it is also crucial in stopping the spread of the SARS-CoV-2 virus. Prior substantiation of artificial intelligence (AI) in various fields of science has encouraged researchers to further address this problem. Various medical imaging modalities including X-ray, computed tomography (CT) and ultrasound (US) using AI techniques have greatly helped to curb the COVID-19 outbreak by assisting with early diagnosis. We carried out a systematic review on state-of-the-art AI techniques applied with X-ray, CT, and US images to detect COVID-19. In this paper, we discuss approaches used by various authors and the significance of these research efforts, the potential challenges, and future trends related to the implementation of an AI system for disease detection during the COVID-19 pandemic.

ADHD/CD-NET: automated EEG-based characterization of ADHD and CD using explainable deep neural network technique.

In this study, attention deficit hyperactivity disorder (ADHD), a childhood neurodevelopmental disorder, is being studied alongside its comorbidity, conduct disorder (CD), a behavioral disorder. Because ADHD and CD share commonalities, distinguishing them is difficult, thus increasing the risk of misdiagnosis. It is crucial that these two conditions are not mistakenly identified as the same because the treatment plan varies depending on whether the patient has CD or ADHD. Hence, this study proposes an electroencephalogram (EEG)-based deep learning system known as ADHD/CD-NET that is capable of objectively distinguishing ADHD, ADHD + CD, and CD. The 12-channel EEG signals were first segmented and converted into channel-wise continuous wavelet transform (CWT) correlation matrices. The resulting matrices were then used to train the convolutional neural network (CNN) model, and the model's performance was evaluated using 10-fold cross-validation. Gradient-weighted class activation mapping (Grad-CAM) was also used to provide explanations for the prediction result made by the 'black box' CNN model. Internal private dataset (45 ADHD, 62 ADHD + CD and 16 CD) and external public dataset (61 ADHD and 60 healthy controls) were used to evaluate ADHD/CD-NET. As a result, ADHD/CD-NET achieved classification accuracy, sensitivity, specificity, and precision of 93.70%, 90.83%, 95.35% and 91.85% for the internal evaluation, and 98.19%, 98.36%, 98.03% and 98.06% for the external evaluation. Grad-CAM also identified significant channels that contributed to the diagnosis outcome. Therefore, ADHD/CD-NET can perform temporal localization and choose significant EEG channels for diagnosis, thus providing objective analysis for mental health professionals and clinicians to consider when making a diagnosis. Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-023-10028-2.

Brain tumor detection and screening using artificial intelligence techniques: Current trends and future perspectives.

A brain tumor is an abnormal mass of tissue located inside the skull. In addition to putting pressure on the healthy parts of the brain, it can lead to significant health problems. Depending on the region of the brain tumor, it can cause a wide range of health issues. As malignant brain tumors grow rapidly, the mortality rate of individuals with this cancer can increase substantially with each passing week. Hence it is vital to detect these tumors early so that preventive measures can be taken at the initial stages. Computer-aided diagnostic (CAD) systems, in coordination with artificial intelligence (AI) techniques, have a vital role in the early detection of this disorder. In this review, we studied 124 research articles published from 2000 to 2022. Here, the challenges faced by CAD systems based on different modalities are highlighted along with the current requirements of this domain and future prospects in this area of research.

Deep neural network technique for automated detection of ADHD and CD using ECG signal.

BACKGROUND AND OBJECTIVE: Attention Deficit Hyperactivity problem (ADHD) is a common neurodevelopment problem in children and adolescents that can lead to long-term challenges in life outcomes if left untreated. Also, ADHD is frequently associated with Conduct Disorder (CD), and multiple research have found similarities in clinical signs and behavioral symptoms between both diseases, making differentiation between ADHD, ADHD comorbid with CD (ADHD+CD), and CD a subjective diagnosis. Therefore, the goal of this pilot study is to create the first explainable deep learning (DL) model for objective ECG-based ADHD/CD diagnosis as having an objective biomarker may improve diagnostic accuracy. METHODS: The dataset used in this study consist of ECG data collected from 45 ADHD, 62 ADHD+CD, and 16 CD patients at the Child Guidance Clinic in Singapore. The ECG data were segmented into 2 s epochs and directly used to train our 1-dimensional (1D) convolutional neural network (CNN) model. RESULTS: The proposed model yielded 96.04% classification accuracy, 96.26% precision, 95.99% sensitivity, and 96.11% F1-score. The Gradient-weighted class activation mapping (Grad-CAM) function was also used to highlight the important ECG characteristics at specific time points that most impact the classification score. CONCLUSION: In addition to achieving model performance results with our suggested DL method, Grad-CAM's implementation also offers vital temporal data that clinicians and other mental healthcare professionals can use to make wise medical judgments. We hope that by conducting this pilot study, we will be able to encourage larger-scale research with a larger biosignal dataset. Hence allowing biosignal-based computer-aided diagnostic (CAD) tools to be implemented in healthcare and ambulatory settings, as ECG can be easily obtained via wearable devices such as smartwatches.

Fabrication of cisplatin-loaded poly(lactide-co-glycolide) composite microspheres for osteosarcoma treatment.

PURPOSE: To reduce the toxicity and achieve a sustainable and controllable release of cisplatin (CDDP). METHODS: CDDP was loaded onto Fe5 (Fe(3+) doped hydroxyapatite at atomic ratio of Fe(added)/Ca(added) = 5%) nanoparticles through surface adsorption. Subsequently, CDDP-loaded Fe5 nanoparticles (CDDP-Fe5) and/or CDDP were encapsulated into poly(lactide-co-glycolide) (PLGA) microspheres using oil-in-water single emulsion. Drug release profiles and degradation behaviors were monitored. RESULTS: CDDP-Fe5 demonstrated a high initial burst (42% on day 1) and short release time (25 days) as CDDP was directly released from Fe5 nanoparticles. CDDP-Fe5 encapsulated within the PLGA microspheres revealed a lower initial burst (23% on day 1) and longer release time (55 days) than CDDP-Fe5. Compared with PLGA microspheres containing only CDDP, which showed typical biphasic release manner, microspheres with CDDP-Fe5 and CDDP demonstrated a nearly linear release after the initial burst. Fe5 and CDDP delayed microsphere degradation. All samples became porous, disintegrated, fused, and formed pellets at the end of the study. CONCLUSION: Fe5/PLGA composite microspheres showed favorable CDDP release behavior compared to microspheres composed of polymer alone, suggesting its potential as a new CDDP formulation.

5-Fluorouracil encapsulated HA/PLGA composite microspheres for cancer therapy.

5-Fluorouracil (5FU) was successfully entrapped within poly(lactide-co-glycolide) (PLGA) and hydroyapatite (HA) composite microspheres using the emulsification/solvent extraction technique. The effects of HA to PLGA ratio, solvent ratio as well as polymer inherent viscosity (IV) on encapsulation efficiency were investigated. The degradation and drug release rates of the microspheres were studied for 5 weeks in vitro in phosphate buffered solution of pH 7.4 at 37 °C. The drug release profile followed a biphasic pattern with a small initial burst followed by a zero-order release for up to 35 days. The initial burst release decreased with increasing HA content. The potential of HA in limiting the initial burst release makes the incorporation of HA into PLGA microspheres advantageous since it reduces the risk of drug overdose from high initial bursts. The linear sustained drug release profile over the course of 5 weeks makes these 5-FU-loaded HA/PLGA composite microparticles a promising delivery system for the controlled release of chemotherapy drugs in the treatment of cancer.

Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011-2022).

BACKGROUND AND OBJECTIVES: Artificial intelligence (AI) has branched out to various applications in healthcare, such as health services management, predictive medicine, clinical decision-making, and patient data and diagnostics. Although AI models have achieved human-like performance, their use is still limited because they are seen as a black box. This lack of trust remains the main reason for their low use in practice, especially in healthcare. Hence, explainable artificial intelligence (XAI) has been introduced as a technique that can provide confidence in the model's prediction by explaining how the prediction is derived, thereby encouraging the use of AI systems in healthcare. The primary goal of this review is to provide areas of healthcare that require more attention from the XAI research community. METHODS: Multiple journal databases were thoroughly searched using PRISMA guidelines 2020. Studies that do not appear in Q1 journals, which are highly credible, were excluded. RESULTS: In this review, we surveyed 99 Q1 articles covering the following XAI techniques: SHAP, LIME, GradCAM, LRP, Fuzzy classifier, EBM, CBR, rule-based systems, and others. CONCLUSION: We discovered that detecting abnormalities in 1D biosignals and identifying key text in clinical notes are areas that require more attention from the XAI research community. We hope this is review will encourage the development of a holistic cloud system for a smart city.

Application of photoplethysmography signals for healthcare systems: An in-depth review.

BACKGROUND AND OBJECTIVES: Photoplethysmography (PPG) is a device that measures the amount of light absorbed by the blood vessel, blood, and tissues, which can, in turn, translate into various measurements such as the variation in blood flow volume, heart rate variability, blood pressure, etc. Hence, PPG signals can produce a wide variety of biological information that can be useful for the detection and diagnosis of various health problems. In this review, we are interested in the possible health disorders that can be detected using PPG signals. METHODS: We applied PRISMA guidelines to systematically search various journal databases and identified 43 PPG studies that fit the criteria of this review. RESULTS: Twenty-five health issues were identified from these studies that were classified into six categories: cardiac, blood pressure, sleep health, mental health, diabetes, and miscellaneous. Various routes were employed in these PPG studies to perform the diagnosis: machine learning, deep learning, and statistical routes. The studies were reviewed and summarized. CONCLUSIONS: We identified limitations such as poor standardization of sampling frequencies and lack of publicly available PPG databases. We urge that future work should consider creating more publicly available databases so that a wide spectrum of health problems can be covered. We also want to promote the use of PPG signals as a potential precision medicine tool in both ambulatory and hospital settings.

CGP17Pat: Automated Schizophrenia Detection Based on a Cyclic Group of Prime Order Patterns Using EEG Signals.

BACKGROUND AND PURPOSE: Machine learning models have been used to diagnose schizophrenia. The main purpose of this research is to introduce an effective schizophrenia hand-modeled classification method. METHOD: A public electroencephalogram (EEG) signal data set was used in this work, and an automated schizophrenia detection model is presented using a cyclic group of prime order with a modulo 17 operator. Therefore, the presented feature extractor was named as the cyclic group of prime order pattern, CGP17Pat. Using the proposed CGP17Pat, a new multilevel feature extraction model is presented. To choose a highly distinctive feature, iterative neighborhood component analysis (INCA) was used, and these features were classified using k-nearest neighbors (kNN) with the 10-fold cross-validation and leave-one-subject-out (LOSO) validation techniques. Finally, iterative hard majority voting was employed in the last phase to obtain channel-wise results, and the general results were calculated. RESULTS: The presented CGP17Pat-based EEG classification model attained 99.91% accuracy employing 10-fold cross-validation and 84.33% accuracy using the LOSO strategy. CONCLUSIONS: The findings and results depicted the high classification ability of the presented cryptologic pattern for the data set used.

Effects of different delivery modes on teaching biomedical science practical skills in higher education during the 2021 pandemic measures.

The COVID-19 pandemic related measures had augmented the rise of online education. While online teaching had mitigated the negative impacts from educational institutional closures, it was unable to displace hands-on biomedical laboratory practical lessons effectively. Without practical sessions, there was concern over the imparting of laboratory skills even with video demonstrations. To investigate the effectiveness of different delivery modes in imparting laboratory skills, theoretical and practical student assessments were analyzed alongside an anonymous survey on their motivation and prior experience. The undergraduate students were exposed to (1) instructor-live demonstration; (2) video demonstration or (3) no demonstration prior to the practical test which was a plasmid extraction. Significantly higher mini-prep yields and purity were found for both instructor-live and video demonstrations compared to no demonstration. Comparison with pre-pandemic theoretical assessment performance showed no significant differences despite longer contact hours during pre-pandemic times. Prior lab experience and motivation for selecting the course did not significantly affect student mini-prep yields. In conclusion, our findings suggest that video demonstrations were as effective as instructor-live demonstrations during the pandemic without noticeably compromising the teaching and learning of biomedical laboratory skills.

Automated detection of ADHD: Current trends and future perspective.

Attention deficit hyperactivity disorder (ADHD) is a heterogenous disorder that has a detrimental impact on the neurodevelopment of the brain. ADHD patients exhibit combinations of inattention, impulsiveness, and hyperactivity. With early treatment and diagnosis, there is potential to modify neuronal connections and improve symptoms. However, the heterogeneous nature of ADHD, combined with its comorbidities and a global shortage of diagnostic clinicians, means diagnosis of ADHD is often delayed. Hence, it is important to consider other pathways to improve the efficiency of early diagnosis, including the role of artificial intelligence. In this study, we reviewed the current literature on machine learning and deep learning studies on ADHD diagnosis and identified the various diagnostic tools used. Subsequently, we categorized these studies according to their diagnostic tool as brain magnetic resonance imaging (MRI), physiological signals, questionnaires, game simulator and performance test, and motion data. We identified research gaps include the paucity of publicly available database for all modalities in ADHD assessment other than MRI, as well as a lack of focus on using data from wearable devices for ADHD diagnosis, such as ECG, PPG, and motion data. We hope that this review will inspire future work to create more publicly available datasets and conduct research for other modes of ADHD diagnosis and monitoring. Ultimately, we hope that artificial intelligence can be extended to multiple ADHD diagnostic tools, allowing for the development of a powerful clinical decision support pathway that can be used both in and out of the hospital.

Automated classification of attention deficit hyperactivity disorder and conduct disorder using entropy features with ECG signals.

BACKGROUND: The most prevalent neuropsychiatric disorder among children is attention deficit hyperactivity disorder (ADHD). ADHD presents with a high prevalence of comorbid disorders such as conduct disorder (CD). The lack of definitive confirmatory diagnostic tests for ADHD and CD make diagnosis challenging. The distinction between ADHD, ADHD + CD and CD is important as the course and treatment are different. Electrocardiography (ECG) signals may become altered in behavioral disorders due to brain-heart autonomic interactions. We have developed a software tool to categorize ADHD, ADHD + CD and CD automatically on ECG signals. METHOD: ECG signals from participants were decomposed using empirical wavelet transform into various modes, from which entropy features were extracted. Robust ten-fold cross-validation with adaptive synthetic sampling (ADASYN) and z-score normalization were performed at each fold. Analysis of variance (ANOVA) technique was employed to determine the variability within the three classes, and obtained the most discriminatory features. Highly significant entropy features were then fed to classifiers. RESULTS: Our model yielded the best classification results with the bagged tree classifier: 87.19%, 87.71% and 86.29% for accuracy, sensitivity and specificity, respectively. CONCLUSION: The proposed expert system can potentially assist mental health professionals in the stratification of the three classes, for appropriate intervention using accessible ECG signals.

Automated detection of conduct disorder and attention deficit hyperactivity disorder using decomposition and nonlinear techniques with EEG signals.

BACKGROUND AND OBJECTIVES: Attention deficit hyperactivity disorder (ADHD) is often presented with conduct disorder (CD). There is currently no objective laboratory test or diagnostic method to discern between ADHD and CD, and diagnosis is further made difficult as ADHD is a common neuro-developmental disorder often presenting with other co-morbid difficulties; and in particular with conduct disorder which has a high degree of associated behavioural challenges. A novel automated system (AS) is proposed as a convenient supplementary tool to support clinicians in their diagnostic decisions. To the best of our knowledge, we are the first group to develop an automated classification system to classify ADHD, CD and ADHD+CD classes using brain signals. METHODS: The empirical mode decomposition (EMD) and discrete wavelet transform (DWT) methods were employed to decompose the electroencephalogram (EEG) signals. Autoregressive modelling coefficients and relative wavelet energy were then computed on the signals. Various nonlinear features were extracted from the decomposed coefficients. Adaptive synthetic sampling (ADASYN) was then employed to balance the dataset. The significant features were selected using sequential forward selection method. The highly discriminatory features were subsequently fed to an array of classifiers. RESULTS: The highest accuracy of 97.88% was achieved with the K-Nearest Neighbour (KNN) classifier. The proposed system was developed using ten-fold validation strategy on EEG data from 123 children. To the best of our knowledge this is the first study to develop an AS for the classification of ADHD, CD and ADHD+CD classes using EEG signals. POTENTIAL APPLICATION: Our AS can potentially be used as a web-based application with cloud system to aid the clinical diagnosis of ADHD and/or CD, thus supporting faster and accurate treatment for the children. It is important to note that testing with larger data is required before the AS can be employed for clinical applications.

Automated interpretation of biopsy images for the detection of celiac disease using a machine learning approach.

BACKGROUND AND OBJECTIVES: Celiac disease is an autoimmune disease occurring in about 1 in 100 people worldwide. Early diagnosis and efficient treatment are crucial in mitigating the complications that are associated with untreated celiac disease, such as intestinal lymphoma and malignancy, and the subsequent high morbidity. The current diagnostic methods using small intestinal biopsy histopathology, endoscopy, and video capsule endoscopy (VCE) involve manual interpretation of photomicrographs or images, which can be time-consuming and difficult, with inter-observer variability. In this paper, a machine learning technique was developed for the automation of biopsy image analysis to detect and classify villous atrophy based on modified Marsh scores. This is one of the first studies to employ conventional machine learning to automate the use of biopsy images for celiac disease detection and classification. METHODS: The Steerable Pyramid Transform (SPT) method was used to obtain sub bands from which various types of entropy and nonlinear features were computed. All extracted features were automatically classified into two-class and multi-class, using six classifiers. RESULTS: An accuracy of 88.89%, was achieved for the classification of two-class villous abnormalities based on analysis of Hematoxylin and Eosin (H&E) stained biopsy images. Similarly, an accuracy of 82.92% was achieved for the two-class classification of red-green-blue (RGB) biopsy images. Also, an accuracy of 72% was achieved in the classification of multi-class biopsy images. CONCLUSION: The results obtained are promising, and demonstrate the possibility of automating biopsy image interpretation using machine learning. This can assist pathologists in accelerating the diagnostic process without bias, resulting in greater accuracy, and ultimately, earlier access to treatment.

Classification of heart sound signals using a novel deep WaveNet model.

BACKGROUND AND OBJECTIVES: The high mortality rate and increasing prevalence of heart valve diseases globally warrant the need for rapid and accurate diagnosis of such diseases. Phonocardiogram (PCG) signals are used in this study due to the low cost of obtaining the signals. This study classifies five types of heart sounds, namely normal, aortic stenosis, mitral valve prolapse, mitral stenosis, and mitral regurgitation. METHODS: We have proposed a novel in-house developed deep WaveNet model for automated classification of five types of heart sounds. The model is developed using a total of 1000 PCG recordings belonging to five classes with 200 recordings in each class. RESULTS: We have achieved a training accuracy of 97% for the classification of heart sounds into five classes. The highest classification accuracy of 98.20% was achieved for the normal class. The developed model was validated with a 10-fold cross-validation, thus affirming its robustness. CONCLUSION: The study results clearly indicate that the developed model is able to classify five types of heart sounds accurately. The developed system can be used by cardiologists to aid in the detection of heart valve diseases in patients.

Hydrolytic degradation and drug release properties of ganciclovir-loaded biodegradable microspheres.

The in vitro hydrolytic degradation of ganciclovir (GCV)-loaded biodegradable microspheres of poly(D,L-lactide) and poly(D,L-lactide-co-glycolide) polymers were studied. Microspheres of size 120+/-40 microm were prepared using an oil-in-water emulsification/solvent evaporation technique. The effects of polymer molecular weight, lactide (LA) to glycolide (GA) ratio and GCV payload on the degradation and drug release profiles were investigated in vitro in phosphate-buffered solution (pH 7.0) at 37 degrees C. GCV accelerated the hydrolysis process of the low (5-7 wt.%) GCV-loaded microspheres due to a basic catalytic effect, giving a larger degradation rate, k', compared with blank and high (18-20 wt.%) GCV-loaded microspheres. In the high GCV-loaded microspheres, hydrolysis of the polymer backbone occurred with little and/or no autocatalytic effect, resulting in a smaller k' compared with low GCV-loaded microspheres. This was due to pores and microchannels created at the surface following the initial burst release, which increased water uptake and the dissolution and diffusion of GCV and degradation products from the matrix. The rate of hydrolytic degradation was also affected by the LA to GA ratio. For polymers of similar LA to GA ratio, those with a higher degree of blockiness had faster hydrolytic degradation rates irrespective of the initial molecular weight. The release profile had a biphasic pattern, which closely followed the degradation profile of the polymer. The time taken for the complete release of GCV was controlled by the diffusion phase and was dependent on the hydrolytic degradation rate of the polymers.

Hydrolytic degradation of electron beam irradiated high molecular weight and non-irradiated moderate molecular weight PLLA.

The purpose of this study is to examine the hydrolytic degradation of electron beam irradiated ring-opening polymerized (ROP) poly(l-lactide) (PLLA-ir) and non-irradiated melt polycondensation polymerized poly(l-lactic acid) (PLLA-pc). It was observed that irradiation increases the hydrolytic degradation rate constant for ROP PLLA. This was due to a more hydrophilic PLLA-ir, as a result of irradiation. The degradation rate constants (k) of PLLA-ir samples were also found to be similar, regardless of the radiation dose, and an empirically formulated equation relating hydrolytic degradation time span to radiation dose was derived. The k value for PLLA-pc was observed to be lower than that for PLLA-ir, though the latter had a higher molecular weight. This was due to the difference in degradation mechanism, in which PLLA-ir undergoes end group scission, through a back- biting mechanism, during hydrolysis and thus a faster hydrolysis rate. Electron beam irradiation, though accelerates the degradation of PLLA, has been shown to be useful in accurately controlling the hydrolytic time span of PLLA. This method of controlling the hydrolytic degradation time was by far an easier task than through melt polycondensation polymerization. This would allow PLLA to be used for drug delivery purposes or as a temporary implant that requires a moderate time span (3-6 months).

Influence of electron-beam radiation on the hydrolytic degradation behaviour of poly(lactide-co-glycolide) (PLGA).

The purpose of this study is to examine the effect of electron-beam (e-beam) radiation on the hydrolytic degradation of poly(lactide-co-glycolide) (PLGA) films. PLGA films were irradiated and observed to undergo radiation-induced degradation through chain scission, as observed from a drop in its average molecular weight with radiation dose. Irradiated (5, 10 and 20 Mrad) and non-irradiated (0 Mrad) samples of PLGA were subsequently hydrolytically degraded in phosphate-buffered saline solution at 37.0 degrees C over a span of 12 weeks. It was observed that the natural logarithmic molecular weight (lnMn) of PLGA decreases linearly with hydrolytic degradation time. The rate of water uptake is higher for samples irradiated at higher radiation dose (e.g. 20 Mrad) and subsequently causing an earlier onset of mass loss. It is postulated that the increase in water uptake is due to the presence of more hydrophilic end groups, which results in the formation of microcavities because of an increase in osmotic pressure. A relationship between radiation dose and the rate of hydrolytic degradation of PLGA films, through its molecular weight was also established. This relationship allows a more accurate and precise control of the life span of PLGA through the use of e-beam radiation.

Effect of isothermal annealing on the hydrolytic degradation rate of poly(lactide-co-glycolide) (PLGA).

Isothermal crystallization through annealing at 115 degrees C was conducted to increase the degree of crystallinity of poly (lactide-co-glycolide) (PLGA). The maximum increase in the degree of crystallinity (approximately 21%) was achieved after 60 min of annealing. The crystal size/perfection was observed to increase with annealing time. The annealed PLGA films were then hydrolytically degraded in phosphate buffered saline solution of pH 7.4 at 37 degrees C for up to 150 days. Minimal mass loss was observed throughout the time investigated, suggesting that the samples were still in the first phase of degradation. The increase in the degree of crystallinity of the PLGA samples annealed at 15 and 30 min was found to retard their overall rate of hydrolytic degradation, when compared to those samples with higher initial crystallinity (annealed for 45 and 60 min) that had faster degradation rates. The increased degradation rate at higher crystallinity was associated with the loss of amorphous material and the formation of voids during annealing, which decreases the glass transition temperature and increases the average water uptake in the samples annealed for longer times. Therefore, the increase in degree of crystallinity is found to retard hydrolytic degradation but only to a certain extent, beyond which the formation of voids through annealing increases the rate of hydrolytic degradation.