Meta-Heuristic Algorithm-Tuned Neural Network for Breast Cancer Diagnosis Using Ultrasound Images.

Breast cancer is the most menacing cancer among all types of cancer in women around the globe. Early diagnosis is the only way to increase the treatment options which then decreases the death rate and increases the chance of survival in patients. However, it is a challenging task to differentiate abnormal breast tissues from normal tissues because of their structure and unclear boundaries. Therefore, early and accurate diagnosis and classification of breast lesions into malignant or benign lesions is an active domain of research. Over the decade, numerous artificial neural network (ANN)-based techniques were adopted in order to diagnose and classify breast cancer due to the unique characteristics of learning key features from complex data via a training process. However, these schemes have limitations like slow convergence and longer training time. To address the above mentioned issues, this paper employs a meta-heuristic algorithm for tuning the parameters of the neural network. The main novelty of this work is the computer-aided diagnosis scheme for detecting abnormalities in breast ultrasound images by integrating a wavelet neural network (WNN) and the grey wolf optimization (GWO) algorithm. Here, breast ultrasound (US) images are preprocessed with a sigmoid filter followed by interference-based despeckling and then by anisotropic diffusion. The automatic segmentation algorithm is adopted to extract the region of interest, and subsequently morphological and texture features are computed. Finally, the GWO-tuned WNN is exploited to accomplish the classification task. The classification performance of the proposed scheme is validated on 346 ultrasound images. Efficiency of the proposed methodology is evaluated by computing the confusion matrix and receiver operating characteristic (ROC) curve. Numerical analysis revealed that the proposed work can yield higher classification accuracy when compared to the prevailing methods and thereby proves its potential in effective breast tumor detection and classification. The proposed GWO-WNN method (98%) gives better accuracy than other methods like SOM-SVM (87.5), LOFA-SVM (93.62%), MBA-RF (96.85%), and BAS-BPNN (96.3%).

Hybrid Model for Detection of Cervical Cancer Using Causal Analysis and Machine Learning Techniques.

Cervical cancer has become the third most common form of cancer in the in-universe, after the widespread breast cancer. Human papillomavirus risk of infection is linked to the majority of cancer cases. Preventive care, the most expensive way of fighting cancer, can protect about 37% of cancer cases. The Pap smear examination is a standard screening procedure for the initial screening of cervical cancer. However, this manual test procedure generates many false-positive outcomes due to individual errors. Various researchers have extensively investigated machine learning (ML) methods for classifying cervical Pap cells to enhance manual testing. The random forest method is the most popular method for anticipating features from a high-dimensional cancer image dataset. However, the random forest method can get too slow and inefficient for real-time forecasts when too many decision trees are used. This research proposed an efficient feature selection and prediction model for cervical cancer datasets using Boruta analysis and SVM method to deal with this challenge. A Boruta analysis method is used. It is improved from of random forest method and mainly discovers feature subsets from the data source that are significant to assigned classification activity. The proposed model's primary aim is to determine the importance of cervical cancer screening factors for classifying high-risk patients depending on the findings. This research work analyses cervical cancer and various risk factors to help detect cervical cancer. The proposed model Boruta with SVM and various popular ML models are implemented using Python and various performance measuring parameters, i.e., accuracy, precision, F1-Score, and recall. However, the proposed Boruta analysis with SVM performs outstanding over existing methods.

Evaluation of Neuro Images for the Diagnosis of Alzheimer's Disease Using Deep Learning Neural Network.

Alzheimer's Disease (AD) is a progressive, neurodegenerative brain disease and is an incurable ailment. No drug exists for AD, but its progression can be delayed if the disorder is identified at its initial stage. Therefore, an early analysis of AD is of fundamental importance for patient care and efficient treatment. Neuroimaging techniques aim to assist the physician in the diagnosis of brain disorders by using images. Positron emission tomography (PET) is a kind of neuroimaging technique employed to create 3D images of the brain. Due to many PET images, researchers attempted to develop computer-aided diagnosis (CAD) to differentiate normal control from AD. Most of the earlier methods used image processing techniques for preprocessing and attributes extraction and then developed a model or classifier to classify the brain images. As a result, the retrieved features had a significant impact on the recognition rate of previous techniques. A novel and enhanced CAD system based on a convolutional neural network (CNN) is formulated to address this issue, capable of discriminating normal control from Alzheimer's disease patients. The proposed approach is evaluated using the 18FDG-PET images of 855 patients, including 635 normal control and 220 Alzheimer's disease patients from the ADNI database. The result showed that the proposed CAD system yields an accuracy of 96%, a sensitivity of 96%, and a specificity of 94%, leading to splendid performance when related to the methods already in use that are specified in the literature.

Gulf Cooperation Council Clinical Trials in the Pursuit of Medications for COVID-19.

Tremendous changes have been witnessed in the post-COVID-19 world. Global efforts were initiated to reach a successful treatment for this emerging disease. These efforts have focused on developing vaccinations and/or finding therapeutic agents that can be used to combat the virus or reduce its accompanying symptoms. Gulf Cooperation Council (GCC) countries have initiated efforts on many clinical trials to address the efficacy and the safety of several therapeutic agents used for COVID-19 treatment. In this article, we provide an overview of the GCC's clinical trials and associated drugs' discovery process in the pursuit of an effective medication for COVID-19.

Diagnosis and combating COVID-19 using wearable Oura smart ring with deep learning methods.

Since the coronavirus (COVID-19) outbreak keeps on spreading all through the world, scientists have been crafting varied technologies mainly focusing on AI for an approach to acknowledge the difficulties of the epidemic. In this current worldwide emergency, the clinical business is searching for new advancements to screen and combat COVID-19 contamination. Strategies used by artificial intelligence can stretch screen the spread of the infection, distinguish highly infected patients, and be compelling in supervising the illness continuously. The artificial intelligence anticipation can further be used for passing dangers by sufficiently dissecting information from past sufferers. International patient support with recommendations for population testing, medical care, notification, and infection control can help fight this deadly virus. We proposed the hybrid deep learning method to diagnose COVID-19. The layered approach is used here to measure the symptom level of the patients and to analyze the patient image data whether he/she is positive with COVID-19. This work utilizes smart AI techniques to predict and diagnose the coronavirus rapidly by the Oura smart ring within 24 h. In the laboratory, a coronavirus rapid test is prepared with the help of a deep learning model using the RNN and CNN algorithms to diagnose the coronavirus rapidly and accurately. The result shows the value 0 or 1. The result 1 indicates the person is affected with coronavirus and the result 0 indicates the person is not affected with coronavirus. X-Ray and CT image classifications are considered here so that the threshold value is utilized for identifying an individual's health condition from the initial stage to a severe stage. Threshold value 0.5 is used to identify coronavirus initial stage condition and 1 is used to identify the coronavirus severe condition of the patient. The proposed methods are utilized for four weighting parameters to reduce both false positive and false negative image classification results for rapid and accurate diagnosis of COVID-19.

Correction to: Diagnosis and combating COVID-19 using wearable Oura smart ring with deep learning methods.

[This corrects the article DOI: 10.1007/s00779-021-01541-4.].

Federated Transfer Learning for Authentication and Privacy Preservation Using Novel Supportive Twin Delayed DDPG (S-TD3) Algorithm for IIoT.

The Industrial Internet of Things (IIoT) has led to the growth and expansion of various new opportunities in the new Industrial Transformation. There have been notable challenges regarding the security of data and challenges related to privacy when collecting real-time and automatic data while observing applications in the industry. This paper proposes an Federated Transfer Learning for Authentication and Privacy Preservation Using Novel Supportive Twin Delayed DDPG (S-TD3) Algorithm for IIoT. In FT-Block (Federated transfer learning blockchain), several blockchains are applied to preserve privacy and security for all types of industrial applications. Additionally, by introducing the authentication mechanism based on transfer learning, blockchains can enhance the preservation and security standards for industrial applications. Specifically, Novel Supportive Twin Delayed DDPG trains the user model to authenticate specific regions. As it is considered one of the most open and scalable interacting platforms of information, it successfully helps in the positive transfer of different kinds of data between devices in more significant and local operations of the industry. It is mainly due to a single authentication factor, and the poor adaptation to regular increases in the number of users and different requirements that make the current authentication mechanism suffer a lot in IIoT. As a result, it has been very clearly observed that the given solutions are very useful.

The Recent Technologies to Curb the Second-Wave of COVID-19 Pandemic.

Different epidemics, specially Coronavirus, have caused critical misfortunes in various fields like monetary deprivation, survival conditions, thus diminishing the overall individual fulfillment. Various worldwide associations and different hierarchies of government fraternity are endeavoring to offer the necessary assistance in eliminating the infection impacts but unfortunately standing up to the non-appearance of resources and expertise. In contrast to all other pandemics, Coronavirus has proven to exhibit numerous requirements such that curated appropriation and determination of innovations are required to deal with the vigorous undertakings, which include precaution, detection, and medication. Innovative advancements are essential for the subsequent pandemics where-in the forthcoming difficulties can indeed be approached to such a degree that it facilitates constructive solutions more comprehensively. In this study, futuristic and emerging innovations are analyzed, improving COVID-19 effects for the general public. Large data sets need to be advanced so that extensive models related to deep analysis can be used to combat Coronavirus infection, which can be done by applying Artificial intelligence techniques such as Natural Language Processing (NLP), Machine Learning (ML), and Computer vision to varying processing files. This article aims to furnish variation sets of innovations that can be utilized to eliminate COVID-19 and serve as a resource for the coming generations. At last, elaboration associated with future state-of-the-art technologies and the attainable sectors of AI methodologies has been mentioned concerning the post-COVID-19 world to enable the different ideas for dealing with the pandemic-based difficulties.

Blockchain technologies to mitigate COVID-19 challenges: A scoping review.

Background: As public health strategists and policymakers explore different approaches to lessen the devastating effects of novel coronavirus disease (COVID-19), blockchain technology has emerged as a resource that can be utilized in numerous ways. Many blockchain technologies have been proposed or implemented during the COVID-19 pandemic; however, to the best of our knowledge, no comprehensive reviews have been conducted to uncover and summarise the main feature of these technologies. Objective: This study aims to explore proposed or implemented blockchain technologies used to mitigate the COVID-19 challenges as reported in the literature. Methods: We conducted a scoping review in line with guidelines of PRISMA Extension for Scoping Reviews (PRISMA-ScR). To identify relevant studies, we searched 11 bibliographic databases (e.g., EMBASE and MEDLINE) and conducted backward and forward reference list checking of the included studies and relevant reviews. The study selection and data extraction were conducted by 2 reviewers independently. Data extracted from the included studies was narratively summarised and described. Results: 19 of 225 retrieved studies met eligibility criteria in this review. The included studies reported 10 used cases of blockchain to mitigate COVID-19 challenges; the most prominent use cases were contact tracing and immunity passports. While the blockchain technology was developed in 10 studies, its use was proposed in the remaining 9 studies. The public blockchain technology was the most commonly utilized type in the included studies. All together, 8 different consensus mechanisms were used in the included studies. Out of 10 studies that identified the used platform, 9 studies used Ethereum to run the blockchain. Solidity was the most prominent programming language used in developing blockchain technology in the included studies. The transaction cost was reported in only 4 of the included studies and varied between USD 10-10 and USD 5. The expected latency and expected scalability were not identified in the included studies. Conclusion: Blockchain technologies are expected to play an integral role in the fight against the COVID-19 pandemic. Many possible applications of blockchain were found in this review; however, most of them are not mature enough to reveal their expected impact in the fight against COVID-19. We encourage governments, health authorities, and policymakers to consider all blockchain applications suggested in the current review to combat COVID-19 challenges. There is a pressing need to empirically examine how effective blockchain technologies are in mitigating COVID-19 challenges. Further studies are required to assess the performance of blockchain technologies' fight against COVID-19 in terms of transaction cost, scalability, and/or latency when using different consensus algorithms, platforms, and access types.

A Comprehensive Overview of the COVID-19 Literature: Machine Learning-Based Bibliometric Analysis.

BACKGROUND: Shortly after the emergence of COVID-19, researchers rapidly mobilized to study numerous aspects of the disease such as its evolution, clinical manifestations, effects, treatments, and vaccinations. This led to a rapid increase in the number of COVID-19-related publications. Identifying trends and areas of interest using traditional review methods (eg, scoping and systematic reviews) for such a large domain area is challenging. OBJECTIVE: We aimed to conduct an extensive bibliometric analysis to provide a comprehensive overview of the COVID-19 literature. METHODS: We used the COVID-19 Open Research Dataset (CORD-19) that consists of a large number of research articles related to all coronaviruses. We used a machine learning-based method to analyze the most relevant COVID-19-related articles and extracted the most prominent topics. Specifically, we used a clustering algorithm to group published articles based on the similarity of their abstracts to identify research hotspots and current research directions. We have made our software accessible to the community via GitHub. RESULTS: Of the 196,630 publications retrieved from the database, we included 28,904 in our analysis. The mean number of weekly publications was 990 (SD 789.3). The country that published the highest number of COVID-19-related articles was China (2950/17,270, 17.08%). The highest number of articles were published in bioRxiv. Lei Liu affiliated with the Southern University of Science and Technology in China published the highest number of articles (n=46). Based on titles and abstracts alone, we were able to identify 1515 surveys, 733 systematic reviews, 512 cohort studies, 480 meta-analyses, and 362 randomized control trials. We identified 19 different topics covered among the publications reviewed. The most dominant topic was public health response, followed by clinical care practices during the COVID-19 pandemic, clinical characteristics and risk factors, and epidemic models for its spread. CONCLUSIONS: We provide an overview of the COVID-19 literature and have identified current hotspots and research directions. Our findings can be useful for the research community to help prioritize research needs and recognize leading COVID-19 researchers, institutes, countries, and publishers. Our study shows that an AI-based bibliometric analysis has the potential to rapidly explore a large corpus of academic publications during a public health crisis. We believe that this work can be used to analyze other eHealth-related literature to help clinicians, administrators, and policy makers to obtain a holistic view of the literature and be able to categorize different topics of the existing research for further analyses. It can be further scaled (for instance, in time) to clinical summary documentation. Publishers should avoid noise in the data by developing a way to trace the evolution of individual publications and unique authors.

Artificial Intelligence in the Fight Against COVID-19: Scoping Review.

BACKGROUND: In December 2019, COVID-19 broke out in Wuhan, China, leading to national and international disruptions in health care, business, education, transportation, and nearly every aspect of our daily lives. Artificial intelligence (AI) has been leveraged amid the COVID-19 pandemic; however, little is known about its use for supporting public health efforts. OBJECTIVE: This scoping review aims to explore how AI technology is being used during the COVID-19 pandemic, as reported in the literature. Thus, it is the first review that describes and summarizes features of the identified AI techniques and data sets used for their development and validation. METHODS: A scoping review was conducted following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). We searched the most commonly used electronic databases (eg, MEDLINE, EMBASE, and PsycInfo) between April 10 and 12, 2020. These terms were selected based on the target intervention (ie, AI) and the target disease (ie, COVID-19). Two reviewers independently conducted study selection and data extraction. A narrative approach was used to synthesize the extracted data. RESULTS: We considered 82 studies out of the 435 retrieved studies. The most common use of AI was diagnosing COVID-19 cases based on various indicators. AI was also employed in drug and vaccine discovery or repurposing and for assessing their safety. Further, the included studies used AI for forecasting the epidemic development of COVID-19 and predicting its potential hosts and reservoirs. Researchers used AI for patient outcome-related tasks such as assessing the severity of COVID-19, predicting mortality risk, its associated factors, and the length of hospital stay. AI was used for infodemiology to raise awareness to use water, sanitation, and hygiene. The most prominent AI technique used was convolutional neural network, followed by support vector machine. CONCLUSIONS: The included studies showed that AI has the potential to fight against COVID-19. However, many of the proposed methods are not yet clinically accepted. Thus, the most rewarding research will be on methods promising value beyond COVID-19. More efforts are needed for developing standardized reporting protocols or guidelines for studies on AI.

Top Concerns of Tweeters During the COVID-19 Pandemic: Infoveillance Study.

BACKGROUND: The recent coronavirus disease (COVID-19) pandemic is taking a toll on the world's health care infrastructure as well as the social, economic, and psychological well-being of humanity. Individuals, organizations, and governments are using social media to communicate with each other on a number of issues relating to the COVID-19 pandemic. Not much is known about the topics being shared on social media platforms relating to COVID-19. Analyzing such information can help policy makers and health care organizations assess the needs of their stakeholders and address them appropriately. OBJECTIVE: This study aims to identify the main topics posted by Twitter users related to the COVID-19 pandemic. METHODS: Leveraging a set of tools (Twitter's search application programming interface (API), Tweepy Python library, and PostgreSQL database) and using a set of predefined search terms ("corona," "2019-nCov," and "COVID-19"), we extracted the text and metadata (number of likes and retweets, and user profile information including the number of followers) of public English language tweets from February 2, 2020, to March 15, 2020. We analyzed the collected tweets using word frequencies of single (unigrams) and double words (bigrams). We leveraged latent Dirichlet allocation for topic modeling to identify topics discussed in the tweets. We also performed sentiment analysis and extracted the mean number of retweets, likes, and followers for each topic and calculated the interaction rate per topic. RESULTS: Out of approximately 2.8 million tweets included, 167,073 unique tweets from 160,829 unique users met the inclusion criteria. Our analysis identified 12 topics, which were grouped into four main themes: origin of the virus; its sources; its impact on people, countries, and the economy; and ways of mitigating the risk of infection. The mean sentiment was positive for 10 topics and negative for 2 topics (deaths caused by COVID-19 and increased racism). The mean for tweet topics of account followers ranged from 2722 (increased racism) to 13,413 (economic losses). The highest mean of likes for the tweets was 15.4 (economic loss), while the lowest was 3.94 (travel bans and warnings). CONCLUSIONS: Public health crisis response activities on the ground and online are becoming increasingly simultaneous and intertwined. Social media provides an opportunity to directly communicate health information to the public. Health systems should work on building national and international disease detection and surveillance systems through monitoring social media. There is also a need for a more proactive and agile public health presence on social media to combat the spread of fake news.

[Mid-term results in the treatment of osteochondritis dissecans of the femoral condyles using osteochondral grafting (mosaic arthroplasty)]. / Résultats à moyen terme du traitement des ostéochondrites disséquantes des condyles fémoraux par greffe ostéochondrale en mosaïque.

Several surgical procedures, including mosaic arthroplasty, can be used to treat patients with cartilage loss in the femoral condyles. This study aims to assess mid-term clinical and radiological results as well as the main prognostics elements. We conducted a retrospective epidemiological study over a period of 15 years. During the study period we collected data from 35 workable medical records of patients with osteochondritis dissecans of the femoral condyles treated by mosaic arthroplasty, with an average follow-up of 24 months. The level of complaints as well as preoperative knee function were evaluated and compared with the healthy knee according to the International Cartilage Repair Society (ICRS) score, the International Knee Documentation Committee (IKDC) score and visual analogue scale (VAS). It was less than 60% in 27 patients. During the follow-up period, the results were analyzed according to Hughston's functional and radiological criteria. After an average follow-up of 24 months, algoneurodystrophy was reported in 5 cases with a single case of haemarthrosis. A net ICRS score improvement was observed with a mean increasing from 54% to 74% on the follow-up visit. Most of patients were satisfied or very satisfied (82.9%). The elements of good prognosis recognized in our study included: a mean time between symptom onset and surgery of less of 18 months, having deep lesions with a diameter less than 02 cm and having lesions in the internal condyle. The treatment of cartilage loss is necessarily based on the correction of its direct and indirect causes namely the morphotype, the laxity and meniscus capital. No consensus in the decision-making was reached and no one could confirm the superiority of a technique in relation to the other but we can say that cartilage defect which sizes from 2 to 4 cm² may be the best indication for mosaic arthroplasty.

[Neurogenic paraosteopathy of the elbow: results of surgical arthrolysis (a retrospective study of 37 cases)]. / La para-ostéopathie neurogène du coude: résultat de l'arthrolyse chirurgicale (étude rétrospective de 37 cas).

Neurogenic paraosteoarthropathies are ectopic ossifications which develop near the joints. They are a process of neo-ectopic osteogenesis occurring after central or peripheral neurological lesions, in some types of comas (oxygen carbon intoxication, prolonged sedation) and following peripheral traumas including burns. They inolve almost exclusively the large proximal joints of the limbs. Elbow is the second area of involvment. The purpose of our study was to analyze the results of surgical arthrolysis in 37 patients with elbow stiffness due to neurogenic osteoarthropathy of the elbow. We conducted a retrospective study of 35 patients and 37 elbows over a 25-year period. Preoperative assessment included clinical and radiological examination. Since 2003 the patients had undergone systematic elbow arthroscopy. The gold standard surgical treatment was arthrolysis. All patients underwent functional rehabilitation protocol. Outcomes were analyzed after a mean 5-year follow-up period (6 months - 10 years). Neurogenic paraosteoarthropathy was caused by head injury with coma in 58.8% of cases. Preoperative assessment showed bending stiffness in the majority of cases (88%), severe or very severe in 64.7% of cases. Intraoperatively functional elbow range of motion from -30° to 130° was obtained in 61.7% of cases and in 41% of cases in the long term. Ulnar nerve liberation was satisfactory in 92% of cases. No postoperative instability of the elbow was reported. Two patients with definitive neurological lesions had osteoma recurrence. The results were equivalent regardless surgical delay. Surgical arthrolysis is an effective treatment for neurogenic osteomas of the elbow.

[Turrett's exostoses: about 35 casesk]. / Les exostoses de Turrett's: à propos de 35 cas.

We conducted a retrospective study of 35 patients with subungual exostosis of the hallux, also known as Turrett's exostosis, in the Department of Orthopedics and Traumatology at the Senior Military Hospital of Instruction of Tunis over the period between 1995 and 2015. We here summarize the outcomes of patients treated for this disease. The average age of patients was 29 years, with a sex ratio of 1.7. The median consultation time was six months. This delay in consultation was caused by a diagnostic error due to clinical picture resemblance with ingrown nail. Diagnosis was always confirmed by frontal and profile X-ray of the involved hallux. Treatment was based on total resection of the exostosis either through large ungual window or by latero-ungual approach. Anatomo-pathological examination was performed systematically. It allowed to confirm the benignity of the disease in all cases. All patients recovered and returned to their previous activity, on average, in 2 months. No patient had a recurrence.

Simple statistical channel model for weak temperature-induced turbulence in underwater wireless optical communication systems.

In this Letter, we use laser beam intensity fluctuation measurements to model and describe the statistical properties of weak temperature-induced turbulence in underwater wireless optical communication (UWOC) channels. UWOC channels with temperature gradients are modeled by the generalized gamma distribution (GGD) with an excellent goodness of fit to the measured data under all channel conditions. Meanwhile, thermally uniform channels are perfectly described by the simple gamma distribution which is a special case of GGD. To the best of our knowledge, this is the first model that comprehensively describes both thermally uniform and gradient-based UWOC channels.

[Results of the deltoid flap for surgery of rotator cuff tears]. / Resultats du lambeau deltoidien dans la chirurgie des ruptures de la coiffe des rotateurs.

AIM: Was to evaluate the results of deltoid flap for the treatment of massive rotator cuff tears. METHODS: This retrospective study included 20 shoulders in 20 patients with a painful massive, irreparable rotator cuff tears. The average patient age was 54 years. They were all treated with acromioplasty and a deltoid flap according to Augereau technique. Follow-up averaged 6 years 3 months (range 18 months - 11 years 4 months). Clinical and radiologic evaluations were done before and after surgery. Function was evaluated according to Constant's score. RESULTS: The Constant's score increased from 24.6/100 to 54.45/100. Eighty five per cent of patients were satisfied. Results on pain were good, with an average of Constant's score of 11/15. However, improvement strength or motion was not significative, with an average of 7.1/25 and 22.8/40 respectively. CONCLUSION: This study concludes that, this technique is a simple surgical procedure and their results on pain were good. Therefore, we conceder it is an excellent indication for the treatment of massive rotator cuff tears in adults after medical treatment failure.

[Stress fracture locations in military personnel]. / Les fractures de fatigue en milieu militaire. A propos de 16 localisations.

BACKGROUND: Physical exercise has beneficial effects on a number of physiologic systems, including the skeleton. However, combined with potential risk factors, unwise training practises may harm theses systems. A stress fracture represents one from of breakdown in the skeletal system. AIM: Assess the usefulness of MRI and kinesitherapy. METHODS: 14 patients in military circles had a stress facture. Most of the fractures are located at the upper metaphysis of the tibia. RESULTS: The radiography was poor, the MRI was the investigation of choice. The images show a FREDERICSON grade 4 lesion. Treatment is mainly non or partially weight bearing until consolidation. CONCLUSION: Although stress fracture results from unbalance between the bone and the forces applied, the injury occurs as a result of summation of various extrinsic and intrinsic factors at a given point in time.