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Machine learning and artificial intelligence are generating significant attention in the scientific community and media. Such algorithms have great potential in medicine for personalizing and improving patient care, including in the diagnosis and management of heart failure. Many physicians are familiar with these terms and the excitement surrounding them, but many are unfamiliar with the basics of these algorithms and how they are applied to medicine. Within heart failure research, current applications of machine learning include creating new approaches to diagnosis, classifying patients into novel phenotypic groups, and improving prediction capabilities. In this paper, we provide an overview of machine learning targeted for the practicing clinician and evaluate current applications of machine learning in the diagnosis, classification, and prediction of heart failure.

Careful clinical evaluation differentiates epileptic seizures from syncope (vasovagal and cardiogenic), dissociative seizures (non-epileptic attack disorder) and other rarer causes of paroxysmal events. The initial diagnosis of epilepsy is incorrect in 20–30% of patients. Investigations can include blood tests, electrocardiograms, electroencephalograms and neuro-imaging. Patients should be informed and counselled about the diagnosis. Anti-epileptic drugs are the first-line treatment for epilepsy, the choice depending on the epilepsy syndrome, the seizure type, the patient and their co-morbidities. Surgical options are available for refractory epilepsy. Status epilepticus is a medical emergency and all clinicians should be aware of the treatment algorithm.

Our objective was to assess the generalizability, across sites and cognitive contexts, of schizophrenia classification based on functional brain connectivity. We tested different training-test scenarios combining fMRI data from 191 schizophrenia patients and 191 matched healthy controls obtained at 6 scanning sites and under different task conditions. Diagnosis classification accuracy generalized well to a novel site and cognitive context provided data from multiple sites were used for classifier training. By contrast, lower classification accuracy was achieved when data from a single distinct site was used for training. These findings indicate that it is beneficial to use multisite data to train fMRI-based classifiers intended for large-scale use in the clinical realm.

In-stent restenosis is a pervasive challenge to the durability of stenting for the treatment of lower extremity ischemia. There is considerable controversy about the criteria for diagnosis, indications for treatment, and preferred algorithm for addressing in-stent restenosis. This evidence summary seeks to review existing information on strategies for the treatment of this difficult problem.

Much recent debate has focused on the optimal classification of epithelial neuroendocrine tumors (NETs). Multiple different systems of terminology, grading, and staging have been proposed, and some systems combine elements of grade and stage into a single prognostic classification. Recently, national and international consensus groups have attempted to standardize the classification of NETs, especially for those arising in the gastrointestinal tract and pancreas. Furthermore, the recognition that common classification criteria (such as proliferative rate) span multiple different systems allows the basic data necessary to predict outcome and tailor therapy to be included in pathology reports, even though a single uniform system of terminology may remain elusive. Formal tumor-node-metastasis (TNM)-based staging systems also have been developed recently, and advances in the treatment of some NETs (pancreatic in particular) are pointing towards the need to assess therapeutic biomarkers in routine practice. This review will present the most widely used systems for classifying, grading, and staging NETs and will summarize the recommendations for the data to be included in standard pathology reports of these uncommon tumors.

SLAP (superior labrum, anterior and posterior) lesions have been identified as a cause of instability and pain in the shoulder. This review describes clinical features, mechanism of injury, physical examination, classification and associated lesions, normal and pathologic anatomy, as well as a treatment algorithm for SLAP lesions.

Cerebrovascular disease has been ranked the second or third of top 10 death causes in Taiwan and has caused about 13,000 people death every year since 1986. Once cerebrovascular disease occurs, it not only leads to huge cost of medical care, but even death. All developed countries in the world put cerebrovascular disease prevention and treatment in high priority, and invested considerable budget and human resource in long-term studies, in order to reduce the heavy burden. As the pathogenesis of cerebrovascular disease is complex and variable, it is hard to make accurate diagnosis in advance. However, in perspective of preventive medicine, it is necessary to build a predictive model to enhance the accurate diagnosis of cerebrovascular disease. Therefore, coupled with the 2007 cerebrovascular disease prevention and treatment program of a regional teaching hospital in Taiwan, this study aimed to apply the classification technology to construct an optimum cerebrovascular disease predictive model. From this predictive model, cerebrovascular disease classification rules were extracted and used to improve the diagnosis and prediction of cerebrovascular disease. This study acquired 493 valid samples from this cerebrovascular disease prevention and treatment program, and adopted three classification algorithms, decision tree, Bayesian classifier and back propagation neural network, to construct classification models, respectively. After analyzing and comparing classification efficiencies - sensitivity and accuracy, the decision tree constructed model was chosen as the optimum predictive model for cerebrovascular disease. In this model, the sensitivity and accuracy were 99.48% and 99.59%, respectively, and eight important influence factors of predicting cerebrovascular disease and 16 diagnosis classification rules were extracted. Five experienced cerebrovascular doctors assessed these rules, and confirmed them to be useful to the current clinical medical condition.

Hallux rigidus is a degenerative and progressive disease of the metatarsal phalangeal joint of the hallux, with its main symptoms being pain and loss of joint movement. Different aetiological mechanisms are involved in its development, and clinical anamnesis and radiological examinations are essential for an accurate diagnosis and to be able to establish the correct stage of progression, with the aim of giving suitable treatment in each case. Surgical treatment is indicated in most cases and is necessary to know all the surgical techniques to make the right treatment for each patient.

Proximal hamstring tendon injuries are rare and potentially disabling injuries. Tendon avulsion occurs because of eccentric overload of the tendon insertion. Diagnosis is based on the typical history, careful examination, and magnetic resonance imaging. Recent classification offers the treating surgeon guidelines for repair based on the pathologic anatomy of the injured tendons. This article describes the surgical treatment in detail and reviews the current literature regarding results of treatment.

The 2001 revised criteria for the diagnosis, classification, and staging of idiopathic osteonecrosis of the femoral head were proposed in June 2001, by the working group of the Specific Disease Investigation Committee under the auspices of the Japanese Ministry of Health, Labor and Welfare, to establish criteria for diagnosis and management of idiopathic osteonecrosis of the femoral head. Five criteria that showed high specificity were selected for diagnosis: collapse of the femoral head (including crescent sign) without joint-space narrowing or acetabular abnormality on x-ray images; demarcating sclerosis in the femoral head without joint-space narrowing or acetabular abnormality; "cold in hot" on bone scans; low-intensity band on T1-weighted MRI (bandlike pattern); and trabecular and marrow necrosis on histology. Idiopathic osteonecrosis of the femoral head is diagnosed if the patient fulfills two of these five criteria and does not have bone tumors or dysplasias. Necrotic lesions are classified into four types, based on their location on T1-weighted images or x-ray images. Type A lesions occupy the medial one-third or less of the weight-bearing portion. Type B lesions occupy the medial two-thirds or less of the weight-bearing portion. Type C1 lesions occupy more than the medial two-thirds of the weight-bearing portion but do not extend laterally to the acetabular edge. Type C2 lesions occupy more than the medial two-thirds of the weight-bearing portion and extend laterally to the acetabular edge. Staging is based on anteroposterior and lateral views of the femoral head on x-ray images. Stage 1 is defined as the period when there are no specific findings of osteonecrosis on x-ray images, although specific findings are observed on MRI, bone scintigram, or histology. Stage 2 is the period when demarcating sclerosis is observed without collapse of the femoral head. Stage 3 is the period when collapse of the femoral head, including crescent sign, is observed without joint-space narrowing. Mild osteophyte formation in the femoral head or acetabulum may be observed in stage 3. Stage 3 is divided into two substages. In stage 3A, collapse of the femoral head is less than 3 mm. In stage 3B, collapse of the femoral head is 3 mm or greater. Stage 4 is the period when osteoarthritic changes are observed.

Diabetes mellitus (DM) is a metabolic disorder characterized by the presence of chronic hyperglycemia accompanied by greater or lesser impairment in the metabolism of carbohydrates, lipids and proteins. The origin and etiology of DM can vary greatly but always include defects in either insulin secretion or response or in both at some point in the course of disease. When characteristic symptoms of DM are clearly present and blood glucose levels are high enough, the diagnosis is usually unequivocal. However, it is important to remember that the diagnosis is made in asymptomatic patients in most cases, based on the results of routine tests. The prevalence of DM, its specific complications and the presence of other diseases that often accompany DM make this disease one of today’s main social and public health problems. The great increase in information available on the etiology and pathophysiology of DM and its chronic complications has led necessarily to the revision of diagnostic criteria and reclassification of the processes involved. Revised diagnostic criteria and classifications were agreed upon in 1997 and 1998 by the American Diabetes Association and the World Health Organization, respectively, and new recommendations were published. Thanks to cross-representation on the committees, the conclusions and final recommendations are, in general, very similar, although a few minor differences are present. Clarification of diagnostic criteria and better classification of patients suffering from DM should allow us to make better choices among the various treatment options available and to improve prognosis.

C-shaped canals are mostly found in mandibular second molars. Studies have shown that frequency ranges from 2.7 to 31%. This study was undertaken to provide further knowledge of the distribution of such canal anatomy, by determining the frequency of C-shaped canals in a sample representing the Lebanese population. Ninety-four mandibular second molars with scheduled endodontic treatment were examined over a 1-yr period. Of the 94 treated molars, 18 exhibited C-shaped canals that would represent 19.1% of the sample. The geographical position of Lebanon could represent an explanation for this phenomenon.