NMD-12: A New Machine-Learning Derived Screening Instrument to Detect Mild Cognitive Impairment and Dementia

Introduction Using machine learning techniques, we developed a brief questionnaire to aid neurologists and neuropsychologists in the screening of mild cognitive impairment (MCI) and dementia. Methods With the reduction of the survey size as a goal of this research, feature selection based on information gain was performed to rank the contribution of the 45 items corresponding to patient responses to the specified questions. The most important items were used to build the optimal screening model based on the accuracy, practicality, and interpretability. The diagnostic accuracy for discriminating normal cognition (NC), MCI, very mild dementia (VMD) and dementia was validated in the test group. Results The screening model (NMD-12) was constructed with the 12 items that were ranked the highest in feature selection. The receiver-operator characteristic (ROC) analysis showed that the area under the curve (AUC) in the test group was 0.94 for discriminating NC vs. MCI, 0.88 for MCI vs. VMD, 0.97 for MCI vs. dementia, and 0.96 for VMD vs. dementia, respectively. Discussion The NMD-12 model has been developed and validated in this study. It provides healthcare professionals with a simple and practical screening tool which accurately differentiates NC, MCI, VMD, and dementia

Arrhythmia and other modifiable risk factors in incident dementia and MCI among elderly individuals with low educational levels in Taiwan

IntroductionThere is increasing evidence that arrhythmia is a risk factor for dementia; however, it appears that arrhythmia affects the cognitive function of individuals differentially across age groups, races, and educational levels. Demographic differences including educational level have also been found to moderate the effects of modifiable risk factors for cognitive decline.MethodsThis study recruited 1,361 individuals including a group of cognitively unimpaired (CU) individuals, a group of patients with mild cognitive impairment (MCI), and a group of patients with dementia with low education levels. The participants were evaluated in terms of modifiable risk factors for dementia, including arrhythmia and neuropsychiatric symptoms.ResultsCox proportional hazard regression models revealed that among older MCI patients (>75 years), those with arrhythmia faced an elevated risk of dementia. Among younger MCI patients, those taking anti-hypertensive drugs faced a relatively low risk of dementia. Among younger MCI patients, male sex and higher educational level were associated with an elevated risk of dementia. Among CU individuals, those with coronary heart disease and taking anti-lipid compounds faced an elevated risk of MCI and those with symptoms of depression faced an elevated risk of dementia.DiscussionThe risk and protective factors mentioned above could potentially be used as markers in predicting the onset of dementia in clinical settings, especially for individuals with low educational levels

Machine Learning for the Preliminary Diagnosis of Dementia

Objective: The reliable diagnosis remains a challenging issue in the early stages of dementia. We aimed to develop and validate a new method based on machine learning to help the preliminary diagnosis of normal, mild cognitive impairment (MCI), very mild dementia (VMD), and dementia using an informant-based questionnaire. Methods: We enrolled 5,272 individuals who filled out a 37-item questionnaire. In order to select the most important features, three different techniques of feature selection were tested. Then, the top features combined with six classification algorithms were used to develop the diagnostic models. Results: Information Gain was the most effective among the three feature selection methods. The Naive Bayes algorithm performed the best (accuracy = 0.81, precision = 0.82, recall = 0.81, and F-measure = 0.81) among the six classification models. Conclusion: The diagnostic model proposed in this paper provides a powerful tool for clinicians to diagnose the early stages of dementia

Machine learning for the preliminary diagnosis of dementia

Objective. The reliable diagnosis remains a challenging issue in the early stages of dementia. We aimed to develop and validate a new method based on machine learning to help the preliminary diagnosis of normal, mild cognitive impairment (MCI), very mild dementia (VMD), and dementia using an informant-based questionnaire. Methods. We enrolled 5,272 individuals who filled out a 37-item questionnaire. In order to select the most important features, three different techniques of feature selection were tested. Then, the top features combined with six classification algorithms were used to develop the diagnostic models. Results. Information Gain was the most effective among the three feature selection methods. The Naive Bayes algorithm performed the best (accuracy = 0.81, precision = 0.82, recall = 0.81, and F-measure = 0.81) among the six classification models. Conclusion. The diagnostic model proposed in this paper provides a powerful tool for clinicians to diagnose the early stages of dementia

Analyze informant-based questionnaire for the early diagnosis of senile dementia using deep learning

OBJECTIVE: This paper proposes a multiclass deep learning method for the classification of dementia using an informant-based questionnaire. METHODS: A deep neural network classification model based on Keras framework is proposed in this paper. To evaluate the advantages of our proposed method, we compared the performance of our model with industry-standard machine learning approaches. We enrolled 6,701 individuals, which were randomly divided into training data sets (6030 participants) and test data sets (671 participants). We evaluated each diagnostic model in the test set using accuracy, precision, recall, and F1-Score. RESULTS: Compared with the seven conventional machine learning algorithms, the DNN showed higher stability and achieved the best accuracy with 0.88, which also showed good results for identifying normal (F1-score = 0.88), mild cognitive impairment (MCI) (F1-score = 0.87), very mild dementia (VMD) (F1-score = 0.77) and Severe dementia (F1-score = 0.94). CONCLUSION: The deep neural network (DNN) classification model can effectively help doctors accurately screen patients who have normal cognitive function, mild cognitive impairment (MCI), very mild dementia (VMD), mild dementia (Mild), moderate dementia (Moderate), and severe dementia (Severe)

Generative adversarial network-based attenuation correction for 99mTc-TRODAT-1 brain SPECT

BackgroundAttenuation correction (AC) is an important correction method to improve the quantification accuracy of dopamine transporter (DAT) single photon emission computed tomography (SPECT). Chang's method was developed for AC (Chang-AC) when CT-based AC was not available, assuming uniform attenuation coefficients inside the body contour. This study aims to evaluate Chang-AC and different deep learning (DL)-based AC approaches on 99mTc-TRODAT-1 brain SPECT using clinical patient data on two different scanners.MethodsTwo hundred and sixty patients who underwent 99mTc-TRODAT-1 SPECT/CT scans from two different scanners (scanner A and scanner B) were retrospectively recruited. The ordered-subset expectation-maximization (OS-EM) method reconstructed 120 projections with dual-energy scatter correction, with or without CT-AC. We implemented a 3D conditional generative adversarial network (cGAN) for the indirect deep learning-based attenuation correction (DL-ACμ) and direct deep learning-based attenuation correction (DL-AC) methods, estimating attenuation maps (μ-maps) and attenuation-corrected SPECT images from non-attenuation-corrected (NAC) SPECT, respectively. We further applied cross-scanner training (cross-scanner indirect deep learning-based attenuation correction [cull-ACμ] and cross-scanner direct deep learning-based attenuation correction [call-AC]) and merged the datasets from two scanners for ensemble training (ensemble indirect deep learning-based attenuation correction [eDL-ACμ] and ensemble direct deep learning-based attenuation correction [eDL-AC]). The estimated μ-maps from (c/e)DL-ACμ were then used in reconstruction for AC purposes. Chang's method was also implemented for comparison. Normalized mean square error (NMSE), structural similarity index (SSIM), specific uptake ratio (SUR), and asymmetry index (%ASI) of the striatum were calculated for different AC methods.ResultsThe NMSE for Chang's method, DL-ACμ, DL-AC, cDL-ACμ, cDL-AC, eDL-ACμ, and eDL-AC is 0.0406 ± 0.0445, 0.0059 ± 0.0035, 0.0099 ± 0.0066, 0.0253 ± 0.0102, 0.0369 ± 0.0124, 0.0098 ± 0.0035, and 0.0162 ± 0.0118 for scanner A and 0.0579 ± 0.0146, 0.0055 ± 0.0034, 0.0063 ± 0.0028, 0.0235 ± 0.0085, 0.0349 ± 0.0086, 0.0115 ± 0.0062, and 0.0117 ± 0.0038 for scanner B, respectively. The SUR and %ASI results for DL-ACμ are closer to CT-AC, Followed by DL-AC, eDL-ACμ, cDL-ACμ, cDL-AC, eDL-AC, Chang's method, and NAC.ConclusionAll DL-based AC methods are superior to Chang-AC. DL-ACμ is superior to DL-AC. Scanner-specific training is superior to cross-scanner and ensemble training. DL-based AC methods are feasible and robust for 99mTc-TRODAT-1 brain SPECT

Use of Ceftriaxone in Treating Cognitive and Neuronal Deficits Associated With Dementia With Lewy Bodies

Dementia with Lewy bodies (DLB) is caused by accumulation of Lewy bodies, destruction of mitochondria, and excess of glutamate in synapses, which eventually leads to excitotoxicity, neurodegeneration, and cognitive impairments. Ceftriaxone (CEF) reduces excitotoxicity by increasing glutamate transporter 1 expression and glutamate reuptake. We investigated whether CEF can prevent cognitive decline and neurological deficits and increase neurogenesis in DLB rats. Male Wistar rats infused with viral vector containing human alpha-synuclein (α-syn) gene, SNCA, in the lateral ventricle were used as a rat model of DLB. CEF (100 mg/kg/day, i.p.) was injected in these rats for 27 days. The active avoidance test and object recognition test was performed. Finally, the brains of all the rats were immunohistochemically stained to measure α-syn, neuronal density, and newborn cells in the hippocampus and substantia nigra. The results revealed that DLB rats had learning and object recognition impairments and exhibited cell loss in the nigrostriatal dopaminergic system, and hippocampal CA1, and dentate gyrus (DG). Additionally, DLB rats had fewer newborn cells in the DG and substantia nigra pars reticulata and more α-syn immune-positive cells in the DG. Treatment with CEF improved cognitive function, reduced cell loss, and increased the number of newborn cells in the brain. To our knowledge, this is the first study showing that CEF prevents loss of neurogenesis in the brain of DLB rats. CEF may therefore has clinical potential for treating DLB

The Role of Age in Predicting the Outcome of Caustic Ingestion in Adults: A Retrospective Analysis

<p>Abstract</p> <p>Background</p> <p>Although the outcomes of caustic ingestion differ between children and adults, it is unclear whether such outcomes differ among adults as a function of their age. This retrospective study was performed to ascertain whether the clinical outcomes of caustic ingestion differ significantly between elderly and non-elderly adults.</p> <p>Methods</p> <p>Medical records of patients hospitalized for caustic ingestion between June 1999 and July 2009 were reviewed retrospectively. Three hundred eighty nine patients between the ages of 17 and 107 years were divided into two groups: non-elderly (< 65 years) and elderly (≥ 65 years). Mucosal damage was graded using esophagogastroduodenoscopy (EGD). Parameters examined in this study included gender, intent of ingestion, substance ingested, systemic and gastrointestinal complications, psychological and systemic comorbidities, severity of mucosal injury, and time to expiration.</p> <p>Results</p> <p>The incidence of psychological comorbidities was higher for the non-elderly group. By contrast, the incidence of systemic comorbidities, the grade of severity of mucosal damage, and the incidence of systemic complications were higher for the elderly group. The percentages of ICU admissions and deaths in the ICU were higher and the cumulative survival rate was lower for the elderly group. Elderly subjects, those with systemic complications had the greatest mortality risk due to caustic ingestion.</p> <p>Conclusions</p> <p>Caustic ingestion by subjects ≥65 years of age is associated with poorer clinical outcomes as compared to subjects < 65 years of age; elderly subjects with systemic complications have the poorest clinical outcomes. The severity of gastrointestinal tract injury appears to have no impact on the survival of elderly subjects.</p

Laparoscopy in management of appendicitis in high-, middle-, and low-income countries: a multicenter, prospective, cohort study.

BACKGROUND: Appendicitis is the most common abdominal surgical emergency worldwide. Differences between high- and low-income settings in the availability of laparoscopic appendectomy, alternative management choices, and outcomes are poorly described. The aim was to identify variation in surgical management and outcomes of appendicitis within low-, middle-, and high-Human Development Index (HDI) countries worldwide. METHODS: This is a multicenter, international prospective cohort study. Consecutive sampling of patients undergoing emergency appendectomy over 6 months was conducted. Follow-up lasted 30 days. RESULTS: 4546 patients from 52 countries underwent appendectomy (2499 high-, 1540 middle-, and 507 low-HDI groups). Surgical site infection (SSI) rates were higher in low-HDI (OR 2.57, 95% CI 1.33-4.99, p = 0.005) but not middle-HDI countries (OR 1.38, 95% CI 0.76-2.52, p = 0.291), compared with high-HDI countries after adjustment. A laparoscopic approach was common in high-HDI countries (1693/2499, 67.7%), but infrequent in low-HDI (41/507, 8.1%) and middle-HDI (132/1540, 8.6%) groups. After accounting for case-mix, laparoscopy was still associated with fewer overall complications (OR 0.55, 95% CI 0.42-0.71, p < 0.001) and SSIs (OR 0.22, 95% CI 0.14-0.33, p < 0.001). In propensity-score matched groups within low-/middle-HDI countries, laparoscopy was still associated with fewer overall complications (OR 0.23 95% CI 0.11-0.44) and SSI (OR 0.21 95% CI 0.09-0.45). CONCLUSION: A laparoscopic approach is associated with better outcomes and availability appears to differ by country HDI. Despite the profound clinical, operational, and financial barriers to its widespread introduction, laparoscopy could significantly improve outcomes for patients in low-resource environments. TRIAL REGISTRATION: NCT02179112