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General General

ARTIFICIAL INTELLIGENCE MODELS SHOW POTENTIAL IN RECOGNIZING THE DENTAL IMPLANT TYPE, PREDICTING IMPLANT SUCCESS, AND OPTIMIZING IMPLANT DESIGN.

In The journal of evidence-based dental practice

ARTICLE TITLE AND BIBLIOGRAPHIC INFORMATION : Revilla-León M, Gómez-Polo M, Vyas S, Barmak BA, Galluci GO,Att W, Krishnamurthy VR. J. Artificial intelligence applications in implant dentistry: A systematic review. J Prosthet Dent 2021:(21);S0022-3913.

SOURCE OF FUNDING : Note reported.

TYPE OF STUDY/DESIGN : Systematic review.

Alqutaibi Ahmed Yaseen

2023-Mar

AI, Artificial intelligence, Dental implant, Implant design, Implant success, Machine learning

General General

Risk evaluation of carbapenem-induced liver injury based on machine learning analysis.

In Journal of infection and chemotherapy : official journal of the Japan Society of Chemotherapy

INTRODUCTION : Information regarding carbapenem-induced liver injury is limited, and the rate of liver injury caused by meropenem (MEPM) and doripenem (DRPM) remains unknown. Decision tree (DT) analysis, a machine learning method, has a flowchart-like model where users can easily predict the risk of liver injury. Thus, we aimed to compare the rate of liver injury between MEPM and DRPM and construct a flowchart that can be used to predict carbapenem-induced liver injury.

METHODS : We investigated patients treated with MEPM (n = 310) or DRPM (n = 320) and confirmed liver injury as the primary outcome. We used a chi-square automatic interaction detection algorithm to construct DT models. The dependent variable was set as liver injury from a carbapenem (MEPM or DRPM), and factors including alanine aminotransferase (ALT), albumin-bilirubin (ALBI) score, and concomitant use of acetaminophen were used as explanatory variables.

RESULTS : The rates of liver injury were 22.9% (71/310) and 17.5% (56/320) in the MEPM and DRPM groups, respectively; no significant differences in the rate were observed (95% confidence interval: 0.710-1.017). Although the DT model of MEPM could not be constructed, DT analysis showed that the incidence of introducing DRPM in patients with ALT >22 IU/L and ALBI scores > -1.87 might be high-risk.

CONCLUSIONS : The risk of developing liver injury did not differ significantly between the MEPM and DRPM groups. Since ALT and ALBI score are evaluated in clinical settings, this DT model is convenient and potentially useful for medical staff in assessing liver injury before DRPM administration.

Asai Yuki, Ooi Hayahide, Sato Yoshiharu

2023-Mar-11

Carbapenem, Decision tree analysis, Doripenem, Flowchart, Machine learning, Meropenem

General General

External Validation of an Extreme Gradient Boosting Model for Prediction of Delayed Cerebral Ischemia after Aneurysmal Subarachnoid Hemorrhage.

In World neurosurgery ; h5-index 47.0

BACKGROUND : Delayed cerebral ischemia (DCI) may significantly worsen the functional status of patients with aneurysmal subarachnoid hemorrhage (aSAH). Several authors have designed predictive models for early identification of patients at risk of post-aSAH DCI. In this study, we externally validate an extreme gradient boosting (EGB) forecasting model for post-aSAH DCI prediction.

METHODS : A 9-year institutional retrospective review of patients with aSAH was performed. Patients were included if they underwent surgical or endovascular treatment and had available follow-up data. DCI was diagnosed as new onset neurological deficits at 4-12 days after aneurysm rupture, defined as worsening GCS for ≥2 points, and new ischemic infarcts at imaging.

RESULTS : 267 patients with aSAH were collected. At admission, median Hunt-Hess score was 2 (range, 1-5), median Fisher score 3 (range, 1-4), and median modified Fisher score 3 (range, 1-4). 145 patients underwent EVD placement for hydrocephalus (54.3%). The ruptured aneurysms were treated with clipping (64%), coiling (34.8%), and stent-assisted coiling (1.1%). 58 patients (21.7%) were diagnosed with clinical DCI and 82 (30.7%) with asymptomatic imaging vasospasm. The EGB classifier correctly predicted 19 cases of DCI (7.1%) and 154 cases of no-DCI (57.7%), achieving sensitivity of 32.76% and specificity of 73.68%. The calculated F1-score and accuracy were 0.288 and 64.8%, respectively.

CONCLUSION : We validated the EGB model has a potential assistant tool to predict post-aSAH DCI in clinical practice, finding moderate-high specificity but low sensitivity. Future research should investigate the underlying pathophysiology of DCI to allow the development of high-performing forecasting models.

Palmisciano Paolo, Hoz Samer S, Johnson Mark D, Forbes Jonathan A, Prestigiacomo Charles J, Zuccarello Mario, Andaluz Norberto

2023-Mar-11

Aneurysmal subarachnoid hemorrhage, Delayed cerebral ischemia, Machine learning, Predictive Analysis, Vasospasm

Dermatology Dermatology

The predictors of death within 1 year in acute ischemic stroke patients based on machine learning.

In Frontiers in neurology

OBJECTIVE : To explore the predictors of death in acute ischemic stroke (AIS) patients within 1 year based on machine learning (ML) algorithms.

METHODS : This study retrospectively analyzed the clinical data of patients hospitalized and diagnosed with AIS in the Second Affiliated Hospital of Xuzhou Medical University between August 2017 and July 2019. The patients were randomly divided into training and validation sets at a ratio of 7:3, and the clinical characteristic variables of the patients were screened using univariate and multivariate logistics regression. Six ML algorithms, including logistic regression (LR), gradient boosting machine (GBM), extreme gradient boosting (XGB), random forest (RF), decision tree (DT), and naive Bayes classifier (NBC), were applied to develop models to predict death in AIS patients within 1 year. During training, a 10-fold cross-validation approach was used to validate the training set internally, and the models were interpreted using important ranking and the SHapley Additive exPlanations (SHAP) principle. The validation set was used to externally validate the models. Ultimately, the highest-performing model was selected to build a web-based calculator.

RESULTS : Multivariate logistic regression analysis revealed that C-reactive protein (CRP), homocysteine (HCY) levels, stroke severity (SS), and the number of stroke lesions (NOS) were independent risk factors for death within 1 year in patients with AIS. The area under the curve value of the XGB model was 0.846, which was the highest among the six ML algorithms. Therefore, we built an ML network calculator (https://mlmedicine-de-stroke-de-stroke-m5pijk.streamlitapp.com/) based on XGB to predict death in AIS patients within 1 year.

CONCLUSIONS : The network calculator based on the XGB model developed in this study can help clinicians make more personalized and rational clinical decisions.

Wang Kai, Gu Longyuan, Liu Wencai, Xu Chan, Yin Chengliang, Liu Haiyan, Rong Liangqun, Li Wenle, Wei Xiu’e

2023

biomarkers, ischemic stroke, machine learning, prediction model, web calculator

Public Health Public Health

Quantitative Morphometry and Machine Learning Model to Explore Duodenal and Rectal Mucosal Tissue of Children with Environmental Enteric Dysfunction.

In The American journal of tropical medicine and hygiene

Environmental enteric dysfunction (EED) is a subclinical enteropathy prevalent in resource-limited settings, hypothesized to be a consequence of chronic exposure to environmental enteropathogens, resulting in malnutrition, growth failure, neurocognitive delays, and oral vaccine failure. This study explored the duodenal and colonic tissues of children with EED, celiac disease, and other enteropathies using quantitative mucosal morphometry, histopathologic scoring indices, and machine learning-based image analysis from archival and prospective cohorts of children from Pakistan and the United States. We observed villus blunting as being more prominent in celiac disease than in EED, as shorter lengths of villi were observed in patients with celiac disease from Pakistan than in those from the United States, with median (interquartile range) lengths of 81 (73, 127) µm and 209 (188, 266) µm, respectively. Additionally, per the Marsh scoring method, celiac disease histologic severity was increased in the cohorts from Pakistan. Goblet cell depletion and increased intraepithelial lymphocytes were features of EED and celiac disease. Interestingly, the rectal tissue from cases with EED showed increased mononuclear inflammatory cells and intraepithelial lymphocytes in the crypts compared with controls. Increased neutrophils in the rectal crypt epithelium were also significantly associated with increased EED histologic severity scores in duodenal tissue. We observed an overlap between diseased and healthy duodenal tissue upon leveraging machine learning image analysis. We conclude that EED comprises a spectrum of inflammation in the duodenum, as previously described, and the rectal mucosa, warranting the examination of both anatomic regions in our efforts to understand and manage EED.

Khan Marium, Jamil Zehra, Ehsan Lubaina, Zulqarnain Fatima, Srivastava Sanjana, Siddiqui Saman, Fernandes Philip, Raghib Muhammad, Sengupta Saurav, Mujahid Zia, Ahmed Zubair, Idrees Romana, Ahmed Sheraz, Umrani Fayaz, Iqbal Najeeha, Moskaluk Christopher, Raghavan Shyam, Cheng Lin, Moore Sean, Ali Syed Asad, Iqbal Junaid, Syed Sana

2023-Mar-13

General General

Automatic segmentation of the choroid plexuses: Method and validation in controls and patients with multiple sclerosis.

In NeuroImage. Clinical

Choroid Plexuses (ChP) are structures located in the ventricles that produce the cerebrospinal fluid (CSF) in the central nervous system. They are also a key component of the blood-CSF barrier. Recent studies have described clinically relevant ChP volumetric changes in several neurological diseases including Alzheimer's, Parkinson's disease, and multiple sclerosis (MS). Therefore, a reliable and automated tool for ChP segmentation on images derived from magnetic resonance imaging (MRI) is a crucial need for large studies attempting to elucidate their role in neurological disorders. Here, we propose a novel automatic method for ChP segmentation in large imaging datasets. The approach is based on a 2-step 3D U-Net to keep preprocessing steps to a minimum for ease of use and to lower memory requirements. The models are trained and validated on a first research cohort including people with MS and healthy subjects. A second validation is also performed on a cohort of pre-symptomatic MS patients having acquired MRIs in routine clinical practice. Our method reaches an average Dice coefficient of 0.72 ± 0.01 with the ground truth and a volume correlation of 0.86 on the first cohort while outperforming FreeSurfer and FastSurfer-based ChP segmentations. On the dataset originating from clinical practice, the method reaches a Dice coefficient of 0.67 ± 0.01 (being close to the inter-rater agreement of 0.64 ± 0.02) and a volume correlation of 0.84. These results demonstrate that this is a suitable and robust method for the segmentation of the ChP both on research and clinical datasets.

Yazdan-Panah Arya, Schmidt-Mengin Marius, Ricigliano Vito A G, Soulier Théodore, Stankoff Bruno, Colliot Olivier

2023-Mar-06

Choroid plexus, Deep learning, Multiple sclerosis, Radiologically isolated syndrome, Segmentation