Early Planning Research Articles

Comparison of coronary CT angiography and invasive coronary angiography results

IntroductionCoronary artery disease (CAD) is a leading cause of death worldwide. Accurate diagnosis and management are critical. Non-invasive imaging, such as coronary computed tomography angiography (CCTA), is vital for early diagnosis and treatment planning. This study evaluates the accuracy of CAD-Reporting and Data System (CAD-RADS) scoring and the compatibility between CCTA and invasive coronary angiography (ICA) in patients suspected of having CAD.Materials and methodsFrom January 1, 2022 to January 15, 2024, 214 patients suspected of CAD underwent both CCTA and ICA. CCTA artifacts led to the exclusion of 32 patients and 128 vessels, leaving 586 vessels for analysis. CAD-RADS scoring categorized coronary stenosis. Diagnostic performance was measured by specificity, sensitivity, accuracy, positive and negative predictive value (NPV). Extracardiac findings were analyzed with a wide field of view (FOV) during CCTA.ResultsA total of 214 patients (67.3% male, median age 56) were examined. Hypertension, smoking, calcium score, and high-risk plaques correlated with CCTA and ICA CAD-RADS scores; calcium score also related to hypertension, smoking, diabetes, and dyslipidemia (p < 0.05). CCTA showed a sensitivity of 80.8% and NPV of 90.3% for detecting stenosis of 70% or more; for 50% stenosis, sensitivity was 93.5% and NPV 92.1%. Agreement between CCTA and ICA was excellent in bypass patients; stenosis detection in stented patients had 85.7% sensitivity and 96.2% NPV.ConclusionThis study highlights the importance of CAD-RADS and CCTA in CAD diagnosis and treatment planning. CCTA effectively evaluates stents and grafts, emphasizing the benefits of extracardiac findings and a wide FOV.

Medical Image Classification using a Many to Many Relation, Multilayered Fuzzy Systems and AI

One of the research gaps in the medical sciences is the study of orphan diseases or rare diseases, due to limited data availability of rare diseases. Our previous study addressed this successfully by developing an Artificial Intelligence (AI)-based medical image classification method using a multilayer fuzzy approach (MFA), for detecting and classifying image abnormalities for large and very small datasets. A fuzzy system is an AI system used to handle imprecise data. There are more than three types of fuzziness in any image data set: 1) due to a projection of a 3D object on a 2D surface, 2) due to the digitalization of the scan, and 3) conversion of the digital image to grayscale, and more. Thus, this was referred to in the previous study as a multilayer fuzzy system, since fuzziness arises from multiple sources. The method used in MFA involves comparing normal images containing abnormalities with the same kind of image without abnormalities, yielding a similarity measure percentage that, when subtracted from a hundred, reveals the abnormality. However, relying on a single standard image in the MFA reduces efficiency, since images vary in contrast, lighting, and patient demographics, impacting similarity percentages. To mitigate this, the current study focused on developing a more robust medical image classification method than MFA, using a many-to-many relation and a multilayer fuzzy approach (MCM) that employs multiple diverse standard images to compare with the abnormal image. For each abnormal image, the average similarity was calculated across multiple normal images, addressing issues encountered with MFA, and enhancing versatility. In this study, an AI-based method of image analysis automation that utilizes fuzzy systems was applied to a cancer data set for the first time. MCM proved to be highly efficient in detecting the abnormality in all types of images and sample sizes and surpassed the gold standard, the convolutional neural network (CNN), in detecting the abnormality in images from a very small data set. Moreover, MCM detects and classifies abnormality without any training, validation, or testing steps for large and small data sets. Hence, MCM may be used to address one of the research gaps in medicine, which detects, quantifies, and classifies images related to rare diseases with small data sets. This has the potential to assist a physician with early detection, diagnosis, monitoring, and treatment planning of several diseases, especially rare diseases.

End of life and palliative care decisions in advanced head neck cancer

The complexity of care for patients with advanced head and neck cancer is a significant issue, and current healthcare systems must focus on appropriate referral criteria, increased integration and coordination of care, and robust evaluation of specific care components. Linkage between research and service design delivery across teams, disciplines, and care settings is also important. Palliative care needs of patients with advanced head and neck cancer pose unique complexities due to the impact the illness has on eating, speaking, appearance, and breathing. Specialty palliative care consultation by trained experts helps primary teams facilitate goals of care discussions, symptom management, and medical resource navigation for the terminally ill patient. Early initiation of palliative care results in less invasive and more cost-effective patient-centered care. There is little literature on care practices in this population, and hospital-based care may increase towards the end of life to address potentially reversible conditions or uncertain prognosis. Early advanced care planning and end-of-life care options should be supported alongside continuation of therapy lines.

Frequency and involvement of lymph nodes in squamous cell carcinoma of oral cavity in cross sectional population.

Objective: To find out the frequent site of squamous cell carcinoma of oral cavity and pattern of neck node involvement in cross sectional population. Study Design: Prospective Study. Setting: Department of Otorhinolaryngology Head and Neck Surgery, Abbasi Shaheed Hospital Karachi and Karachi Medical and Dental College. Period: 01-06-2023 to 31-12-2023. Methods: Patients who biopsy proven squamous cell carcinoma of oral cavity visited ENT department Abbasi Shaheed Hospital were include in this study. Proforma was made in which detail history and examination were noted. Oral cavity examined to see the size and site of tumor. Neck was palpated to see palpable cervical lymph node. CT scan head and neck with contrast was done. Those patients who fulfilled the inclusion and exclusion criteria included in this. Result: 56 patients were selected in this study who fulfill the inclusion criteria. Male were 37 (66%). Maximum number of patients were between the 41-50 years of age, 24 patients (42.8%). Most common site of carcinoma was buccal mucosa 27 (48.2%). Highest number of patients presented with T3 stage 22 patients (39.2%). The frequency of lymph node involvement noted in 35 (62.5%) patients while 21 (37.5%) came with no lymph node. Level I was the most frequent level involved, it was involved in 28 patients (50%). Conclusion: Oral squamous cell carcinoma usually reported late. Early referral to otorhinolaryngologist and educate patient to consult Otorhinolaryngologist as earliest for early diagnosis and appropriate treatment plan.

Extended nnU-Net for Brain Metastasis Detection and Segmentation in Contrast-Enhanced Magnetic Resonance Imaging With a Large Multi-Institutional Data Set

PurposeTo investigate an extended self-adapting nnU-Net framework for detecting and segmenting brain metastases (BM) on MRI. ApproachSix different nnU-Net systems with adaptive data sampling, adaptive Dice loss (ADL) or different patch/batch sizes were trained and tested for detecting and segmenting intraparenchymal BM with a size ≥ 2 mm on 3D post-Gd T1-weighted MRI volumes using 2092 patients from seven institutions (1712, 195 and 185 patients for training, validation and testing, respectively). Gross tumor volumes (GTVs) of BM delineated by physicians for stereotactic radiosurgery (SRS) were collected retrospectively and curated at each institute. Additional centralized data curation was carried out to create GTVs of uncontoured BM by two radiologists to improve accuracy of ground truth. The training dataset was augmented with synthetic BMs of 1025 MRI volumes using a 3D generative pipeline. BM detection was evaluated by lesion-level sensitivity and false-positive (FP) rate. BM segmentation was assessed by lesion-level Dice similarity coefficient (DSC), 95-percentile Hausdorff distance (HD95) and average HD. The performances were assessed across different BM sizes. Additional testing was performed using a second dataset of 206 patients. ResultsOf the six nnU-Net systems, the nnU-Net with ADL achieved the best detection and segmentation performance on the first testing dataset. At an FP rate of 0.65±1.17, overall sensitivity was 0.904 for all sizes of BM, 0.966 for BM ≥ 0.1 cm3 and 0.824 for BM < 0.1 cm3. Mean values of DSC, HD95 and average HD of all detected BMs were 0.758, 1.45 mm and 0.23 mm, respectively. Performances on the second testing dataset achieved sensitivity of 0.907 at an FP rate of 0.57±0.85 for all BM sizes, and average HD of 0.33 mm for all detected BM. ConclusionsOur proposed extension of the self-configuring nnU-Net framework substantially improved small BM detection sensitivity while maintaining a controlled FP rate. Clinical utility of the extended nnU-Net model for assisting early BM detection and SRS planning will be investigated.

A novel compression methodology for medical images using deep learning for high-speed transmission

Medical imaging is a rapidly growing field having a high impact on the early detection, diagnosis and surgical planning of diseases. Several imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRI) and ultrasound (US) imaging generate a higher volume of data, necessitating additional storage and communication requirements. Hence, image compression is utilized in medical field to reduce redundancy and alleviate memory and bandwidth issues. This paper presents a novel deep learning-based compression method to reduce the size of medical images. This method employs a deep convolutional neural network for learning compact representations of medical images, then coded by a Huffman encoder. The compression process is reversed to reconstruct the original image. Several tests are conducted to compare the results with other wellknown compression methods. The proposed model achieved a mean peak signal-to-noise ratio (PSNR) of 42.82 dB with storage space saving (SSS) of 96.15% for CT, 43.88 dB with SSS of 96.25% for MRI, 46.29 dB with SSS of 96.07% for US and 43.51 dB with SSS of 96.95% for X-ray images. The findings showed that the proposed compression technique could greatly compress the image size, saving storage space, facilitating better transmission and preserving critical diagnostic information.

The Role of Adverse Childhood Experience in the Relationship Between Autism Severity and Early Intervention and Special Education Plan.

The purpose of this study is to examine the association between autism spectrum disorder (ASD) severity and having a special education or early intervention plan and the impact of adverse childhood experiences (ACEs) on this association. This study used the 2020-2021 National Survey of Children's Health (NSCH) and included 2,537 children aged 3-17 years old who currently have ASD. Multivariable logistic regression, controlling for demographic and family characteristics and health status, was used to explore the association between autism severity and having an early intervention plan. The analysis was stratified by the number of ACEs to explore their role in the association. Children with moderate or severe ASD were more likely to have a special education or early intervention plan than those with mild ASD in the crude and adjusted models. This association continued to be true for children who experienced 1 ACE (aOR: 2.28, 95%CI: 1.09-4.77) but not true for those who experienced no ACEs (aOR: 1.16, 95%CI: 0.70-1.94) and 2 or more ACEs (aOR: 1.84, 95%CI: 0.92-3.69). Results demonstrate that children with moderate or severe autism were more likely to receive early intervention or special education. This association changed depending on the number of ACEs experiences.

Polyp-LVT: Polyp segmentation with lightweight vision transformers

Automatic segmentation of polyps in endoscopic images is crucial for early diagnosis and surgical planning of colorectal cancer. However, polyps closely resemble surrounding mucosal tissue in both texture and indistinct borders and vary in size, appearance, and location which possess great challenge to polyp segmentation. Although some recent attempts have been made to apply Vision Transformer (ViT) to polyp segmentation and achieved promising performance, their application in clinical scenarios is still limited by high computational complexity, large model size, redundant dependencies, and significant training costs. To address these limitations, we propose a novel ViT-based approach named Polyp-LVT, strategically replacing the attention layer in the encoder with a global max pooling layer, which significantly reduces the model’s parameter count and computational cost while keeping the performance undegraded. Furthermore, we introduce a network block, named Inter-block Feature Fusion Module (IFFM), into the decoder, aiming to offer a streamlined yet highly efficient feature extraction. We conduct extensive experiments on three public polyp image benchmarks to evaluate our method. The experimental results show that compared with the baseline models, our Polyp-LVT network achieves a nearly 44% reduction in model parameters while gaining comparable segmentation performance.

Multimodal Data-Driven Intelligent Systems for Breast Cancer Prediction

Cancer, a malignant disease, results from abnormalities in the body cells that lead to uncontrolled growth and division, surpassing healthy growth and stability. In the case of breast cancer, this uncontrolled growth and division occurs in breast cells. Early identification of breast cancer is key to lowering mortality rates. Several new developments in artificial intelligence predictive models show promise for assisting decision-making. The primary goal of the proposed study is to build an efficient Breast Cancer Intelligent System using a multimodal dataset. The aim is to to establish Computer-Aided Diagnosis for breast cancer by integrating various data.This study uses the TCGA "The Cancer Genome Atlas Breast Invasive Carcinoma Collection" (TCGA-BRCA) dataset, which is part of an ongoing effort to create a community integrating cancer phenotypic and genotypic data. The TCGA- BRCA dataset includes: Clinical Data, RNASeq Gene Data, Mutation Data, and Methylation Data. Both clinical and genomic data are used in this study for breast cancer diagnosis. Integrating multiple data modalities enhances the robustness and precision of diagnostic and prognostic models in comparison with conventional techniques. The approach offers several advantages over unimodal models due to its ability to integrate diverse data sources. Additionally, these models can be employed to forecast the likelihood of a patient developing breast cancer in the near future, providing a valuable tool for early intervention and treatment planning.

Predicting Driving Cessation Among Cognitively Normal Older Drivers: The Role of Alzheimer Disease Biomarkers and Clinical Assessments.

With the aging US population and increasing incidence of Alzheimer disease (AD), understanding factors contributing to driving cessation among older adults is crucial for clinicians. Driving is integral for maintaining independence and functional mobility, but the risk factors for driving cessation, particularly in the context of normal aging and preclinical AD, are not well understood. We studied a well-characterized community cohort to examine factors associated with driving cessation. This prospective, longitudinal observation study enrolled participants from the Knight Alzheimer Disease Research Center and The DRIVES Project. Participants were enrolled if they were aged 65 years or older, drove weekly, and were cognitively normal (Clinical Dementia Rating [CDR] = 0) at baseline. Participants underwent annual clinical, neurologic, and neuropsychological assessments, including β-amyloid PET imaging and CSF (Aβ42, total tau [t-Tau], and phosphorylated tau [p-Tau]) collection every 2-3 years. The primary outcome was time from baseline visit to driving cessation, accounting for death as a competing risk. The cumulative incidence function of driving cessation was estimated for each biomarker. The Fine and Gray subdistribution hazard model was used to examine the association between time to driving cessation and biomarkers adjusting for clinical and demographic covariates. Among the 283 participants included in this study, there was a mean follow-up of 5.62 years. Driving cessation (8%) was associated with older age, female sex, progression to symptomatic AD (CDR ≥0.5), and poorer performance on a preclinical Alzheimer cognitive composite (PACC) score. Aβ PET imaging did not independently predict driving cessation, whereas CSF biomarkers, specifically t-Tau/Aβ42 (hazard ratio [HR] 2.82, 95% CI 1.23-6.44, p = 0.014) and p-Tau/Aβ42 (HR 2.91, 95% CI 1.28-6.59, p = 0.012) ratios, were independent predictors in the simple model adjusting for age, education, and sex. However, in the full model, progression to cognitive impairment based on the CDR and PACC score across each model was associated with a higher risk of driving cessation, whereas AD biomarkers were not statistically significant. Female sex, CDR progression, and neuropsychological measures of cognitive functioning obtained in the clinic were strongly associated with future driving cessation. The results emphasize the need for early planning and conversations about driving retirement in the context of cognitive decline and the immense value of clinical measures in determining functional outcomes.

Integrating Architectural Heritage with Environmental Concerns: A Case Study of Clock Tower, Faisalabad

Faisalabad is experiencing a surge in industrial development alongside its established agricultural base. This economic growth, however, presents a challenge of balancing progress with environmental protection. Industrial activity can often lead to environmental degradation, impacting the city's physical landscape and the health of its residents. To address this concern, the current research focused on the restoration of the Faisalabad Clock Tower, a historical structure from the British Era. The Clock Tower holds a significant importance due to its role in the city's early urban planning and its status as a prominent landmark. The research methodology employed a comprehensive literature review, examining local and international examples of heritage building projects. Air pollution levels were measured around the Clock Tower by using HORIBA APMA 360. According to the findings, NO2 was found above the limit which confirmed that the presence of pollutants is the major deterioration factor. Furthermore, a detailed physical survey of the Clock Tower was conducted to identify the deteriorated areas within the building. The research culminates in a proposal outlining the necessary restoration work for the Clock Tower, informed by a thorough analysis that caters to architectural and environmental concerns to ensure a sustainable and historically sensitive restoration of this iconic structure.

Macrodactyly.

Macrodactyly is a rare congenital limb difference manifesting as an overgrowth of one or more fingers or toes. The pathological process affects all tissues of the ray in the hand or foot. The enlargement can significantly alter the limb's appearance and impair its function. The role of a pediatrician is to distinguish isolated macrodactyly from syndromic conditions (including PIK3CA-Related Overgrowth Spectrum) or mimicking conditions to enable early interdisciplinary consultation and treatment planning. The psychological stigma associated with this often disfiguring condition necessitates support for patients and their family. We present a practical guide for physicians who might be the first to raise suspicion of macrodactyly and initiate further diagnostics to achieve adequate treatment and support for children and caregivers.

Brain Tumor Detection and Classification Using Adjusted InceptionV3, AlexNet, VGG16, VGG19 with ResNet50-152 CNN Model

INTRODUCTION: Brain tumors have become a major global health concern, characterized by the abnormal growth of brain cells that can negatively affect surrounding tissues. These cells can either be malignant (cancerous) or benign (non-cancerous), with their impact varying based on their location, size and type. OBJECTIVE: Early detection and classification of brain tumors are challenging due to their complex and variable structural makeup. Accurate early diagnosis is crucial to minimize mortality rates. METHOD: To address this challenge, researchers proposed an optimized model based on Convolutional Neural Networks (CNNs) with transfer learning, utilizing architectures like Inception-V3, AlexNet, VGG16, and VGG19. This study evaluates the performance of these adjusted CNN models for brain tumor identification and classification using MRI data. The TCGA-LGG and The TCIA, two well-known open-source datasets, were employed to assess the model's performance. The optimized CNN architecture leveraged pre-trained weights from large image datasets through transfer learning. RESULTS: The refined ResNet50-152 model demonstrated impressive performance metrics: for the non-tumor class, it achieved a precision of 0.98, recall of 0.95, F1 score of 0.93, and accuracy of 0.94; for the tumor class, it achieved a precision of 0.87, recall of 0.92, F1 score of 0.88, and accuracy of 0.96. CONCLUSION: These results indicate that the refined CNN model significantly improves accuracy in classifying brain tumors from MRI scans, showcasing its potential for enhancing early diagnosis and treatment planning.

A Comparative Analysis of Low-Carbon Design Strategies for China’s Higher Education Parks Based on Building and Urban Scale in Sustainability Rating Systems

In the global context of carbon neutrality, higher education parks are an important strategic position for achieving China’s goal of carbon peaking and carbon neutralization. Strategies from the perspective of life cycle to guide early low-carbon planning and design are an effective way to achieve carbon emission reduction goals. As the scale of university construction gradually expands, the “urban” attributes of them are becoming prominent. However, there is no quantitative study on analyzing the life cycle carbon emission strategies at both the building and urban scale based on sustainability rating systems. This study first extracts the design strategies according to BREEAM, LEED, DGNB and relative assessment standards for campuses and cities in China at the building and urban scale based on the 7-dimensional low-carbon strategy framework, then sorts out and compares the proportions of carbon-emission-related strategies across various dimensions and life cycle stages. It then summarizes the applications and concerns of low-carbon design strategies at different design scales. Finally, the weighting and calculation methods of life cycle carbon emissions in different sustainability rating systems are compared, the scope and methods of carbon emission benchmarks under different standards are compiled, and the evaluation method for locally applicable carbon emission benchmarks in China is proposed in light of China’s national conditions, which provides guidance for the design process and standard formulation.

What Makes Effective Acute Geriatric Care? - A mixed Methods Study From Aotearoa New Zealand

Structured Objectives Current policies for older patients do not adequately address the barriers to effective implementation of optimal care models in New Zealand, partly due to differences in patient definitions and the in-patient pathway they should follow through hospital. This research aims to: (a) synthesise a definition of a complex older patient; (b) identify and explore primary and secondary health measures; and (c) identify the primary components of a care model suitable for a tertiary hospital in the midland region of the North Island of New Zealand.MethodThis mixed-methods study utilised a convergence model, in which qualitative and quantitative data were investigated separately and then combined for interpretation. Semi-structured interviews (n=11) were analysed using a general inductive method of enquiry to develop key codes, categories and themes. Univariate data analysis was employed using six years of routinely collected data of patients admitted to the emergency department and inpatient units (n=261,773) of the tertiary hospital.ResultsA definition of a complex older patient was determined that incorporates chronic conditions, comorbidities and iatrogenic complications, functional decline, activities of daily living, case fatality, mortality, hospital length of stay, hospital costs, discharge destination, hospital readmission and emergency department revisit and age – not necessarily over 65 years old. Well-performing geriatric care models were found to include patient-centred care, frequent medical review, early rehabilitation, early discharge planning, a prepared environment and multidisciplinary teams.ConclusionsThe findings of this New Zealand study increase understanding of acute geriatric care for complex older patients by filling a gap in policies and strategies, identifying potential components of an optimal care model and defining a complex geriatric patient.Implications for Public HealthThe findings of this study present actionable opportunities for clinicians, managers, academics and policymakers to better understand a complex older patient in New Zealand, with significant relevance also for international geriatric care and to establish an effective acute geriatric care model that leads to beneficial health outcomes and provides safeguard mechanisms.

Enhancing brain tumor classification in MRI scans with a multi-layer customized convolutional neural network approach.

The necessity of prompt and accurate brain tumor diagnosis is unquestionable for optimizing treatment strategies and patient prognoses. Traditional reliance on Magnetic Resonance Imaging (MRI) analysis, contingent upon expert interpretation, grapples with challenges such as time-intensive processes and susceptibility to human error. This research presents a novel Convolutional Neural Network (CNN) architecture designed to enhance the accuracy and efficiency of brain tumor detection in MRI scans. The dataset used in the study comprises 7,023 brain MRI images from figshare, SARTAJ, and Br35H, categorized into glioma, meningioma, no tumor, and pituitary classes, with a CNN-based multi-task classification model employed for tumor detection, classification, and location identification. Our methodology focused on multi-task classification using a single CNN model for various brain MRI classification tasks, including tumor detection, classification based on grade and type, and tumor location identification. The proposed CNN model incorporates advanced feature extraction capabilities and deep learning optimization techniques, culminating in a groundbreaking paradigm shift in automated brain MRI analysis. With an exceptional tumor classification accuracy of 99%, our method surpasses current methodologies, demonstrating the remarkable potential of deep learning in medical applications. This study represents a significant advancement in the early detection and treatment planning of brain tumors, offering a more efficient and accurate alternative to traditional MRI analysis methods.

Session 20. Oral Presentation for: Decommissioning and restoration: early planning is critical to an efficient and risk-minimised process

Session 20. Oral Presentation for: Decommissioning and restoration: early planning is critical to an efficient and risk-minimised process

Predicting Flood Event Class Using a Novel Class Membership Function and Hydrological Modeling

AbstractPredicting flood event classes aids in the comprehensive investigation of flood behavior dynamics and supports flood early warning and emergency plan development. Existing studies have mainly focused on historical flood event classification and the prediction of flood hydrographs or certain metrics (e.g., magnitude and timing) but have not focused on predicting flood event classes. Our study proposes a new approach for predicting flood event classes based on the class membership functions of flood regime metrics and hydrological modeling. The approach is validated using 1446 unimpacted flood events in 68 headstream catchments widely distributed across China. The new approach performs well, with class hit rates of 68.3% ± 0.4% for all events; 65.8% ± 0.6%, 56.8% ± 0.9%, and 69.5% ± 0.9% for the small, moderate and high spike flood event classes, respectively; and 82.5% ± 1.2% and 75.4% ± 1.1% for the moderate and high dumpy flood event classes, respectively. Furthermore, it performs better in the basins of northern China than in those of southern China, particularly for the small spike flood event class in the Songliao and Yellow River Basins, with hit rates of 80.0% ± 3.2% and 78.8% ± 3.2%, respectively. Our results indicate that the new approach will help improve the prediction performance of flood events and their corresponding classes, and provide deep insights into the comprehensive dynamic patterns of flood events for early warning and control management.

Machine learning techniques to identify risk factors of breast cancer among women in Mashhad, Iran.

Low survival rates of breast cancer in developing countries are mainly due to the lack of early detection plans and adequate diagnosis and treatment facilities. This study aimed to apply machine learning techniques to recognize the most important breast cancer risk factors. This case-control study included women aged 17-75 years who were referred to medical centers affiliated with Mashhad University of Medical Science between March 21, 2015, and March 19, 2016. The study had two datasets: one with 516 samples (258 cases and 258 controls) and another with 606 samples (303 cases and 303 controls). Written informed consent has been observed. Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), and Principal Component Analysis (PCA) were applied using R studio software. Regarding the DT and RF, the most important features that impact breast cancer were family cancer, individual history of breast cancer, biopsy sampling, rarely consumption of a dairy, fruit, and vegetable meal, while in PCA and LR these features including family cancer, pregnancy number, pregnancy tendency, abortion, first menstruation, the age of first childbirth and childbirth number. Machine learning algorithms can be used to extract the most important factors in the diagnosis of breast cancer in developing countries such as Iran.

A multimodal machine learning model for predicting dementia conversion in Alzheimer’s disease

Alzheimer’s disease (AD) accounts for 60–70% of the population with dementia. Mild cognitive impairment (MCI) is a diagnostic entity defined as an intermediate stage between subjective cognitive decline and dementia, and about 10–15% of people annually convert to AD. We aimed to investigate the most robust model and modality combination by combining multi-modality image features based on demographic characteristics in six machine learning models. A total of 196 subjects were enrolled from four hospitals and the Alzheimer’s Disease Neuroimaging Initiative dataset. During the four-year follow-up period, 47 (24%) patients progressed from MCI to AD. Volumes of the regions of interest, white matter hyperintensity, and regional Standardized Uptake Value Ratio (SUVR) were analyzed using T1, T2-weighted-Fluid-Attenuated Inversion Recovery (T2-FLAIR) MRIs, and amyloid PET (αPET), along with automatically provided hippocampal occupancy scores (HOC) and Fazekas scales. As a result of testing the robustness of the model, the GBM model was the most stable, and in modality combination, model performance was further improved in the absence of T2-FLAIR image features. Our study predicts the probability of AD conversion in MCI patients, which is expected to be useful information for clinician’s early diagnosis and treatment plan design.