Diagnosis Process Research Articles

Deep Learning Algorithm for Optimized Sensor Data Fusion in Fault Diagnosis and Tolerance

Environmental perception is one of the key technologies to realize autonomous vehicles. The fault diagnosis process involves identifying the fault that occurred or the cause of the out-of-control condition. Here, the major objective is to locate problems in detection by analysing previous data or sequential patterns of data that cause failure. This study evaluates the use of deep learning for improved sensor data fusion in fault identification and tolerance using the KITTI dataset. The input video from the dataset has been transformed to frames through median filtering. Next, feature extraction is applied to a preprocessed image, resulting in the fusion of sensor data. Data fusion is then carried out utilizing an enhanced RPN (region proposal network). The enhanced RPN also has a loss function (object detection loss, bounding box loss and target classification loss), an estimate of ROI and feature extraction network (FEN). Through the use of the COOT connected blue monkey optimization (CCBMO) model, the weight of the optimally enhanced RPN is established. Next, using global non-maximum suppression with both global and local confidence, fault identification and tolerance are carried out. From the analysis, it clearly shows that proposed method accomplished better results in terms of accuracy, precision and specificity of 97.78%, 93.76% and 93.43%, respectively, when compared with various conventional models with respect to diverse performance measures.

Hybrid similarity based feature selection and Cascade deep maxout fuzzy network for Autism Spectrum Disorder detection using EEG signal

Autism Spectrum Disorder (ASD) is a neurological disorder that influences a person’s comprehension and way of behaving. It is a lifetime disability that cannot be completely treated using any therapy up to date. Nevertheless, in time identification and continuous therapies have a huge effect on autism patients. The existing models took a long time to confirm the diagnosis process and also, it is highly complex to differentiate autism from various developmental disorders. To facilitate early diagnosis by providing timely intervention, saving healthcare costs and reducing stress for the family in the long run, this research introduces an affordable and straightforward diagnostic model to detect ASD using EEG and deep learning models. Here, a hybrid deep learning model called Cascade deep maxout fuzzy network (Cascade DMFN) is proposed to identify ASD and it is achieved by the integration of Deep Maxout Network (DMN) and hybrid cascade neuro-fuzzy. Moreover, hybrid similarity measures like Canberra distance and Kumar-hassebrook is employed to conduct the feature selection technique. Also, the EEG dataset and BCIAUT_P300 dataset are used for analyzing the designed Cascade DMFN for detecting Autism Spectrum disorder. The designed Cascade DMFN has outperformed other classical models by yielding a high accuracy of 0.930, Negative Predictive Value (NPV) of 0.919, Positive Predictive Value (PPV) of 0.923, True Negative Rate (TNR) of 0.926, and True Positive Rate (TPR) of 0.934.

Patient perceptions of the Finnish guidelines enabling coeliac disease diagnosis without biopsies in adults

Objectives Diagnosis of coeliac disease based on serology only has been allowed since 2018 in Finland for adults meeting specific criteria. We studied the patient experiences and perceptions of this novel diagnostic option. Methods Altogether 194 adult patients were questioned on socio‐demographic and health-related characteristics, quality of life and various coeliac disease-related issues. The results were compared between patients diagnosed with intestinal biopsy or based on serology only. Results Altogether 69 (36%) of the patients were diagnosed without duodenal biopsies. They were younger (median 43 vs. 51 years, p = 0.046), diagnosed more recently (2021 vs. 2020, p < 0.001) and more often in primary health care (78% vs. 43%, p = 0.001), had fewer esophageal symptoms at diagnosis (17% vs. 30%, p = 0.046) and considered the diagnostic process easier (49% vs. 30%, p = 0.032) than those diagnosed by duodenal biopsy (n = 125). The no-biopsy group received less often dietician follow-up (4% vs. 17%, p = 0.012), reported more persistent symptoms (36% vs. 21%, p = 0.026) and experienced more stress due to the diet (68% vs. 47%, p = 0.039). Symptom persistence or stress were not associated with year of diagnosis or dietician follow-up. The groups were comparable in socio-economic characteristics, general health, quality of life, diagnostic delay, dietician guidance at diagnosis, and dietary adherence. Conclusions The non-invasive approach resulted in de-centralized diagnosis and easier patient-experienced diagnostic process of coeliac disease, but was associated with increased risk for persistent symptoms and stress due to gluten-free diet. These results highlight the significance of appropriate patient guidance and support regardless of the diagnostic strategy.

Safety in Connected Health Network: Predicting and Detecting Hidden Information in Data Using Multilayer Perception Deep Learning Model

With the tremendous growth in the use of information technology, the connected health networks are becoming more relevant, greatly improving the traditional standard of healthcare procedures from data acquisition, storage, and sharing among the medics for timely clinical diagnosis processes, therapy, and disease management. However, connected health network comes with network and cyber criminality challenges, and frequent security breach attacks on digital platforms and databases. Unfortunately, Sensitive clinical information is greatly at risk with adversaries, most clinical stakeholders find it difficult to overlook free access to the clinical records. Previously, feature aggregation networks, convolutional neural networks, residual convolutional neural networks, and machine learning models were used in different methodological approaches towards ensuring the detection of hidden information in images, unfortunately, none was able to produce optimal results. This study proposes a three-phased framework to determine the suitability of embedder networks’ feature extraction for image steganalysis, predicting and detecting hidden information in images. A Multilayer Perceptron (MLP) deep learning model was trained for pattern recognition of steganography instances in acquired digital image signals. The digital image signals used for the predictive steganalysis are publicly available images contained in two circumstances highlighted regarding clean image signals (situation of cover images, but without steganography) and the embedded situation of image signals (where images (stego) with hidden data or information). Interestingly, the results of the parameter show that the Max-iter parameter of the MLP classifier hugely determines the performance of the algorithm towards detecting steganography in digital image signals. The parameter stipulates the number of times the training set will pass through the MLP network for the training process. Significantly, in our experiment, Max-iter returned the best result at 1000 netting an accuracy of 93%, precision of 57%, and recall of 100% weighted averages. Our study does not only implement a model that detects hidden information in images, but it also discovered and tuned the multilayer perceptron to determine where it will perform best.

Gynecology and obstetrics patients' attitudes towards body privacy: A cross-sectional-descriptive study.

It is extremely important for healthcare professionals to protect patients' privacy during diagnosis and treatment processes related to gynecology and obstetrics and to warn them in case patients' privacy is violated. This study was aimed at determining attitudes of female patients having obstetrics examination towards privacy and raising their awareness of privacy. This cross-sectional-descriptive study was conducted with 410 patients who presented to the gynecology and obstetrics outpatient clinic of a university hospital. The survey form consisted of two forms: The descriptive characteristics questionnaire and the body privacy scale for gynecology and obstetrics (BPSGO). Frequency tables, descriptive statistics, Mann-Whitney U-test, Kruskal-Wallis H-test, and Bonferroni correction were used in the analysis of the data. The mean age of the participants was 36.75 ± 11.18 years. Of these, 38.0% were in the age group of ≥40 years, 84.0% were married, 66.8% presented to the gynecology clinic, and 58.3% had negative emotions before they had gynecologic examination. Ethics and privacy subscale scores of the single participants were significantly higher than were those of the married participants. It was determined that privacy was affected by cultural characteristics, belief systems and personal values, and that most of the participants were likely to experience negative emotions. It is important to raise health professionals' awareness of privacy by drawing their attention to the value of privacy which is among ethical values in their education and to support the better functioning of legal regulations on this issue.

WITHDRAWN: Expert Consensus on the Diagnosis and Treatment of FGFR Gene-altered Solid Tumors

The fibroblast growth factor receptor (FGFR) is a crucial receptor tyrosine kinase involved in essential biological processes, including growth, development, and tissue repair. However, FGFR gene mutations, including amplification, fusion, and mutation, can disrupt epigenetics, transcriptional regulation, and tumor microenvironment interactions, leading to cancer development. Targeting these kinase mutations with small molecule drugs or antibodies has shown clinical benefits. For example, Edatinib is approved for treating locally advanced or metastatic urothelial cancer patients with FGFR2/FGFR3 mutations, and Pemitinib is approved for treating cholangiocarcinoma with FGFR2 fusion/rearrangement. Effective screening of FGFR variant patients is crucial for the clinical application of FGFR inhibitors. Various detection methods, such as polymerase chain reaction (PCR), next-generation sequencing (NGS), fluorescence in situ hybridization (FISH), and immunohistochemistry (IHC), are available, and their selection should be based on diagnostic and treatment decision-making needs. Our developed expert consensus aims to standardize the diagnosis and treatment process for FGFR gene mutations and facilitate the practical application of FGFR inhibitors in clinical practice.

Applying Deep Learning Models to Breast Ultrasound Images for Automating Breast Cancer Diagnosis

Breast cancer is a result of uncontrolled human cell division. The vast growth of breast cancer patients has been an issue worldwide. Most of the patients are women, but breast cancer also affects men with a much lesser percentage. Breast cancer might lead to death for those who are suffering from it. Numerous types of research have been done to make an early diagnosis of breast cancer. It has been proven that the tumor can be detected by using an ultrasound image. Artificial Intelligence techniques have been used to detect breast cancer fundamentally. This paper studies the effectiveness of deep learning (DL) techniques in automating breast cancer diagnosis. Subsequently, the paper evaluates the diagnosis performance of three DL models utilizing the criteria of accuracy, recall, precision, and f1-score. The Densenet-169, U-Net, and ConvNet DL models are selected based on the examination of the related work. The DL diagnosis process involves identifying two types of breast cancer tumors: benign and malignant. The evaluation outcomes of the DL models show that the most effective model for diagnosing breast cancer among the three is the ConvNet, which achieves an accuracy of 91%, a recall of 83%, a precision of 85%, and an F1-score of 83%.

Walking a tightrope: perspectives of non-degree allopathic providers (NDAPs) on providing diabetes and hypertension care in urban informal settlements of Mumbai Metropolitan Region

BackgroundIn India, Non-Degree Allopathic Providers (NDAPs), who do not have formal training in allopathic medicine, play a prominent role in basic healthcare delivery in both rural areas and urban informal settlements. Often recognized as providers of ‘first contact’ care for minor acute ailments, there is little information regarding the roles they play in providing services for non-communicable diseases (NCDs). In this study, we explore the roles played by NDAPs in diagnosing and managing two NCDs—diabetes and hypertension—in urban informal settlements of the Mumbai Metropolitan Region.MethodsThis is a qualitative study involving data collection with 25 NDAPs (19 males and 6 females). Data was collected between December 2022 and September 2023. Data was coded inductively, and an iterative process of coding was followed to derive key themes. These themes were further refined through reflections within the author group. The qualitative software NVivo Version 10.3 was used to facilitate the analysis process.ResultsAll NDAPs we spoke to noted an increase in diabetes and hypertension patients in the urban informal settlements they worked in. All of them provided medication for ‘quick relief’ to patients from the bothersome symptoms of the two diseases. But in some cases, NDAPs also reported acting as counsellors, patient navigators, and local supervisors of therapy initiated by other doctors. Generally, risk-averse, NDAPs were cautious about how much of the diagnosis and treatment process they participated in. Those with informal and formal connections with private, qualified allopathic providers involved themselves more extensively in the management of the two NCDs. NDAPs had limited ties with the public health system and preferred sending patients to other private doctors if they felt a case was beyond their purview.ConclusionThe informal health sector in India is currently offering a range of services to address the needs of patients with NCDs. Our study suggests that the strong presence of this sector in resource-constrained communities can be leveraged by the public health system to enable community-level screening for NCDs, facilitate access to specialist care, improve treatment adherence, and promote wellness initiatives. In light of the changing epidemiological burden, our study underscores that despite the contentious nature of practices in the informal health sector, overlooking this group of providers is no longer an option for health policies.

A Modified Transformer Network for Seizure Detection Using EEG Signals.

Seizures have a serious impact on the physical function and daily life of epileptic patients. The automated detection of seizures can assist clinicians in taking preventive measures for patients during the diagnosis process. The combination of deep learning (DL) model with convolutional neural network (CNN) and transformer network can effectively extract both local and global features, resulting in improved seizure detection performance. In this study, an enhanced transformer network named Inresformer is proposed for seizure detection, which is combined with Inception and Residual network extracting different scale features of electroencephalography (EEG) signals to enrich the feature representation. In addition, the improved transformer network replaces the existing Feedforward layers with two half-step Feedforward layers to enhance the nonlinear representation of the model. The proposed architecture utilizes discrete wavelet transform (DWT) to decompose the original EEG signals, and the three sub-bands are selected for signal reconstruction. Then, the Co-MixUp method is adopted to solve the problem of data imbalance, and the processed signals are sent to the Inresformer network for seizure information capture and recognition. Finally, discriminant fusion is performed on the results of three-scale EEG sub-signals to achieve final seizure recognition. The proposed network achieves the best accuracy of 100% on Bonn dataset and the average accuracy of 98.03%, sensitivity of 95.65%, and specificity of 98.57% on the long-term CHB-MIT dataset. Compared to the existing DL networks, the proposed method holds significant potential for clinical research and diagnosis applications with competitive performance.

Circumpapillary OCT-based multi-sector analysis of retinal layer thickness in patients with glaucoma and high myopia

Glaucoma is the leading cause of irreversible blindness worldwide. The diagnosis process for glaucoma involves the measurement of the thickness of retinal layers in order to track its degeneration. The elongated shape of highly myopic eyes can hinder this diagnosis process, since it affects the OCT scanning process, producing deformations that can mimic or mask the degeneration caused by glaucoma. In this work, we present the first comprehensive cross-disease analysis that is focused on the anatomical structures most impacted in glaucoma and high myopia patients, facilitating precise differential diagnosis from those solely afflicted by myopia. To achieve this, a fully automatic approach for the retinal layer segmentation was specifically tailored for the accurate measurement of retinal thickness in both highly myopic and emmetropic eyes. To the best of our knowledge, this is the first approach proposed for the analysis of retinal layers in circumpapillary optical coherence tomography images that takes into account the elongation of the eyes in myopia, thus addressing critical diagnostic needs. The results from this study indicate that the temporal superior (mean difference 11.1μm, p<0.05), nasal inferior (13.1μm, p<0.01) and temporal inferior (13.3μm, p<0.01) sectors of the retinal nerve fibre layer show the most significant reduction in retinal thickness in patients of glaucoma and myopia with regards to patients of myopia.

Health monitoring and fault analysis of induction motors: a review

Abstract Induction motors are in high demand for industrial applications due to their reliable and efficient performance compared to other electrical motors. Therefore, the implementation of a predictive maintenance scheme is necessary to minimise financial losses in terms of revenue and maintenance costs. This scheme involves health monitoring system, aimed to enhance motor reliability, extend their useful lifespan and reduce maintenance expenses. Furthermore, traditional health monitoring systems often involve human intervention, which can slow down the diagnosis process. Therefore, a self-sufficient automated system has evolved to improve motor performance and reliability. This paper presents a comprehensive study of induction motor failures and the various methods used for their diagnosis. Additionally, it also discussed the importance of predictive maintenance and the pivotal role played by automated systems in ensuring the sustained efficiency of induction motors in industrial settings.

Rapid, Non-Invasive, Accurate Diagnosis and Efficient Clearance of Metastatic Lymph Nodes.

Sentinel lymph node (SLN) biopsy is currently the standard procedure for clinical cancer diagnosis and treatment, but still faces the risks of false negatives and tumor metastasis, as well as time-consuming pathological evaluation procedure. Herein, we proposed a near-infrared-II (NIR-II, 1000-1700 nm) theranostic nanosystem (FLAGC) for rapid, non-invasive, accurate diagnosis and efficient clearance of metastatic lymph nodes in breast cancer. Initialized by chlorin e6 (Ce6), a pH-sensitive amphiphilic amino acid fluorenylmethoxycarbonyl-L-histidine (Fmoc-His) was assembled with Gd3+, luminol, and AgAuSe quantum dots (AAS QDs) to form FLAGC. In FLAGC, luminol and AAS QDs form a NIR-II chemical resonance energy transfer (CRET) system (Luminol-AAS); Ce6 initiates the assembly and also serves as a photosensitizer. Upon subcutaneous injection, FLAGC is easily drained into SLNs, achieving their precise localization. Subsequently, the acidity of tumor microenvironment triggers the rapid disassembly of FLAGC, exposing Luminol-AAS. myeloperoxidase (MPO) secreted by tumor-associated macrophages and neutrophils in SLNs mediates the oxidation of luminol, lighting up AAS QDs through the CRET process for precise diagnosis of metastatic lymph nodes. Moreover, highly efficient clearance of positive lymph nodes is achieved through Ce6-mediated photodynamic therapy. Our strategy provides a new paradigm for identifying and eliminating clinically metastatic lymph nodes.

Low Serum Beta-2 Microglobulin Level: A Possible Biomarker for Sarcopenia in the Elderly Population.

Background and Objectives: One of the most critical problems regarding sarcopenia is the difficulty of the diagnosis process. This study aimed to determine the prevalence and investigate the role of serum beta-2 microglobulin level as a biomarker for diagnosing sarcopenia. Materials and Methods: This nested case-control study was conducted between 2023 and 2024 on 251 older adults. Muscle strength was measured using a hand dynamometer, and muscle mass was assessed using the bioelectrical impedance method. Individuals with low muscle strength and low muscle mass were accepted as having definitive sarcopenia. Results: The mean age of the 251 older adults included in the study was 72.19 ± 6.11 years. The prevalence of sarcopenia in individuals aged 65 years and over was found to be 5.2%. Serum beta-2 microglobulin levels were statistically significantly lower in sarcopenic participants compared to the control group (p = 0.002). The optimal cut-off value for serum beta-2 microglobulin level was 2.26 mcg/mL, and values lower than this point were found to be diagnostic for sarcopenia. Regarding the cut-off value, the sensitivity was 92.31% and the specificity was 80.77%, the positive predictive value was 70.59%, the negative predictive value was 95.45%, the Youden index was 0.731, and the area under the curve value was 0.901. Individuals who had beta-2 microglobulin levels below 2.26 mcg/mL were found to have a 10.75 times higher risk of sarcopenia. Conclusions: A low serum beta-2 microglobulin level has the potential to be an important candidate biomarker for the diagnosis of sarcopenia.

Building a DenseNet-Based Neural Network with Transformer and MBConv Blocks for Penile Cancer Classification

Histopathological analysis is an essential exam for detecting various types of cancer. The process is traditionally time-consuming and laborious. Taking advantage of deep learning models, assisting the pathologist in the diagnosis process is possible. In this work, a study was carried out based on the DenseNet neural network. It consisted of changing its architecture through combinations of Transformer and MBConv blocks to investigate its impact on classifying histopathological images of penile cancer. Due to the limited number of samples in this dataset, pre-training is performed on another larger lung and colon cancer histopathological image dataset. Various combinations of these architectural components were systematically evaluated to compare their performance. The results indicate significant improvements in feature representation, demonstrating the effectiveness of these combined elements resulting in an F1-Score of up to 95.78%. Its diagnostic performance confirms the importance of deep learning techniques in men’s health.

Crop Disease Detection

Detecting diseases in crops is a vital yet labor-intensive task in agriculture, often demanding extensive time and expert knowledge. This paper presents an innovative approach to crop disease detection using advanced computer vision and machine learning techniques. By automating the identification of common crop diseases, this system aims to reduce the reliance on expert intervention, expedite the diagnosis process, and ultimately improve crop management efficiency. The proposed method integrates deep learning models trained on a diverse dataset of diseased and healthy crop images, achieving high accuracy in disease recognition. This approach not only saves time but also provides farmers with a powerful tool to protect their crops from potential threats, thereby contributing to increased agricultural productivity and sustainability.

Systematic Review of Deep Learning Techniques in Skin Cancer Detection

Skin cancer is a serious health condition, as it can locally evolve into disfiguring states or metastasize to different tissues. Early detection of this disease is critical because it increases the effectiveness of treatment, which contributes to improved patient prognosis and reduced healthcare costs. Visual assessment and histopathological examination are the gold standards for diagnosing these types of lesions. Nevertheless, these processes are strongly dependent on dermatologists’ experience, with excision advised only when cancer is suspected by a physician. Multiple approaches have surfed over the last few years, particularly those based on deep learning (DL) strategies, with the goal of assisting medical professionals in the diagnosis process and ultimately diminishing diagnostic uncertainty. This systematic review focused on the analysis of relevant studies based on DL applications for skin cancer diagnosis. The qualitative assessment included 164 records relevant to the topic. The AlexNet, ResNet-50, VGG-16, and GoogLeNet architectures are considered the top choices for obtaining the best classification results, and multiclassification approaches are the current trend. Public databases are considered key elements in this area and should be maintained and improved to facilitate scientific research.

OGC SO46 - Gastric Cancer: A Comprehensive Literature Review

Abstract Background Gastric cancer stands as a significant global health concern with high mortality rates urging urgent attention. This article comprehensively explores the epidemiology, anatomy, risk factors, pathophysiology, clinical presentation, diagnosis, staging, treatment modalities, prevention strategies, and survival rates associated with gastric cancer. Notably, Helicobacter pylori infection, dietary choices, and intricate stomach anatomy play pivotal roles in disease development. Early detection, utilizing staging, grading, and genetic testing for personalized treatment approaches is emphasized. Treatment modalities encompass surgery, chemotherapy, radiation therapy, targeted therapy, and immunotherapy. Prevention strategies involve lifestyle changes, and genetic counseling. Survival rates vary by stage, highlighting the need for individualized care. Method This comprehensive review was conducted by systematically analyzing a wide range of published literature on gastric cancer. The search strategy involved utilizing electronic databases such as PubMed, Google Scholar, and Scopus, with specific keywords including "gastric cancer," "stomach cancer," "epidemiology," "anatomy," "risk factors," "pathophysiology," "diagnosis," "treatment modalities," "prevention," and "survival rates." Results Risk Factors: Factors such as Helicobacter pylori infection, dietary choices, tobacco use, and genetic predispositions contribute to gastric cancer risk. Pathophysiology: Genetic alterations, chronic inflammation, and dysregulated signaling pathways drive the development of gastric cancer. Clinical Presentation: Symptoms vary &amp; diagnosis involves a combination of methods, including endoscopy and molecular profiling. Treatment Modalities: Treatment options include surgery, chemotherapy, radiation therapy, targeted therapy, and immunotherapy. Prevention Strategies: Prevention efforts focus on minimizing risk factors through lifestyle changes, screening, and genetic counseling. Survival Rates: Survival rates vary significantly by stage, ranging from 90-95% for stage 0 to less than 5% for stage IV Conclusion The intricate anatomy of the stomach plays a crucial role in disease localization, staging, and treatment planning, with genetic mutations and inflammation central to the pathophysiology. Early detection is underscored through clinical presentation and diagnosis processes, utilizing staging, grading, and genetic testing for personalized treatment approaches, including surgery, chemotherapy, radiation therapy, targeted therapy, and immunotherapy. Prevention strategies emphasize lifestyle changes and screening, while survival rates vary by stage, underscoring the need for individualized care. In conclusion, addressing gastric cancer requires a collaborative effort to reduce its impact and enhance outcomes on a global scale.

The Discourse Surrounding Polycystic Ovary Syndrome on TikTok: A Social Media Analysis.

Individuals are turning increasingly towards online resources, such as TikTok, to educate themselves on their medical conditions. Polycystic ovary syndrome (PCOS) is a prominent example, as women report dissatisfaction with the diagnosis process and treatment options. This study aims to provide a content analysis of the quality of PCOS health information on TikTok. A total of 325 TikTok videos were screened. Pertinent data, including likes, comments, shares, and views, along with video content concerning symptoms, interventions, and provider interactions were analyzed. Two reviewers independently used a modified DISCERN criteria to assess the quality of information for each video. A logistic regression was also utilized to model the probability of healthcare professionals creating educational videos. A total of 238 videos met the eligibility criteria for analysis. Videos had a median of 468,400 views (Q1 = 146,400, Q3 = 1,100,000) and 18,000 likes (Q1 = 5631, Q3 = 65,100). The mean modified DISCERN scores were 3.6 ± 0.9 for physicians (n = 23), 2.0 ± 1.1 for non-physician healthcare providers (n = 52), and 1.0 ± 0.2 for non-healthcare professionals (n = 141) (p < 0.001). Healthcare professionals were 10.9 times more likely to create educational videos in comparison to non-healthcare professionals (p < 0.001). TikTok videos related to PCOS attract considerable engagement but provide low-quality information. Most videos were made by non-healthcare professionals, who discussed treatment options with limited or no research. Healthcare providers should be aware of the impacts on patients' preconceived notions and help to improve patient education.

Theoretical analysis on the response observation of acupuncture and moxibustion

The paper introduces the method of "response observation of acupuncture and moxibustion" and its clinical application, focusing on the subjects in the observation of acupuncture and moxibustion in view of the changes of illness and the body responses after acupuncture and moxibustion. The theoretical connotation of "response observation of acupuncture and moxibustion" is summarized. During and after the delivery of acupuncture and moxibustion, through observing the responses after stimulation on the body surface, the illness or the patient's functional state can be detected or judged by the doctors; and the more effective treatment be explored. It is pointed out that the "response observation of acupuncture and moxibustion" is an indispensable part in the entire process of clinical diagnosis and treatment of acupuncture and moxibustion.

QLTI-13. A REVIEW OF THE BEST TREATMENT STRATEGY FOR NEUROFIBROMATOSIS TYPE 2 PATIENTS THROUGH A SINGLE-CENTER RETROSPECTIVE STUDY: HOW TO ORCHESTRATE MULTIMODAL TREATMENT OPTIONS

Abstract PURPOSE Neurofibromatosis type 2(NF2) is an autosomal-dominant tumor predisposition syndrome that affects the whole neuro-axis and causes various neurological symptoms decreasing the quality of life throughout the patient’s lifespan. However, due to the diversity of related diseases and the complexity of treatment, there are many cases in which only partial treatments are performed, the timing of the treatment is missed, and inappropriate treatment modality selection is made despite the intervention of many clinicians. Therefore, to increase the overall understanding of the disease, the single-institution experience was analyzed. MATERIALS AND METHODS Between 1998 and 2022, a total of 80 patients who clinically suspected NF2 visited our Brain Tumor Center. After excluding the patients with follow-up loss, the remaining 48 patients were finally analyzed. The patient’s symptoms, diagnosis process, treatment process, and outcomes were analyzed. RESULTS The average age at the time of symptom onset was 31.0(2-63) year-old. Hearing impairment was the major primary symptom(46.8%) among the patients. Five patients(10.9%) had family history of NF2. Thirty-five patients(76.1%) was clinically diagnosed as a NF2 for the bilateral vestibular schwannomas, and 34 patients(73.9%) had intracranial meningiomas, and 37 patients(80.4%) had spinal lesions at the time of diagnosis. Eleven patients were genetically confirmed as NF2. For vestibular schwannomas, 16 patients had surgical resection and 20 patients took gamma-knife surgery as a primary or secondary treatment. For intracranial meningiomas, 16 patients had surgical resection, 13 patients took gamma-knife surgery, and 15 patients took conventional radiation therapy as a primary or secondary treatment. Ventriculo-peritoneal shunt due to increased intracranial pressure was performed in 4 patients(8.7%). For spinal lesions, 16 patients had surgical resection. CONCLUSION NF2 is a disease group requiring a multidisciplinary team approach. The key to treatment is to set and implement a treatment sequence and method tailored to each patient. For this, an overall understanding of the disease and the treatment options is essential.