Misdiagnosis Rate Research Articles

Analysis of EEG characteristics in Alzheimer’s disease

Alzheimer’s disease (AD) brings a heavy burden to society and families because of the high misdiagnosis rate, prevalence rate, and disability rate. At present, the treatment of AD still faces great challenges. Early intervention can effectively delay the disease progression and improve the burden of symptom care. However, the key to early intervention is accurately identifying and assessing the disease. The examination is difficult to be widely used because of its high invasive cost. There is an urgent need for a simple, convenient, and non-invasive diagnosis of AD. Studies have shown that the brain number of AD patients is slower than that of healthy people, and the complexity of EEG is disturbed. Clinical practice has proved that the specificity and sensitivity of α, β, δ, θ absolute power and α/θ absolute power ratio, especially θ absolute power and α/θ absolute power ratio in the model, are important electrophysiological indexes for AD diagnosis EEG. Non-invasive and economical electrophysiological techniques can characterize and identify the EEG characteristics of AD patients from different angles. They are relatively simple to implement, and patients have a high degree of cooperation. Therefore, EEG, as a diagnostic method of AD, can improve the accuracy of differential diagnosis and early detection of AD and quantify the severity of the disease. It has the advantage of non-invasive convenience and price, which is of great significance for early diagnosis of AD.

Applying the 2022 optic neuritis criteria to noninflammatory optic neuropathies with optic nerve T2-hyperintensity: an observational study.

Recent diagnostic criteria for optic neuritis include T2-hyperintensity of the optic nerve (ON), even without associated contrast enhancement. However, isolated ON-T2-hyperintensity is a nonspecific finding found in any optic neuropathy or severe retinopathy. We applied the 2022 optic neuritis diagnostic criteria to a cohort of patients with noninflammatory optic neuropathy and ON-T2-hyperintensity in at least one eye, to assess the rate of optic neuritis misdiagnosis using these criteria. Retrospective study of consecutive patients who underwent brain/orbit MRI with/without contrast between 07/01/2019 and 06/30/2022. Patients with ON-T2-hyperintensity in at least one eye were included. The 2022 optic neuritis diagnostic criteria were applied to patients with noninflammatory optic neuropathies who had an ophthalmologic examination available for review. Of 150 patients included, 85/150 had compressive optic neuropathy; 32/150 had glaucoma; 12/150 had papilledema; 8/150 had hereditary (3), radiation-induced (3), nutritional (1), traumatic (1) optic neuropathies (none fulfilled the criteria); 13/150 had ischemic optic neuropathy and 4 fulfilled the criteria as definite optic neuritis due to contrast enhancement of the ON head. Seven additional patients would have satisfied the diagnostic criteria if red flags for alternative diagnoses had been overlooked. The application of the 2022 optic neuritis diagnostic criteria in patients with noninflammatory optic neuropathy and ON-T2-hyperintensity in at least one ON resulted in misdiagnosis of optic neuritis in only 4 patients because of ON head enhancement, all with nonarteritic anterior ischemic optic neuropathy. Neuro-ophthalmologic evaluation and exclusion of the ON head as a location in the MRI criteria would have prevented optic neuritis misdiagnosis in our study.

Challenging pitfalls in frozen section pathology: a case of mandible ghost cell odontogenic carcinoma and the literature review

BackgroundGhost cell odontogenic carcinoma (GCOC) is a rare malignancy characterized by the presence of ghost cells, preferably in the maxilla. Only slightly more than 50 case reports of GCOC have been documented to date. Due to the rarity of this tumor and its nonspecific clinical criteria, there is a heightened risk of misdiagnosis in clinical examination, imaging findings, and pathology interpretation.Case presentationA 50-year-old male patient presented to the hospital due to experiencing pain in his lower front teeth while eating for the past 2 months. Upon examination, a red, hard, painless mass was found in his left lower jaw, measuring approximately 4.0 cm × 3.5 cm. Based on the malignant histological morphology of the tumor and the abundant red-stained keratinized material, the preoperative frozen section pathology misdiagnosed it as squamous cell carcinoma (SCC). The surgical resection specimen pathology via paraffin section revealed that the tumor was characterized by round-like epithelial islands within the fibrous interstitium, accompanied by a large number of ghost cells and some dysplastic dentin with infiltrative growth. The malignant components displayed marked heterogeneity and mitotic activity. Additionally, a calcified cystic tumor component of odontogenic origin was observed. Hemorrhage, necrosis, and calcifications were present, with a foreign body reaction around ghost cells. Immunoreactivity for β-catenin showed strong nuclear positivity in tumor cells, while immunostaining was completely negative for p53. The Ki67 proliferation index was approximately 30–40%. The tumor cells exhibited diffuse CK5/6, p63, and p40 immunoreactivity, with varying immunopositivity for EMA. Furthermore, no BRAFV600E mutation was identified by ARMS-PCR. The final pathology confirmed that the tumor was a mandible GCOC.ConclusionWe have reported and summarized for the first time the specific manifestations of GCOC in frozen section pathology and possible pitfalls in misdiagnosis. We also reviewed and summarized the etiology, pathological features, molecular characteristics, differential diagnosis, imaging features, and current main treatment options for GCOC. Due to its rarity, the diagnosis and treatment of this disease still face certain challenges. A correct understanding of the pathological morphology of GCOC, distinguishing the ghost cells and the secondary stromal reaction around them, is crucial for reducing misdiagnosis rates.

Atypical Presentations of Multiple Sclerosis in an Adult Neurogenetics Clinic: Rates of Misdiagnosis and Red Flag Features (P1-6.005)

Atypical Presentations of Multiple Sclerosis in an Adult Neurogenetics Clinic: Rates of Misdiagnosis and Red Flag Features (P1-6.005)

Comparing Essential Tremor with and without Soft Dystonic Signs and Tremor Combined with Dystonia: The TITAN Study.

Tremor disorders remain as clinical diagnoses and the rate of misdiagnosis between the commonest non-parkinsonian tremors is relatively high. To compare the clinical features of Essential Tremor without other features (pure ET), ET plus soft dystonic signs (ET + DS), and tremor combined with dystonia (TwD). We compared the clinical features of patients with pure ET, ET + DS, and TwD enrolled in The ITAlian tremor Network (TITAN). Linear regression models were performed to determine factors associated with health status and quality of life. Three-hundred-eighty-three patients were included. Sex distribution was significantly different between the groups with males being more represented in pure ET and females in TwD. The initial site of tremor was different between the groups with about 40% of TwD having head tremor and ET + DS unilateral upper limb tremor at onset. This pattern mirrored the distribution of overt dystonia and soft dystonic signs at examination. Sensory trick, task-specificity, and position-dependence were more common, but not exclusive, to TwD. Pure ET patients showed the lowest degree of alcohol responsiveness and ET + DS the highest. Midline tremor was more commonly encountered and more severe in TwD than in the other groups. Regression analyses demonstrated that tremor severity, sex, age, and to a lesser degree the variable "group", independently predicted health status and quality of life, suggesting the existence of other determinants beyond tremor. Pure ET and TwD manifest with a phenotypic overlap, which calls for the identification of diagnostic biomarkers. ET + DS shared features with both syndromes, suggesting intra-group heterogeneity.

Heart Disease Prediction using SVM

Diagnosing and predicting the outcome of cardiovascular disease are essential tasks in medicine that help ensure patients receive accurate classification and treatment from cardiologists. The use of machine learning in the healthcare sector has grown due to its ability to identify patterns in data. By applying machine learning techniques to classify the presence of cardiovascular diseases, it's possible to decrease the rate of misdiagnosis. This study aims to create a model capable of accurately forecasting cardiovascular diseases to minimize the deaths associated with these conditions. In this paper, two types of SVM model such as linear SVM and polynomial SVM is used. Accuracy, precision, recall and F1 score has been evaluated for comparing linear SVM and polynomial SVM. Polynomial SVM provides better accuracy than linear SVM.

The Application Value of Ultrasound Imaging in the Differential Diagnosis of Benign and Malignant Breast Nodules of BI-RADS 3 and Above

Objective: To explore the diagnostic value of ultrasound imaging for breast nodules of breast imaging-reporting and data system (BI-RADS) category 3 and above. Methods: From June 2021 to July 2022, 163 patients with breast nodules of BI-RADS 3 or above were selected as the research subjects. After pathological diagnosis, 24 cases were malignant breast nodules of BI-RADS 3 or above, while 139 cases were benign breast nodules of BI-RADS 3 or above. The diagnosis rate of malignant and benign breast nodules of BI-RADS 3 or above, including 95% CI, was observed and analyzed. Results: The malignant and benign detection rates of conventional ultrasound were 88.63% and 75.00%, respectively, and the malignant and benign detection rates of ultrasound imaging were 93.18% and 87.50%, respectively, with 95% CIs greater than 0.7. Conclusion: Ultrasound imaging can help improve the diagnostic accuracy of benign and malignant breast nodules of BI-RADS 3 and above and reduce the misdiagnosis rate.

Langerhans Cell Histiocytosis Following Hodgkin Lymphoma in Adult: A Case Report

Introduction: Langerhans cell histiocytosis (LCH) is a very rare disorder, and usually considered a disease of childhood. It is rarely observed in adults. Its etiopathogenesis remains to be elucidated. One possible etiological cause is a reactive proliferation of Langerhans cells following chemotherapy or radiotherapy for Hodgkin's disease (HD). Eosinophilic granuloma is the benign accumulation of histiocytes located primarily in the bones, but which also affects other organs, including the skin, lungs and lymph nodes. The time interval between LCH occurrence and previous lymphoma is variable. The occurrence of eosinophilic granuloma in a patient with Hodgkin disease has rarely been reported. Objective: This case report aimed to describe the clinic-pathological, histopathological, immunohistochemical and other features of LCH and to analyze LCH clinical features for improving diagnosis and decreasing misdiagnosis rate. Case report: In this report, a 38-year-old female presented with LCH twenty-two months after diagnosis and treatment of nodular sclerosing HD. The patient presented with generalized lymphadenopathy and general weakness. The Langerhans cells diffusely infiltrated in the inguinal lymph node and the tumor cells were positive for CD1a and S-100 expression. The patient received chemotherapy and show complete remission. Conclusion: LCH has a very rare occurrence following HD in adult. The definitive diagnosis depends on pathological biopsy and immunohistochemistry. Although specific therapeutic approach hasn't been well established, combined chemotherapy for multisystem lesions and surgical operation or radiotherapy for unifocal lesions may improve the therapy.

Influence of the number of washings for embryos on non-invasive preimplantation chromosome screening results.

To explore the effect of varying numbers of embryo washings prior to blastocyst formation in non-invasive preimplantation chromosome screening (NICS) on the accuracy of NICS results. In this study, 68 blastocysts from preimplantation genetic testing (PGT)-assisted pregnancy were collected at our institution. On the fourth day of embryo culture, the embryos were transferred to a new medium for blastocyst culture and were washed either three times (NICS1 group) or ten times (NICS2 group). A trophectoderm (TE) biopsy was performed on the blastocysts, and the corresponding embryo culture media were collected for whole genome amplification (WGA) and high-throughput sequencing. The success rate of WGA was 100% (TE biopsy), 76.7% (NICS1 group), and 89.5% (NICS2 group). The success rate of WGA in embryo medium on days 5 and 6 of culture was 75.0% (33/44) and 100% (24/24), respectively. Using TE as the gold standard, the karyotype concordance rate between the results of the NICS1 and NICS2 groups' embryo culture medium samples and TE results was 43.5% (10/23) and 73.5% (25/34), respectively. The sensitivity and specificity of detecting chromosomal abnormalities were higher in the NICS2 group than in the NICS1 group when TE was used (83.3% vs 60.0%; 62.5% vs 30.8%, respectively). The false-positive rate and false-negative rate (i.e., misdiagnosis rate and missed diagnosis rate, respectively) were lower in the NICS2 group than in the NICS1 group (37.5% vs 69.2%; 16.7% vs 40.0%, respectively). The NICS yielded favorable results after ten washings of the embryos. These findings provide a novel method for lowering the amount of cell-free DNA contamination from non-embryonic sources in the medium used for embryo development, optimizing the sampling procedure and improving the accuracy of the NICS test.

Evaluation of metabolomics-based urinary biomarker models for recognizing major depression disorder and bipolar disorder

BackgroundMajor depressive disorder (MDD) and bipolar disorder (BD) are psychiatric disorders with overlapping symptoms, leading to high rates of misdiagnosis due to the lack of biomarkers for differentiation. This study aimed to identify metabolic biomarkers in urine samples for diagnosing MDD and BD, as well as to establish unbiased differential diagnostic models. MethodsWe utilized a metabolomics approach employing ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) to analyze the metabolic profiles of urine samples from individuals with MDD (n = 50), BD (n = 12), and healthy controls (n = 50). The identification of urine metabolites was verified using MS data analysis tools and online metabolite databases. ResultsTwo diagnostic panels consisting of a combination of metabolites and clinical indicators were identified—one for MDD and another for BD. The discriminative capacity of these panels was assessed using the area under the receiver operating characteristic (ROC) curve, yielding an area under the curve (AUC) of 0.9084 for MDD and an AUC value of 0.9017 for BD. ConclusionsHigh-resolution mass spectrometry-based assays show promise in identifying urinary biomarkers for depressive disorders. The combination of urine metabolites and clinical indicators is effective in differentiating healthy controls from individuals with MDD and BD. The metabolic pathway indicating oxidative stress is seen to significantly contribute to depressive disorders.

Machine-based learning of multidimensional data in bipolar disorder - pilot results.

Owing to the heterogenic picture of bipolar disorder, it takes approximately 8.8 years to reach a correct diagnosis. Early recognition and early intervention might not only increase quality of life, but also increase life expectancy as a whole in individuals with bipolar disorder. Therefore, we hypothesize that implementing machine learning techniques can be used to support the diagnostic process of bipolar disorder and minimize misdiagnosis rates. To test this hypothesis, a de-identified data set of only demographic information and the results of cognitive tests of 196 patients with bipolar disorder and 145 healthy controls was used to train and compare five different machine learning algorithms. The best performing algorithm was logistic regression, with a macro-average F1-score of 0.69 [95% CI 0.66-0.73]. After further optimization, a model with an improved macro-average F1-score of 0.75, a micro-average F1-score of 0.77, and an AUROC of 0.84 was built. Furthermore, the individual amount of contribution per variable on the classification was assessed, which revealed that body mass index, results of the Stroop test, and the d2-R test alone allow for a classification of bipolar disorder with equal performance. Using these data for clinical application results in an acceptable performance, but has not yet reached a state where it can sufficiently augment a diagnosis made by an experienced clinician. Therefore, further research should focus on identifying variables with the highest amount of contribution to a model's classification.

Transforming Healthcare: A Comprehensive Approach to Mitigating Bias and Fostering Empathy through AI-Driven Augmented Reality

The integration of Artificial Intelligence (AI) into Augmented Reality (AR) for medical applications is propelled by the aim to address evident healthcare disparities. Certain communities have encountered disparities in medical diagnoses, exemplified by Black individuals exhibiting a 2.4 times higher likelihood of schizophrenia diagnosis compared to their white counterparts (Faber et al., 2023). These disparities often arise from structured interview assessments overlooking cultural nuances, resulting in increased misdiagnosis rates. This study leverages AI and AR to develop unbiased diagnostic tools and enhance empathy in healthcare professionals' training. Uniquely prioritizing the reduction of biased language and the fostering of empathy through AI-driven Natural Language Processing (NLP) and AI-driven virtual patients, the research aims to enhance diagnostic accuracy while promoting cultural sensitivity among healthcare professionals. Aligned with broader goals of achieving equitable healthcare and reducing disparities, the evaluation involves pre- and post-training assessments to measure language improvements and empathy enhancements. Successful implementation could lead to a more equitable healthcare landscape, fostering trust in AI-driven systems and ensuring fairer medical care for diverse communities.

Evaluation of multi-parameter MRI in preoperative staging of endometrial carcinoma

BackgroundEndometrial carcinoma (EC) is a prevalent gynecological malignancy, necessitating accurate preoperative staging for effective treatment planning. This study explores the application value of multi-parameter MRI in diagnosing and staging endometrial cancer. MethodsSeventy-six patients diagnosed with endometrial cancer underwent 3.0 T pelvic MRI within two weeks before surgery. Imaging data were analyzed based on FIGO clinical staging criteria. The study assessed the sensitivity, specificity, positive predictive value, and negative predictive value of MRI for each stage. ResultsPostoperative pathology confirmed 71 cases of endometrial adenocarcinoma, 3 serous adenocarcinoma, and 2 clear cell carcinomas. MRI staging showed a high consistency (Kappa value = 0.786) with postoperative pathology. The overall accuracy of MRI diagnosis was 86.8%. Sensitivity and specificity varied for each stage: IA (91.3%, 96.2%), IB (88.6%, 93.8%), II (97.4%, 89.2%), and III (84.2%, 100%). ConclusionWhile there was a slight misdiagnosis rate, the overall accuracy of preoperative MRI for endometrial cancer was high, aiding in precise diagnosis and clinical staging. MRI effectively identified myometrial infiltration, cervical involvement, paracentral extension, and lymph node metastasis. Further research with larger sample sizes is recommended for enhanced reliability.

Detection of hidden pediatric elbow fractures in X-ray images based on deep learning

BackgroundPediatric elbow fractures pose a common diagnostic challenge in the field of pediatric orthopedics. Accurate diagnosis of these fractures through X-ray images is crucial in clinical practice, often requiring highly specialized medical imaging experts. In recent years, the development of deep learning technology has provided a new approach to address this issue. This study aims to explore the potential of using deep convolutional neural network (DCNN) technology, specifically the DenseNet-201 model, for diagnosing pediatric elbow fractures from X-ray images. MethodsThis study utilized X-ray images from 1370 pediatric patients, obtained from multiple healthcare centers. The images were carefully selected based on specific inclusion and exclusion criteria, ensuring a diverse representation of elbow fractures. Each image underwent preprocessing, including normalization and resizing, to standardize the input for the DenseNet-201 model. The DensenNet-201 model was trained using a split of 70% for training and 30% for validation. Key training parameters included a learning rate of 0.001, 50 epochs, and a batch size of 32. Advanced techniques like dropout and data augmentation were employed to enhance the model's ability to generalize and prevent overfitting. The performance of the models was evaluated using metrics such as accuracy, sensitivity, specificity, and area under the curve (AUC). ResultsThe research findings demonstrate the outstanding performance of the DenseNet-201 model across all datasets. It achieved an accuracy of 94.1% and an AUC of 98.7% on the training dataset, indicating its robust discriminatory ability in accurately diagnosing elbow fractures. Furthermore, the model exhibited excellent sensitivity (93.2%) and specificity (94.8%) in the training dataset, indicating its ability to accurately detect positive and negative cases of elbow fractures. ConclusionThe results of this study emphasize the potential of the DenseNet-201 model as a diagnostic tool for pediatric elbow fractures. Accurate and timely diagnosis of pediatric elbow fractures is crucial for effective treatment and patient prognosis. The exceptional performance of the DenseNet-201 model, particularly in terms of sensitivity and specificity, suggests that it can assist clinical practitioners in confidently diagnosing these fractures, thereby reducing misdiagnosis rates and improving the quality of patient care. It should be noted that multiple models were compared, with DenseNet-201 demonstrating the most prominent performance.

A systemic infection involved in lung, brain and spine caused by Scedosporium apiospermum species complex after near-drowning: a case report and literature review

Scedosporium apiospermum species complex are widely distributed fungi that can be found in a variety of polluted environments, including soil, sewage, and decaying vegetation. Those opportunistic pathogens with strong potential of invasion commonly affect immunosuppressed populations However, few cases of scedosporiosis are reported in immunocompetent individuals, who might be misdiagnosed, leading to a high mortality rate. Here, we reported an immunocompetent case of systemtic infection involved in lung, brain and spine, caused by S. apiospermum species complex (S. apiospermum and S. boydii). The patient was an elderly male with persistent fever and systemtic infection after near-drowning. In the two tertiary hospitals he visited, definite diagnosis was extremely difficult. After being admitted to our hospital, he was misdiagnosed as tuberculosis infection, before diagnosis of S. apiospermum species complex infection by the metagenomic next-generation sequencing. His symptoms were alleviated after voriconazole treatment. In the present case, the details associated with its course were reported and published studies on Scedosporium spp. infection were also reviewed, for a better understanding of this disease and reducing the misdiagnosis rate.

Chinese expert consensus on the clinical diagnosis and treatment of autosomal dominant optic atrophy (2024)

Autosomal dominant optic atrophy (ADOA) primarily affects retinal ganglion cells and their axons, resulting in varying degrees of central vision loss from childhood. Due to the rarity of ADOA in clinical practice, Chinese ophthalmologists currently lack sufficient understanding of the disease and experience non-standardized diagnostic procedures and high clinical and genetic misdiagnosis rates. To address these issues, the Ophthalmology Group of China Alliance for Rare Diseases/Beijing Society of Rare Disease Clinical Care and Accessibility and the Neuro-ophthalmology Group of Ophthalmology Branch of Chinese Medical Association have established an expert panel to form consensus opinions based on extensive discussions. This consensus would enhance the knowledge and diagnostic capabilities of Chinese clinicians regarding ADOA and promote awareness of related treatment principles and genetic counseling.

Gastric metastasis from breast cancer: five cases and a single-institutional review.

Gastric metastasis from breast cancer has a high rate of misdiagnosis and missed diagnosis. Data of patients who had gastric metastasis from breast cancer were retrieved from our hospital between 2014 and 2020. The gastric metastasis from breast cancer incidence was 0.04% (5/14,169 cases of breast cancer). Four patients had invasive lobular carcinoma, and the other patient had invasive ductal carcinoma. The time from the initial diagnosis of breast cancer to the appearance of gastric metastasis ranged from 0 to 12 years. One patient's endoscopic presentation was similar to mucosal-associated lymphoid tissue lymphoma and presented with gastric mucosal congestion and edema, widened wrinkles, mixed color fading, and redness. The initial pathological diagnosis of this patient was mucosal-associated lymphoid tissue lymphoma, and breast cancer was finally confirmed by immunohistochemistry. Hormonal receptors were highly expressed in four patients with primary and metastasis lesions and were negative in one patient. Human epidermal growth factor receptor 2 was negative in all patients. Mammaglobin and GATA3 were positive in all patients. In conclusion, the gastric metastasis of breast cancer incidence rate is low, and misdiagnosis can lead to insufficient or excessive treatment. Multiple biopsies and immunohistochemistry should be performed to diagnose gastric metastasis of breast cancer.

Research on the Application of Semantic Network in Disease Diagnosis Prompts Based on Medical Corpus

Portion of the causes of medical errors in outpatient clinics are incorrect treatment resulting from misdiagnosis. Misdiagnosis between diseases is often caused by similar and indistinguishable symptoms. Currently, disease knowledge and related symptom words that are prone to misdiagnosis are scattered in various medical literature or open online databases. Therefore, it is possible to merge symptom words of related diseases, build an ontology based on the semantic relationship between symptoms, and associate the association between diseases and symptoms. This project finally established the "Disease Symptoms" Semantic Network (DSSN). Using DSSN as a basic data set can serve as prompts for diseases that are easily misdiagnosed, assisting doctors in accurately diagnosing diseases. This plays a significant role in reducing the misdiagnosis rate.

Advancing the Diagnosis of Acute Lymphoblastic Leukemia with Hybrid Neural Networks

Acute Lymphoblastic Leukemia (ALL) is a malignancy of B or T lymphoblasts characterized by the uncontrolled replication of abnormal cells in the blood. The difficulty in diagnosing ALL arises from its visual similarity with other cells (HEM) in the blood, and misdiagnosis rates are high with other diseases. Thus, we seek an alternate solution to help medical professionals with the diagnosis of ALL. Recent solutions have utilized hybrid models that have delivered better performance than prior models, so we utilize a hybrid architecture unused for ALL detection. Our proposed architecture consists of EfficientNetB0 as our feature extractor and XGBoost classifier for its gradient boosting framework to perform a binary classification between ALL and HEM cells. The model maintains an accuracy of 85% when diagnosing ALL, proving the effectiveness of a data-driven approach for diagnosis. In the future, our model could be trained with other ALL datasets in order to become more versatile. Our work can assist doctors in providing an accurate and efficient diagnosis of leukemia, allowing for early intervention of the disease.

Quantitative Assessment of Fungal Biomarkers in Clinical Samples via an Interface-Modulated Optical Fiber Biosensor.

Invasive fungal infections pose a significant public health threat. The lack of precise and timely diagnosis is a primary factor contributing to the significant increase in patient mortality rates. Here, an interface-modulated biosensor utilizing an optical fiber for quantitative analysis of fungal biomarkers at the early stage of point-of-care testing (POCT), is reported. By integrating surface refractive index (RI) modulation and plasmon enhancement, the sensor to achieve high sensitivity in a directional response to the target analytes, is successfully optimized. As a result, a compact fiber-optic sensor with rapid response time, cost-effectiveness, exceptional sensitivity, stability, and specificity, is developed. This sensor can successfully identify the biomarkers of specific pathogens from blood or other tissue specimens in animal models. It quantifies clinical blood samples with precision and effectively discriminates between negative and positive cases, thereby providing timely alerts to potential patients. It significantly reduces the detection time of fungal infection to only 30 min. Additionally, this approach exhibits remarkable stability and achieves a limit of detection (LOD) three orders of magnitude lower than existing methods. It overcomes the limitations of existing detection methods, including a high rate of misdiagnosis, prolonged detection time, elevated costs, and the requirement for stringent laboratory conditions.