Method For Diagnosis Research Articles

A survey of Autoencoder and Convolutional Neural Network Based Methods for Fault Diagnosis

Deep learning technology, with its exceptional nonlinear feature extraction capability, has been gradually accepted and widely adopted by the industry. This paper reviews the fault diagnosis techniques utilizing deep learning, emphasizing an in-depth analysis of two main deep learning models that are extensively applied in the current fault diagnosis domain: Autoencoder (AE) and Convolutional Neural Network(CNN). In addition, we also discuss the specific application cases of both algorithms, and introduce their specific principles of implementation. Through these in-depth analysis, it is intended to provide readers with the application overview and technical progress of deep learning technology in the field of fault diagnosis, so as to offer reference and enlightenment for related research.

Enhanced multistage deep learning for diagnosing anterior disc displacement in temporomandibular joint using magnetic resonance imaging.

This study aimed to propose a new method for the automatic diagnosis of anterior disc displacement of the temporomandibular joint (TMJ) using magnetic resonance imaging (MRI) and deep learning. By employing a multistage approach, the factors affecting the final result can be easily identified and improved. This study introduces a multistage automatic diagnostic technique using deep learning. This process involves segmenting the target from MR images, extracting distance parameters, and classifying the diagnosis into three classes. MRI exams of 368 TMJs from 204 patients were evaluated for anterior disc displacement. In the first stage, five algorithms were used for the semantic segmentation of the disc and condyle. In the second stage, 54 distance parameters were extracted from the segments. In the third stage, a rule-based decision model was developed to link the parameters with the expert diagnosis results. In the first stage, DeepLabV3+ showed the best result (95% Hausdorff distance, Dice coefficient, and sensitivity of 6.47 ± 7.22, 0.84 ± 0.07, and 0.84 ± 0.09, respectively). This study used the original MRI exams as input without preprocessing and showed high segmentation performance compared with that of previous studies. In the third stage, the combination of SegNet and a random forest model yielded an accuracy of 0.89 ± 0.06. An algorithm was developed to automatically diagnose TMJ-anterior disc displacement using MRI. Through a multistage approach, this algorithm facilitated the improvement of results and demonstrated high accuracy from more complex inputs. Furthermore, existing radiological knowledge was applied and validated.

A transformer-based weakly supervised computational pathology method for clinical-grade diagnosis and molecular marker discovery of gliomas

A transformer-based weakly supervised computational pathology method for clinical-grade diagnosis and molecular marker discovery of gliomas

SCAE-Stacked Convolutional Autoencoder for Fault Diagnosis of a Hydraulic Piston Pump with Limited Data Samples.

Deep learning (DL) models require enormous amounts of data to produce reliable diagnosis results. The superiority of DL models over traditional machine learning (ML) methods in terms of feature extraction, feature dimension reduction, and diagnosis performance has been shown in various studies of fault diagnosis systems. However, data acquisition can sometimes be compromised by sensor issues, resulting in limited data samples. In this study, we propose a novel DL model based on a stacked convolutional autoencoder (SCAE) to address the challenge of limited data. The innovation of the SCAE model lies in its ability to enhance gradient information flow and extract richer hierarchical features, leading to superior diagnostic performance even with limited and noisy data samples. This article describes the development of a fault diagnosis method for a hydraulic piston pump using time-frequency visual pattern recognition. The proposed SCAE model has been evaluated on limited data samples of a hydraulic piston pump. The findings of the experiment demonstrate that the suggested approach can achieve excellent diagnostic performance with over 99.5% accuracy. Additionally, the SCAE model has outperformed traditional DL models such as deep neural networks (DNN), standard stacked sparse autoencoders (SSAE), and convolutional neural networks (CNN) in terms of diagnosis performance. Furthermore, the proposed model demonstrates robust performance under noisy data conditions, further highlighting its effectiveness and reliability.

Prevalence, risk factors, and outcomes of dysphagia after stroke: a systematic review and meta-analysis.

Dysphagia is a common complication after stroke, which not only brings adverse outcomes but also greatly affects the quality of life of patients. At present, there is no systematic review or meta-analysis to comprehensively evaluate the epidemiological characteristics of post-stroke dysphagia (PSD). A systematic review of the prevalence, risk factors, and prognosis of PSD is essential. Through 31 December 2022, a comprehensive literature search was performed for observational studies related to PSD. Five databases were retrieved. Random-effects models were used to estimate the pooled prevalence, odds ratio (OR), and 95% CIs. A total of 34 studies were included, and the results showed that the overall prevalence of PSD was 46.6% (95% CI, 0.405-0.528). The prevalence of dysphagia in ischemic stroke and hemorrhagic stroke was 43.6% (95% CI 0.370-0.501) and 58.8% (95% CI 0.519-0.654), respectively. The prevalence of PSD in Africa was 49.4% (95% CI, 0.196-0.792), in Asia was 40.1% (95% CI, 0.348-0.454), in Europe was 45.8% (95% CI, 0.327-0.590), in North America was 44.3% (95% CI, 0.370-0.517), in South America was 57.5% (95% CI, 0.441-0.708), and in Oceania was 64.1% (95%CI, 0.558, 0.724). In risk factor analysis, hypertension, previous stroke, and atrial fibrillation were significantly associated with the occurrence of PSD, pooled OR = 1.179 [(95% CI, 1.002-1.386), p < 0.05], pooled OR = 1.514 [(95% CI, 1.204-1.905), p < 0.001], and pooled OR = 1.980 [(95% CI, 1.580-2.481), p < 0.001]. In outcome studies, the prevalence of aphasia and dysarthria in PSD was 35.6% (95% CI, 0.213-0.499) and 54.5% (95% CI, 0.293-0.798), respectively. The prevalence of respiratory tract infection was 27.1% (95%CI, -0.038-0.579), and the prevalence of pneumonitis was 32.1% (95% CI, 0.224-0.418). Persistence of dysphagia at discharge and at 1 month was 74.5% (95% CI, 0.621-0.869) and 50.9% (95% CI, 0.142-0.876), respectively. Mortality rates for PSD patients during admission and discharge at 1 month, 3 months, and 1 year were 11.8% (95% CI, 0.083-0.152), 26.5% (95% CI, 0.170-0.359), 25.7% (95% CI, 0.19-0.324), and 31.3% (95% CI, 0.256-0.369), respectively. This study found that the overall prevalence of PSD was 46.6%. Prevalence is most influenced by the diagnosis method. Hypertension, history of stroke, atrial fibrillation, patient age, and stroke severity were risk factors significantly associated with PSD. The prevalence of aphasia, dysarthria, respiratory tract infection, and pneumonitis in PSD patients is 2-4 times that of patients without PSD.Systematic review registration: www.crd.york.ac.uk/PROSPERO, PROSPERO, CRD42021252967.

An Intelligent diagnosis method for rolling bearings based on Ghost module and adaptive weighting module

An Intelligent diagnosis method for rolling bearings based on Ghost module and adaptive weighting module

Simulation data-driven fault diagnosis method for metro traction motor bearings under small samples and missing fault samples

Abstract Traction motor bearings are crucial for guaranteeing the safe operation of metro vehicles. However, in the metro traction motor bearing fault diagnosis, there are usually problems of small samples and missing fault samples, leading to inaccurate results. Therefore, a novel bearing fault diagnosis method utilizing a track-vehicle-bearing coupled dynamic model and the IDCG-MAMCNN model is proposed in this paper. The IDCG-MAMCNN model combines an improved deep convolutional generative adversarial network (IDCGAN) with a multi-scale convolutional neural network with mixed attention (MA-MCNN). Specifically, simulation data is first provided by the coupled dynamic model to supplement missing fault samples. Secondly, the IDCGAN, along with a training method that involves pre-training models with simulation samples and fine-tuning models with experimental samples, is introduced to generate high-quality samples and augment experimental samples under small samples. Lastly, the MA-MCNN serves as the classification model, trained with the augmented dataset comprising experimental, simulation, and generated samples. The fault diagnosis performance of the proposed method is evaluated on the experimental samples of two bearing datasets under small samples and various conditions of missing fault samples. It has been demonstrated by the experimental results that the proposed method exhibits robust fault diagnosis performance and generates high-quality samples under small samples and missing fault samples. Furthermore, the proposed method showcases its adaptability to different operation speeds.

Gearbox Fault Diagnosis Method in Noisy Environments Based on Deep Residual Shrinkage Networks.

Gearbox fault diagnosis is essential in the maintenance and preventive repair of industrial systems. However, in actual working environments, noise frequently interferes with fault signals, consequently reducing the accuracy of fault diagnosis. To effectively address this issue, this paper incorporates the noise attenuation of the DRSN-CW model. A compound fault detection method for gearboxes, integrated with a cross-attention module, is proposed to enhance fault diagnosis performance in noisy environments. First, frequency domain features are extracted from the public dataset by using the fast Fourier transform (FFT). Furthermore, the cross-attention mechanism model is inserted in the optimal position to improve the extraction and recognition rate of global and local fault features. Finally, noise-related features are filtered through soft thresholds within the network structure to efficiently mitigate noise interference. The experimental results show that, compared to existing network models, the proposed model exhibits superior noise immunity and high-precision fault diagnosis performance.

Point-of-Care Testing of Whole Blood for Liver Injury Auxiliary Diagnosis with Biothiols Activable Chemiluminescent Probe.

Liver injury significantly affects a patient's health and quality of life. However, timely and convenient diagnosis of this disease via whole blood detection remains challenging due to the lack of user-friendly and fast readout blood test methods. Herein, we developed such a method for the swift auxiliary diagnosis of liver injury via whole blood detection using a customed point-of-care testing (POCT) system consisting of a biothiols-activatable chemiluminescent probe and a hand-held POCT device. Biothiols served as the target to build the activable chemiluminescence probe due to their abnormal level in liver injury. Compared with fluorescent and electrical POCTs, this method is more convenient and has strong universality. By incorporating cyclodextrin via host-guest chemistry, we intensified chemiluminescence while mitigating chemical hemolysis caused by the dissolution of organic molecules, making this system suitable for whole blood analysis. Preliminary assessments in aqueous solutions, living cells, and mouse models confirmed its sensitivity, reliability, and feasibility. Simply mixing blood with the probe for 30 min yielded a clear signal readout within 15 s on the POCT device. Utilizing this portable detector, the reduced biothiol level was tested in 18 liver injury patient blood samples, and the results were similar to those measured by a commercial kit and in vivo imaging system. Thus, this work provides a universal platform for the fast and convenient detection of other biomarkers in whole blood samples and opens up possibilities for the rapid clinical diagnosis of diseases, enabling patients to conduct home self-examinations with ease.

Diffuse large B-cell lymphoma in the uterus with unexpected manifestations: a case report

BackgroundLymphoid neoplasm is a common disease, arising from lymphoid cells. It is divided into Hodgkin lymphoma and non-Hodgkin lymphoma. Non-Hodgkin lymphoma can be intranodular or extranodular, which happens in 25% of primary cases. The most common locations of extranodular non-Hodgkin lymphoma are the skin and gastrointestinal tract. The genital tract is a rare location; most lymphomas arise from the cervix and vagina, while the uterine corpus is an extremely rare location. In our case, the patient was diagnosed with primary extranodular non-Hodgkin lymphoma in different locations of her genital tract.Case presentationA 48-year-old nonparous Syrian woman complained of diffuse abdominal pain, fatigue, debility, high fever, vomiting, and urinary retention for a week. The last menstrual period of the patient was 5 years previously. The physical examination showed periodic abdominal pain with severe fatigue and increased abdominal size. The laboratory investigations were within normal limits except for a low level of hemoglobin and a high level of cancer antigen 125. The radiological investigations showed a uterine sizable lobulated mass with irregular borders and high and heterogeneous density, extending to the right and left ovaries, enlargement lymph nodes around the abdominal aortic and right iliac vessels, and severe right pleural effusion with right inferior lobe atelectasis. A total hysterectomy and oophorectomy were done. The histopathological examination showed that the patient had non-Hodgkin lymphoma (primary tumor).ConclusionPrimary non-Hodgkin lymphoma in the female genital tract is an extremely rare disease. Fast diagnosis and treatment can improve the outcomes, so this differential diagnosis should be in our minds even in the absence of systematic manifestations of lymphoma. More studies are needed to explain the pathology of this disease and to put guidelines that determine the perfect methods for diagnosis and treatment.

Intelligent Diagnosis Method for Typical Co-frequency Vibration Faults of Rotating Machinery Based on SAE and Ensembled ResNet-SVM

Intelligent fault diagnosis is an important method in rotating machinery fault diagnosis and equipment health management. To deal with co-frequency vibration faults, a type of typical fault in rotating machinery, this paper proposes a fault diagnosis method based on the stacked autoencoder (SAE) and ensembled ResNet-SVM. Furthermore, the time- and frequency-domain features of several co-frequency vibration faults are summarized based on the mechanism analysis and calculated using actual vibration data. To realize and validate the high-precision diagnosis method of rotating equipment with co-frequency faults proposed in this study, the following three criteria are required: First, to improve the effectiveness and robustness of the ensembled model and the sliding window using data augmentation, adding noise, autoencoder (AE) and SAE methods are analyzed in terms of principle and practical effects. Second, ResNet is used as the feature extractor for the ensembled ResNet-SVM model. Feature extraction is carried out twice, and the extracted co-frequency fault features are more comprehensive. Finally, the data augmentation method and ensemble ResNet-SVM are combined for fault diagnosis and compared with other methods. The experimental results show that the accuracy of the proposed method can exceed 99.9%.

Lightweight bearing fault diagnosis method based on cross-scale learning transformer under imbalanced data*

Abstract Due to the limited amount of failure data in rolling bearing faults, traditional fault diagnosis models encounter challenges such as low diagnostic accuracy and efficiency when dealing with imbalanced data. Additionally, many fault diagnosis models are overly complex and demand high computational resources. To address these issues, a lightweight bearing fault diagnosis method based on cross-scale learnable transformer (CSLT) is proposed for imbalanced data. For difficult-to-classify samples, a learnable generalized focal loss function is defined. The learnable parameters are employed to increase its flexibility, it better addresses the issue of bearing fault diagnosis under imbalanced data conditions. Then, a multi-head broadcasted self-attention mechanism is designed by capturing critical local features of the signal through one-dimensional convolution operations, which not only improves feature extraction capability but also reduces computational complexity. Finally, a dynamic label prediction pruning module is developed to trim redundant labels, which helps in lightening the model and enhancing both feature extraction and diagnostic efficiency. The experimental results demonstrate that the proposed diagnosis method exhibits superior diagnostic precision and efficiency by comparing with other methods.

Choriocarcinoma of the ovaries: the role of molecular-genetic testing in the differential diagnosis of gestational and non-gestational forms

The treatment approach for gestational and non-gestational ovarian choriocarcinoma has several differences, and their differential diagnosis requires special attention. The implementation of molecular-genetic testing which determines the presence of paternal genetic material in the tumor allows for a reliable determination of the origin of ovarian choriocarcinoma. The presented clinical case demonstrates the importance of this method in the differential diagnosis of gestational and non-gestational forms of ovarian choriocarcinoma.

Unsupervised method for detecting surface defects in steel based on joint optimization of pseudo-labeling and clustering

Abstract Advances in the field of measurement science and technology have improved the detection of defects in industrial production. One of the key challenges in steel plate surface defect detection is the need to quickly detect a small number of defects in an overwhelmingly defect-free sample. Unlike supervised learning, which relies heavily on precise sample labeling, unsupervised learning leverages its inherent learning capabilities for detection. This paper introduces an innovative method for smart steel diagnosis, integrating joint optimization of feature extraction and clustering. The proposed approach merges mini-batch K-Means clustering with a feature extraction network to acquire pseudo-label information for current images. It employs a multi-view transformation strategy, enabling classification through the optimized feedback from pseudo-labels. This method allows the network to self-optimize the distinction of image features through backpropagation. The method exhibits a mere 4% classification failure rate for steel surface images. This significant reduction in additional data processing requirements enhances the inspection system's efficiency and accuracy. Furthermore, the versatility of this method extends beyond steel defect diagnosis. It holds potential for application in various engineering domains, particularly in scenarios characterized by data imbalance.

Application of metagenomic next-generation sequencing in the etiological diagnosis of refractory pneumonia in children.

Metagenomic next-generation sequencing (mNGS) was used to analyze the etiological distribution of refractory pneumonia in children. We compared its efficacy in pathogen diagnosis against traditional methods to provide a basis for clinical adjustment and treatment. A total of 60 children with refractory pneumonia treated at the Department of Respiratory Medicine, Children's Hospital Affiliated with the Capital Institute of Paediatrics, from September 2019 to December 2021 were enrolled in this study. Clinical data (including sex, age, laboratory tests, complications, and discharge diagnosis) and lower respiratory tract specimens were collected, including bronchoalveolar lavage fluid (BALF), deep sputum, pleural effusion, lung abscess puncture fluid, traditional respiratory pathogens (culture, acid-fast staining, polymerase chain reaction, serological testing, etc.), and mNGS detection methods were used to determine the distribution of pathogens in children with refractory pneumonia and to compare the positive rate and diagnostic efficiency of mNGS and traditional pathogen detection for different types of pathogens. Among the 60 children with refractory pneumonia, 43 specimens were positive by mNGS, and 67 strains of pathogens were detected, including 20.90% (14 strains) of which were Mycoplasma pneumoniae, 11.94% (8 strains) were Streptococcus pneumoniae, 7.46% (5 strains) were cytomegalovirus, and 5.97% (4 strains) were Candida albicans. Thirty-nine strains of Mycoplasma pneumoniae (41.03%, 16 strains), Streptococcus pneumoniae (10.26%, 4 strains), Candida albicans (7.69%, 3 strains), and Aspergillus (5.13%, 2 strains) were detected using traditional methods. The positive rate of mNGS detection was 90.48%, and the positive rate of the traditional method was 61.90% (p = 0.050), especially for G+ bacteria. The positive rate of mNGS was greater than that of traditional methods (p < 0.05), but they had no significant difference in detecting G- bacteria, viruses, fungi, or Mycoplasma/Chlamydia. Among the 60 patients, 21 had mixed infections, 25 had single infections, and the other 14 had unknown pathogens. Mycoplasma pneumoniae was most common in both mixed infections and single infections. The sensitivity, specificity, positive predictive value, and negative predictive value of mNGS were 95.45, 37.50, 80.77, and 75.00%, respectively. The sensitivity, specificity, positive predictive value, and negative predictive value of the traditional methods were 72.72, 62.50, 84.21, and 45.45%, respectively. The clinical compliance of mNGS was 80.00%, and that of the traditional method was 70.00%. The sensitivity and negative predictive value of mNGS were high, and the difference in the sensitivity for detecting G+ bacteria was statistically significant (p < 0.05). However, the differences in G- bacteria, fungi, and Mycoplasma/Chlamydia were not statistically significant (p > 0.05). Due to the small sample size, statistical analysis could not be conducted on viral infections. mNGS has higher overall efficacy than traditional methods for the etiological diagnosis of refractory pneumonia in children. The application of mNGS can significantly improve the detection rate of pathogens in children with refractory pneumonia. The sensitivity and negative predictive value of mNGS for detecting G+ bacteria are greater than those of other methods, and it can exclude the original suspected pathogenic bacteria. Unnecessary antibiotic use was reduced, but there was no statistically significant difference in G- bacteria, fungi, or Mycoplasma/Chlamydia.

Fault Diagnosis Method of Bearings Based on SCSSA-VMD-MCKD

To tackle the issue of detecting early, subtle faults in rolling bearings in the presence of noise interference, the SCSSA-VMD-MCKD method is suggested. This method optimizes the Variational Mode Decomposition (VMD) and Maximum Correlated Kurtosis Deconvolution (MCKD) by integrating the sine-cosine and Cauchy Mutation Sparrow Search Algorithm (SCSSA). The approach aims to effectively diagnose faults in rolling bearings by leveraging the strengths of VMD and MCKD in noise reduction and highlighting fault frequencies. The method utilizes the SCSSA algorithm to autonomously search for parameters in both VMD and MCKD, using the EnvelopeCrest Factor Ec as a fitness function. Firstly, SCSSA is employed to optimize the decomposition mode number K and penalty factor α in the VMD algorithm. Secondly, the parameters in the MCKD algorithm are optimized, and MCKD is used for deconvolution to enhance the impulsive characteristics of the best modal component. Finally, the signal is further analyzed after deconvolution. The results demonstrate that this algorithm can effectively identify subtle fault signals in bearing signals and diagnose fault frequencies in noisy environments. The accuracy of fault diagnosis can reach nearly 99%.

Decoding depression: a comprehensive multi-cohort exploration of blood DNA methylation using machine learning and deep learning approaches

The causes of depression are complex, and the current diagnosis methods rely solely on psychiatric evaluations with no incorporation of laboratory biomarkers in clinical practices. We investigated the stability of blood DNA methylation depression signatures in six different populations using six public and two domestic cohorts (n = 1942) conducting mega-analysis and meta-analysis of the individual studies. We evaluated 12 machine learning and deep learning strategies for depression classification both in cross-validation (CV) and in hold-out tests using merged data from 8 separate batches, constructing models with both biased and unbiased feature selection. We found 1987 CpG sites related to depression in both mega- and meta-analysis at the nominal level, and the associated genes were nominally related to axon guidance and immune pathways based on enrichment analysis and eQTM data. Random forest classifiers achieved the highest performance (AUC 0.73 and 0.76) in CV and hold-out tests respectively on the batch-level processed data. In contrast, the methylation showed low predictive power (all AUCs < 0.57) for all classifiers in CV and no predictive power in hold-out tests when used with harmonized data. All models achieved significantly better performance (>14% gain in AUCs) with pre-selected features (selection bias), with some of the models (joint autoencoder-classifier) reaching AUCs of up to 0.91 in the final testing regardless of data preparation. Different algorithmic feature selection approaches may outperform limma, however, random forest models perform well regardless of the strategy. The results provide an overview over potential future biomarkers for depression and highlight many important methodological aspects for DNA methylation-based depression profiling including the use of machine learning strategies.

A stable and robust fault diagnosis method for bearing using lightweight batch normalization-free residual network

Abstract The conventional deep learning-based bearing fault diagnosis methods tend to utilize denoising modules to improve the fault diagnosis performance in noisy scenes. However, the addition of denoising modules will increase expensive computational costs, leading to a delayed acquisition of fault diagnosis results. This work proposed a lightweight batch normalization (BN)-free residual network without any denoising modules for bearing fault diagnosis which properly rescaled the weights in a standard initialization instead of BN to avoid the exploding gradient problem and vanishing gradient problem at the beginning of training for deep neural networks. Therefore, it prevents the undesirable properties caused by BN. Compared with other methods, the fault diagnosis performance of the proposed method can maintain a high level with different input sizes and batch sizes. Especially in noisy scenes, the testing accuracy of fault diagnosis on different bearing datasets can be improved by 13.54% and 7.74% using fewer parameters and floating point operations on different bearing datasets.

Blood-Derived Extracellular Vesicles as a Promising Liquid Biopsy Diagnostic Tool for Early Cancer Detection.

Cancer poses a significant public health challenge worldwide, and timely screening has the potential to mitigate cancer progression and reduce mortality rates. Currently, early identification of most tumors relies on imaging techniques and tissue biopsies. However, the use of low-cost, highly sensitive, non-invasive detection methods for early cancer screening has become more attractive. Extracellular Vesicles (EVs) released by all living cells contain distinctive biological components, such as nucleic acids, proteins, and lipids. These vesicles play crucial roles in the tumor microenvironment and intercellular communication during tumor progression, rendering liquid biopsy a particularly suitable method for diagnosis. Nevertheless, challenges related to purification methods and validation of efficacy currently hinder its widespread clinical implementation. These limitations underscore the importance of refining isolation techniques and conducting comprehensive investigations on EVs. This study seeks to evaluate the potential of liquid biopsy utilizing blood-derived EVs as a practical, cost-effective, and secure approach for early cancer detection.

Compositional nutrient diagnosis (CND) applied to strawberry soilless

Soluble fertilizers are among the main costs in strawberry production, so knowing the nutrients balance in plant tissue is essential. In this sense, the Compositional Nutrient Diagnosis (CND) method is presented as a multivariate analysis tool for the nutritional diagnosis of the crop. The main goal of this paper was to establish CND standards and critical nutrient levels in strawberries cultivated in a soilless system. This study uses the Compositional Nutrient Diagnosis (CND) method to analyze nutrient levels grown in a soilless system. Commercial production greenhouses were evaluated for yield and leaf nutrients contents to develop the standards. Data were collected from 52 plots, each consisting of diagnostic leaves from 10 plants, and subsequently analyzed to determine critical nutrient deficiencies and excesses. The CND methodology was sensitive in diagnosing the differences between high and low-yield populations. Thus, adjusting the critical nutrient levels for soilless strawberry cultivation was possible. Due to deficiency, the most limiting nutrients for strawberry production were N, Mg, and S, and Zn and Cu due to excess. Results indicate specific nutrient thresholds, with significant findings from the integration of all indices, it was possible to calculate the sufficiency ranges for soilless strawberry: N = 26–31, P = 4–7, K = 8–11, Ca = 10–15, Mg = 3–4, S = 0.3–0.5 in g kg−1, and B = 18–95, Cu = 1–2, Fe = 46–96, Mn = 80–203, Zn = 1–40, Mo = 1.3–2.7, Ni = 0.1–0.4 in mg kg−1.