Equal Thresholds Research Articles

The structured assessment of violence risk in youth demonstrates measurement invariance between Black and White justice-referred youths.

Identification and implementation of effective methods for reducing racial/ethnic bias and disparities in legal settings are paramount in the United States and other countries. One procedure originally thought to reduce bias in legal decisions is the use of risk assessment instruments, which is now being heavily scrutinized. Measurement invariance, a latent trait technique, is a robust method for assessing one form of bias. Measurement invariance involves determining whether risk items in an instrument appear to be functioning the same between racial or other groups. Thus, the present study examined measurement invariance of the Structured Assessment of Violence Risk in Youth (SAVRY) between non-Latino Black and White youths to examine racial bias. We expected the SAVRY to be invariant (lacking measurement bias) between Black and White youths. The sample included 687 Black and 361 White youths, and the study used a large, multistate data set of SAVRYs conducted by probation officers. We conducted measurement invariance testing in a series of hierarchical steps including testing configural and scalar invariance. The SAVRY demonstrated scalar invariance (equal thresholds for ratings from "low" to "moderate" and "moderate" to "high") for all items except one-community disorganization. The findings lend further credibility to the SAVRY, and the structured professional judgment approach, as a method to assess violence risk and case planning needs among youths involved in the legal system. These findings provide more confidence that significant differences in SAVRY risk level or items between Black and White youths are not based in measurement bias, with the exception of the community disorganization item. Potential fixes discussed include eliminating or deemphasizing this item in final risk level ratings. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Bornean Orangutan Nest Classification using Image Enhancement with Convolutional Neural Network and Kernel Multi Support Vector Machine Classifier

Preserving wildlife habitats is crucial in mitigating climate change. Species like orangutans and monkeys contribute to fruiting and planting in forests. The World Wide Fund Sabah Malaysia faces challenges in manually identifying and classifying orangutan nests for studying their behaviour and conserving their habitats. To address this, we propose automating the classification of captured images using machine learning algorithms. This research involves three key components: image processing, feature extraction, and image classification. Our proposed image processing includes several steps, such as image pre-processing and enhancement techniques like local contrast enhancement, sharpening, intensity adjustment, histogram equalization, and colour thresholding. We applied four different Convolutional Neural Networks (CNNs) to extract and identify orangutan nests’ features. Subsequently, we utilize Support Vector Machine (SVM) for image classification. The results reveal that the Inception Residual Network Version 2 (ResNet-v2) achieves the best performance. This architecture is then combined with a kernel SVM to classify Bornean orangutan nests. Our approach demonstrates impressive results, boasting an accuracy of 96.60%, an F1-score of 96.60%, a precision of 96.59%, and a recall of 96.58%. These metrics underscore the high accuracy and effectiveness of our proposed methodology for classifying Bornean orangutan nests. By reducing the need for extensive human intervention in image analysis, our method presents a valuable tool for conservationists and researchers committed to studying and safeguarding these endangered orangutans and their habitats. In future work, we aim to develop orangutan nest detector, contributing to wildlife conservation research.

Improved Background Subtraction with Histogram Equalization and Adaptive Thresholding

Improved Background Subtraction with Histogram Equalization and Adaptive Thresholding

Diagnostic and therapeutic performances of three score-based Thyroid Imaging Reporting and Data Systems after application of equal size thresholds.

The aim of this study was to explore the diagnostic and therapeutic performances of the artificial intelligence (AI), American College of Radiology (ACR), and Kwak Thyroid Imaging Reporting and Data Systems (TIRADSs) using the size thresholds for fine needle aspiration (FNA) and follow-up defined in the ACR TIRADS. This retrospective study included 3,833 consecutive thyroid nodules identified in 2,590 patients from January 2010 to August 2017. Ultrasound (US) features were reviewed using the 2017 white paper of the ACR TIRADS. US categories were assigned according to the ACR/AI and Kwak TIRADS. We applied the thresholds for FNA and follow-up defined in the ACR TIRADS to the Kwak TIRADS. The diagnostic and therapeutic performances were calculated and compared using the McNemar or DeLong methods. The AI TIRADS had higher specificity, accuracy, and area under the curve (AUC) than did the ACR and Kwak TIRADS (specificity: 64.6% vs. 57.4% and 52.69%; accuracy: 78.5% vs. 75.4% and 73.0%; AUC: 88.2% vs. 86.6% and 86.0%; all P values <0.05). Meanwhile, the AI TIRADS had a lower FNA rate (FNAR), unnecessary FNA rate (UFR), and follow-up rate (FUR) than did the ACR and Kwak TIRADS using the size thresholds of the ACR TIRADS (specificity: 30.9% vs. 34.4% and 36.9%; accuracy: 41.1% vs. 47.8% and 48.7%; AUC: 34.2% vs. 37.7% and 41.0%; all P values <0.05). In addition, the Kwak TIRADS incorporating the size thresholds of the ACR TIRADS was almost similar to the ACR TIRADS in diagnostic and therapeutical performance. The ACR TIRADS can be simplified, which potentially enhances its diagnostic and therapeutic performance. The method of score-based TIRADS (counting in the Kwak TIRADS and weighting in the ACR and AI TIRADS) might not determine the diagnostic and therapeutic performances of the TIRADS. Thus, we propose choosing a straightforward and practical TIRADS in daily practice.

And again, about lasing threshold, light localization, and density of states in 1D photonic crystals

We investigate numerically the relationship between the lasing threshold, the density of states, and field localization in 1D photonic crystals for off-axis laser action. The angular dependence of the corresponding quantities for the two polarizations is obtained, and it is shown that the relationship between the threshold and density of states is complex, especially near the Brewster angle in the case of p-polarization. Moreover, for most angles of incidence (the cone angle), the lasing threshold for s-modes is much smaller than for p-modes which results in stable linear polarization of the conical modes. A strong correlation between the lasing thresholds and field localization of the two modes for s-polarization was found. This fact allows to easily calculate the angular dependence of one mode, if such dependence is known for another mode of the same order and the field localization dependence for both modes, which can be useful both in numerical simulation of lasing in photonic crystals, and in theoretical analysis. The parameters for binary photonic crystals with equal lasing thresholds for edge modes of the same order are found. The results of this work can be used for optimizing lasers based on one-dimensional photonic crystals, both for normal and off-axis lasing.

Right bundle branch pacing: Criteria, characteristics, and outcomes

Targets for right-sided conduction system pacing (CSP) include His bundle and right bundle branch. Electrocardiographic patterns, diagnostic criteria, and outcomes of right bundle branch pacing (RBBP) are not known. Our aims were to delineate electrocardiographic and electrophysiological characteristics of RBBP and to compare outcomes between RBBP and His bundle pacing (HBP). Patients with confirmed right CSP were divided according to the conduction system potential to QRS interval at the pacing lead implantation site. Six hypothesized RBBP criteria as well as pacing parameters, echocardiographic outcomes, and all-cause mortality were analyzed. All analyzed criteria discriminated between HBP and RBBP: double QRS transition during the threshold test, selective paced QRS different from conducted QRS, stimulus to selective-QRS > potential-QRS, small increase in V6 R-wave peak time (V6RWPT) during QRS transition, equal capture thresholds of CSP and myocardium, and stimulus-V6RWPT > potential-V6RWPT (adopted as the diagnostic standard). According to the last criterion, RBBP was observed in 19.2% of patients (64 of 326) who had been targeted for HBP, present mainly among patients with potential to QRS <35 ms (90.6% [48 of 53]) and occasionally among the remaining patients (5.6% [16 of 273]). RBBP was characterized by longer QRS (by 10.5 ms), longer V6RWPT (by 11.6 ms), and better sensing (by 2.6 mV) compared with HBP. During a median follow-up duration of 29 months, no differences in capture threshold, echocardiographic outcomes, or mortality were found. RBBP has distinct features that separate it from HBP and is observed in approximately a fifth of patients in whom HBP is intended.

Children's social emotional competence in Pakistan and Sweden: Factor structure and measurement invariance of the Social Competence Scale (teacher edition).

Social emotional competence is fundamental to the positive development of children and youth. Accurately understanding and assessing children's social emotional competencies, using psychometrically sound instruments, are essential to global efforts to support children's social emotional learning, academic achievements, and health. This study examined the psychometric properties of a teacher-reported measure of young children's social emotional competence, the Social Competence Scale - Teacher edition (SCS-T), in two samples of children growing up with varied economic resources/conditions, cultural norms, and educational experiences, namely Pakistan (N = 396) and Sweden (N = 309). Participants were aged 4-6 years old. The study design was cross-sectional. Using structural equation modelling, bi-factor confirmatory factor analysis models implying shared variance, among all items and domain-specific shared variance, among the prosocial items, emotion regulation items, and academic skills items resulted in good fitting models in each respective sample. Invariance testing across samples revealed a subset of items from each factor structure with partial scalar invariance, whereby five items had equal thresholds and could be comparable across the two samples. Thus, results provided partial support for hypotheses 1, 2, and 3, in that the posited three factor model (H1) was not clearly supported and a bi-factor model evidenced the best fit, among tested models, for both samples. Further, partial scalar invariance (H3) was found for five items out of 25 items, concerning social competence and academic skills. In regards, to the posited research question, the results of Z-tests showed significant (p < 0.001) latent mean differences between the samples. Compared to the Swedish sample, the Pakistani sample was 1.80 units lower on social competence (z = -6.41, p < 0.001) and 1.86 units lower on academic skills (z = -7.87, p < 0.001). The implications of these findings in light of efforts to promote positive child development in diverse parts of the world are considered.

𝐹-thresholds and test ideals of Thom-Sebastiani type polynomials

We provide a formula for F F -thresholds of a Thom-Sebastiani type polynomial over a perfect field of prime characteristic. We also compute the first test ideal of Thom-Sebastiani type polynomials. Finally, we apply our results to find hypersurfaces where the log canonical thresholds equal the F F -pure thresholds for infinitely many prime numbers.

UNAS-Net: A deep convolutional neural network for predicting Covid-19 severity

We present a study on Covid-19 detection using deep learning algorithms that help predict and detect Covid-19. Chest X-ray images were used as the input dataset to prepare and train the proposed model. In this context, deep learning architecture (DLA) and optimisation strategies have been proposed and explored to support the automated detection of Covid-19. A model based on a convolutional neural network was proposed to extract features of images for the feature-learning phase. Data augmentation and fine-tuning with deep-feature-based methods were applied to improve the model. Image enhancement and saliency maps were used to enhance visualisation and estimate the disease severity level based on two parameters; degree of opacity and geographic extent. Contrast-limited adaptive histogram equalization and Otsu thresholding were employed with several parameters to investigate the effects on the visualisation results. An experimental investigation was performed between the proposed method and other pretrained DLAs. The proposed work obtained excellent classification accuracy and sensitivity of 97.36% and 95.24% respectively. In addition, the input parameters for image enhancement significantly affected the results. The overall performance metrics were perfect for DenseNet and adequately high for the proposed work which is comparable to other models. Data augmentation and fine-tuning successfully handed the networks to enhance the overall performance, especially in our case with limited datasets.

Quantifying cannabis problems among college students from English and Spanish speaking countries: Cross-cultural validation of the Cannabis Use Disorders Identification Test-Revised (CUDIT-R)

ObjectiveThe Cannabis Use Disorders Identification Test - Revised (CUDIT-R) is a broadly employed measure of cannabis-related problems. However, minimal research has tested the measurement invariance of the CUDIT-R among youths from different countries, hindering cross-national comparisons. Thus, the present study aimed to test the measurement invariance of the CUDIT-R between seven countries and gender groups, and provide different sources of reliability and validity evidence of the scale. MethodsA sample of 4,712 college student lifetime cannabis users (mean age = 20.57, SD = 3.97; 70.4% females) from seven countries completed the CUDIT-R. Last 30-day cannabis users (n = 2402; mean age = 20.09, SD = 3.18; 67.7% females) additionally completed another measure of cannabis-related problems, and measures of cannabis frequency, quantity and motives. ResultsMultigroup analysis showed configural (equal number of factors and pattern of factor-indicator relationships), metric (equal factor loadings) and scalar (equal thresholds) invariance of the CUDIT-R across five countries and across gender in the sample of lifetime cannabis users. Cronbach’s alphas and ordinal omegas ranked from .72 and .85. Large correlations were found between the CUDIT-R and another cannabis-related problem scale. Small to large associations were found between the CUDIT-R and other criterion variables (frequency and quantity of consumption and cannabis-related motives) providing convergent and discriminant validity evidence. Only a few differences in the magnitude of the correlations across countries were found. ConclusionsThe results suggest that the CUDIT-R is a suitable measure to assess cannabis-related problems among college student from the U.S., Canada, South Africa, Spain, and Argentina and across gender groups.

CtNorm: Real time PCR cycle of threshold (Ct) normalization algorithm

In relative quantification with Real Time PCR (qRT-PCR,), accurate analysis requires equal amplification efficiency for both genes (Gene of interest and reference gene) and equal threshold values for all the samples. In this quantification method the expression level in treated samples will be calculated in comparison to the control group. We conducted the present study to design an algorithm for converting the data obtained from different runs containing identical standard samples into one run with the same amplification efficiency and threshold value. For this purpose, two formulas were designed; one to convert the amplification efficiency of the each run to 100%, and the other one for converting data from different runs into one run. Utilizing these two formulas, an algorithm was developed and named CtNorm. The online version of CtNorm algorithm is available at http://ctnorm.sums.ac.ir/. We used qRT-PCR technique to validate the accuracy of the designed algorithm for the normalization of four different human internal control genes. Normalizing the Ct values obtained from separate runs with the CtNorm algorithm has eliminated the differences and the average of the Ct values has become similar to the condition in which all the samples were amplified in a single run. The CtNorm algorithm could be utilized for equalizing the Ct values of several qRT-PCR runs with the same standard samples. The algorithm has also the ability to convert the amplification efficiency to 100% which is useful in absolute and relative quantification.

Measurement invariance testing of the patient health questionnaire-9 (PHQ-9) across people with and without diabetes mellitus from the NHANES, EMHS and UK Biobank datasets

BackgroundThe prevalence of depression is higher among those with diabetes than in the general population. The Patient Health Questionnaire (PHQ-9) is commonly used to assess depression in people with diabetes, but measurement invariance of the PHQ-9 across groups of people with and without diabetes has not yet been investigated. MethodsData from three independent cohorts from the USA (n=1,886 with diabetes, n=4,153 without diabetes), Quebec, Canada (n= 800 with diabetes, n= 2,411 without diabetes), and the UK (n=4,981 with diabetes, n=145,570 without diabetes), were used to examine measurement invariance between adults with and without diabetes. A series of multiple group confirmatory factor analyses were performed, with increasingly stringent model constraints applied to assess configural, equal thresholds, and equal thresholds and loadings invariance, respectively. One-factor and two-factor (somatic and cognitive-affective items) models were examined. ResultsResults demonstrated that the most stringent models, testing equal loadings and thresholds, had satisfactory model fit in the three cohorts for one-factor models (RMSEA = .063 or below and CFI = .978 or above) and two-factor models (RMSEA = .042 or below and CFI = .989 or above). LimitationsData were from Western countries only and we could not distinguish between type of diabetes. ConclusionsResults provide support for measurement invariance between groups of people with and without diabetes, using either a one-factor or a two-factor model. While the two-factor solution has a slightly better fit, the one-factor solution is more parsimonious. Depending on research or clinical needs, both factor structures can be used.

Follicle Detection for Polycystic Ovary Syndrome by using Image Processing Methods

Polycystic ovary syndrome is a hormonal disorder seen in many women. It occurs by the combination of many small and benign cysts in the ovaries. These cysts, called follicles, create a special pattern in the ovaries observed with ultrasound imaging. The number, structure, and size of these follicles provide important information for the diagnosis of ovarian diseases. In this study, two different methods of follicle detection are tested for Polycystic Ovary Syndrome. The first method consists of noise filtering, contrast adjustment, binarization, and morphological processes. For this method, Median Filter, Average Filter, Gaussian Filter, and Wiener Filter were used for noise reduction, and then histogram equalization and adaptive thresholding were tested. For the second method, Gaussian Filter and Wavelet Transform were selected for noise reduction, and k-means clustering and morphological operations were applied to the images. In the segmentation phase performed for both methods, follicles were detected with the Canny Edge Detection algorithm. False Acceptance Rate (FAR) and False Rejection Rate (FRR) were used to evaluate the accuracy of the results. Our results show that the most accurate follicle detection was obtained by using the Wiener Filter and Gaussian Filter.

Enhancing underwater image via color correction and Bi-interval contrast enhancement

Underwater captured images often suffer from color cast and low visibility due to light is scattered and absorbed while it traveling in water. In this paper, we proposed a novel method of color correction and Bi-interval contrast enhancement to improve the quality of underwater images. Firstly, a simple and effective color correction method based on sub-interval linear transformation is employed to address color distortion. Then, a Gaussian low-pass filter is applied to the L channel to decompose the low- and high-frequency components. Finally, the low- and high-frequency components are enhanced by Bi-interval histogram based on optimal equalization threshold strategy and S-shaped function to enhancement image contrast and highlight image details. Inspired by the multi-scale fusion, we employed a simple linear fusion to integrate the enhanced high- and low-frequency components. Comparison with state-of-the-art methods show that the proposed method outputs high-quality underwater images with qualitative and quantitative evaluation well.

Effects of metal fluoride/sulfide microparticles generated by consecutive high-pulse-power breakdowns on the insulating performance in compressed SF6

Spark gas switches (SGS) are widely used in various pulsed power applications where sulfur hexafluoride (SF6) is still dominant because of its excellent insulating performance. However, particle contamination generated by SF6 possesses some special properties that can greatly reduce the effectiveness of the gas switches. The objective of this paper is to study the effects of particles generated spontaneously by consecutive breakdowns of high-pulse-power SGS on the insulating performance in compressed SF6. A double electrode/double pulse method, coupled with laser scattering and laser shadow photography, is adopted to detect the particles and examine their specific roles in the breakdown process. Many large particles in SF6, of approximately 150 μm in diameter, are observed at about 80 milliseconds after a single breakdown. Furthermore, numerous particles gradually suspend and accumulate in the gap after consecutive breakdowns. Particles generated by SF6 can reach tens of micrometers in size. They have rough microtopography covered by abundant floccules, and contain rich electronegative elements: fluorine and sulfur. Moreover, particle-involved abnormal breakdowns in SF6 usually occur with nearly equal threshold and probability in spite of the increasing consecutive breakdowns, and conversely, the abnormal breakdowns in N2 appear more frequently and unsteadily with a high dispersibility. The analysis of field enhancement caused by these large metal-fluoride/sulfide particles shows that they can directly cause significant field emission (FE) due to their properties of surface-field enhancement and causticity. Such FE further triggers micro-discharge. Subsequently, this behavior eventually leads to abnormal breakdowns at a lower threshold.

Joint pre-processing framework for two-dimensional gel electrophoresis images based on nonlinear filtering, background correction and normalization techniques

BackgroundTwo-dimensional gel electrophoresis (2-DGE) is a commonly used tool for proteomic analysis. This gel-based technique separates proteins in a sample according to their isoelectric point and molecular weight. 2-DGE images often present anomalies due to the acquisition process, such as: diffuse and overlapping spots, and background noise. This study proposes a joint pre-processing framework that combines the capabilities of nonlinear filtering, background correction and image normalization techniques for pre-processing 2-DGE images. Among the most important, joint nonlinear diffusion filtering, adaptive piecewise histogram equalization and multilevel thresholding were evaluated using both synthetic data and real 2-DGE images.ResultsAn improvement of up to 46% in spot detection efficiency was achieved for synthetic data using the proposed framework compared to implementing a single technique of either normalization, background correction or filtering. Additionally, the proposed framework increased the detection of low abundance spots by 20% for synthetic data compared to a normalization technique, and increased the background estimation by 67% compared to a background correction technique. In terms of real data, the joint pre-processing framework reduced the false positives up to 93%.ConclusionsThe proposed joint pre-processing framework outperforms results achieved with a single approach. The best structure was obtained with the ordered combination of adaptive piecewise histogram equalization for image normalization, geometric nonlinear diffusion (GNDF) for filtering, and multilevel thresholding for background correction.

Diagnostic performances and unnecessary US-FNA rates of various TIRADS after application of equal size thresholds

We compared the diagnostic performances and unnecessary FNA rates of several guidelines and modified versions using the size threshold of the ACR TIRADS. Our Institutional Review Board approved this retrospective study and waived the requirement for informed consent and all methods were performed in accordance with the Declaration of Helsinki. A total of 1,384 thyroid nodules in 1,301 patients with definitive cytopathologic findings were included. US categories were assigned according to each guideline. We applied the size threshold suggested by the ACR TIRADS for FNA to the Kwak, ATA and EU guidelines and defined these modified guidelines as the modified Kwak (mKwak), modified ATA (mATA) and modified EU (mEU) guidelines. Diagnostic performances and unnecessary FNA rates of all guidelines were evaluated. Of 1,384 thyroid nodules, 291 (21%) were malignant. Among the original guidelines, the ACR TIRADS had the highest specificity, accuracy, LR and AUC (62.2%, 66%, 2.128 and 0.713). The mKwak, mATA and mEU guidelines had higher specificity, accuracy, LR and AUC (P < 0.001 for all), and fewer unnecessary FNAs, compared with their original guidelines. Among all original and modified guidelines, the mKwak guideline had the highest specificity, accuracy, LR and AUC (64%, 68.6%, 2.389 and 0.75). The unnecessary FNA rate was the lowest with the mKwak guideline (61.1%). The highest sensitivity was observed with the ATA guideline (98.6%). After incorporating the size threshold of the ACR TIRADS to other TIRADS, all guidelines showed higher diagnostic accuracy and lower unnecessary FNA rates than their original versions. The mKwak guideline showed the best diagnostic performances.

ASSESSMENT OF HEARING THRESHOLDS IN MEDICAL STUDENTS BASED ON GENDER AND USE OF HEADPHONES.

Introduction: The sounds we are exposed to everyday are usually at a safe level and do not affect our hearing. However, development of latest technology and frequent use of headphones has increased the proximity of sound to the ear. Adolescents and young adults who are commonly exposed to loud music/noise are at a greater risk of developing hearing impairment. Hence the adverse effects of chronic exposure to loud sounds need to be assessed.&#x0D; Objectives: To determine hearing thresholds in students (Males/Females) at various frequencies and the changes in hearing thresholds associated with prolonged exposure (&gt;90mins) to sound.&#x0D; Materials and Methods: The study was conducted in 148 first year MBBS students.&#x0D; Materials used - ALPS Advanced Digital Audiometer AD2100, bone oscillator and headphones.&#x0D; Statistical analysis was done using SPSS version 22.0 with p&lt;0.05 as significant.&#x0D; Results: 1) Males have lower hearing thresholds than females except at higher frequencies where females have equal or better hearing thresholds than males.&#x0D; 2) The mean exposure to headphones was 115.432±92.1348 mins.&#x0D; Subjects with &gt;90mins exposure had better hearing thresholds at lower frequencies but at higher frequencies showed increase in hearing thresholds.&#x0D; Conclusion: Lower hearing thresholds in females at higher frequencies could be evolutionary as females are more sensitive to high pitch sounds like baby’s cry. Prolonged exposure to loud sound damages hair cells at base of cochlea responsible for picking up high frequency sounds.&#x0D; Keywords: Hearing thresholds, noise, adolescents.

A comparative analysis of information provision strategies for parking variable message sign display problems

Parking spaces are insufficient and are plagued by over-consumption in hot areas. To assist drivers easily in identifying available parking spaces, parking variable message signs are commonly adopted to display information on space availability. This paper analyzes the performance of various information provision strategies. To achieve this objective, we first present the mechanisms of the information provision strategies. Then, the information provision strategies are classified into three categories: regular, symmetric, and discriminative. The regular strategies provide the collected parking information directly to drivers; the symmetric schemes employ the equal threshold values for all parking lots; and the discriminative schedules adopt an independent threshold value for each parking lot. The threshold value provides an upper limit for the Space Occupancy Percentage (SOP): when the SOP is larger than the threshold value, the parking lot status becomes FULL; otherwise, it is displayed having available spaces. Finally, an agent-based simulation model is introduced to describe the parking and traffic conditions. The results indicate that both the symmetric and discriminative strategies significantly decrease the highest failure rate and average travel time, whereas the latter performs better. The results of this comparative analysis can assist in the configuration and operation of an urban parking guidance and information system.

Age‐related changes in flicker sensitivity with rod‐ and cone‐enhanced stimuli

AbstractPurposeCone and rod photoreceptors are usually isolated using high light levels for cone vision and full dark‐adaptation, when only rods respond to light. Such methods are not ideal for clinical use. A new rod/cone flicker test has been designed to isolate and measure rod‐ and cone‐mediated signals in the absence of colour signals with one minute adaptation.Methods118 healthy adults (Mean age ± standard deviation: 40 ± 15 years), were investigated. None of the participants had a history of or exhibited signs of ocular disease. Rod and cone thresholds were measured monocularly in central vision and in each of the four quadrants (superonasal, superotemporal, inferonasal and inferotemporal) at an eccentricity of 5°. Temporal frequencies, stimulus sizes, background luminances and spectral composition were adjusted appropriately to achieve approximately equal thresholds with ~ 10‐fold separation in sensitivity between the rod‐ and cone‐enhanced stimuli. Flicker thresholds were measured using five adaptive (two‐down, one‐up), interleaved staircases. Spectrally calibrated, ‘neutral density’ glasses were used to enable adequate control of display luminance at low levels.ResultsThe superotemporal quadrant had the lowest mean rod and cone thresholds compared to other tested quadrants. For participants, above 50 years of age, the central rod thresholds increased 0.53% per year, whereas cone thresholds increased 0.29% per year and this decline was faster than other respective quadrants. Overall, among the locations tested, the central rod (ρ = 0.547; p &lt; 0.005) and cone (ρ = 0.504, p &lt; 0.005) thresholds demonstrated the strongest correlation with age.ConclusionsThe new test isolates rod and cone signals sufficiently well to detect selective loss of photoreceptor function in normal ageing. The potential use of rod‐ and cone‐enhanced tests in patients with selective loss of photoreceptors and other retinal diseases (e.g., AMD, glaucoma, diabetes) remains to be explored.