====== Digital sensor ======

A digital [[sensor]], also known as a digital image sensor, is an electronic device that captures light and converts it into a [[digital image]]. It is a crucial component in digital cameras, smartphones, and other imaging devices. The sensor is responsible for capturing the light information from the scene being photographed and converting it into an electrical signal that can be processed and stored as a digital image file.

Digital sensors typically use a grid of photosensitive elements called pixels to detect light. Each pixel measures the intensity of light falling on it and generates an electrical charge proportional to that intensity. The size and number of pixels on the sensor determine the resolution and image quality.

There are two main types of digital sensors used in consumer cameras:

Charge-Coupled Device (CCD): CCD sensors use a technology that moves the charge across the chip to a single output node. The charge is then converted into a voltage, amplified, and converted into a digital signal. CCD sensors are known for producing high-quality images with excellent color accuracy, dynamic range, and low noise. However, they tend to consume more power and are relatively slower compared to the other type.

Complementary Metal-Oxide-Semiconductor (CMOS): CMOS sensors use a different technology where each pixel has its own amplifier and digital converter. This allows for faster readout speeds and lower power consumption compared to CCD sensors. Initially, CMOS sensors were considered to be inferior in terms of image quality, but advancements in technology have significantly improved their performance. Many modern digital cameras and smartphones use CMOS sensors due to their speed, power efficiency, and ability to perform various on-chip processing functions.

Both CCD and CMOS sensors have their advantages and are used in different applications based on their specific characteristics and requirements. The choice between the two depends on factors such as desired image quality, speed, power consumption, and cost.

In addition to cameras, digital sensors are also used in various other applications, including scientific imaging, surveillance systems, medical imaging, and industrial inspection, where capturing and analyzing visual information is necessary.
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Computer-aided [[detection]], used in the [[screening]] and diagnosing of cognitive [[impairment]], provides an objective, valid, and convenient [[assessment]]. [[Digital sensor]] technology is a promising detection method.

A study aimed to develop and validate a novel [[Trail Making Test]] (TMT) using a combination of paper and electronic [[device]]s.

This study included community-dwelling older adult individuals (n=297), who were classified into (1) cognitively healthy controls (HC; n=100 participants), (2) participants diagnosed with mild cognitive impairment (MCI; n=98 participants), and (3) participants with Alzheimer disease (AD; n=99 participants). An electromagnetic tablet was used to record each participant's hand-drawn stroke. A sheet of A4 paper was placed on top of the tablet to maintain the traditional interaction style for participants who were not familiar or comfortable with electronic devices (such as touchscreens). This way, all participants were instructed to perform the TMT-square and circle. Furthermore, we developed an efficient and interpretable cognitive impairment-screening model to automatically analyze cognitive impairment levels dependent on demographic characteristics and time-, pressure-, jerk-, and template-related features. Among these features, novel template-based features were based on a vector quantization algorithm. First, the model identified a candidate trajectory as the standard answer (template) from the HC group. The distance between the recorded trajectories and reference was computed as a critical evaluation index. To verify the effectiveness of our method, we compared the performance of a well-trained machine-learning model using the extracted evaluation index with conventional demographic characteristics and time-related features. The well-trained model was validated using follow-up data (HC group: n=38; MCI group: n=32; and AD group: n=22).

They compared 5 candidates [[machine learning]] methods and selected random forest as the ideal model with the best performance (accuracy: 0.726 for HC vs MCI, 0.929 for HC vs AD, and 0.815 for AD vs MCI). Meanwhile, the well-trained classifier achieved better performance than the conventional assessment method, with high stability and accuracy of the follow-up data.

The study demonstrated that a model combining both paper and electronic TMTs increases the accuracy of evaluating participants' [[cognitive impairment]] compared to conventional paper-based feature assessment
((Zhang W, Zheng X, Tang Z, Wang H, Li R, Xie Z, Yan J, Zhang X, Yu Q, Wang F, Li Y. Combination of Paper and Electronic Trail Making Tests for Automatic Analysis of Cognitive Impairment: Development and Validation Study. J Med Internet Res. 2023 Jun 9;25:e42637. doi: 10.2196/42637. PMID: 37294606.)).