Brightness controller optimization for the digital diaphanoscopy system

Bryanskaya, Ekaterina O.; Gneushev, Roman Y.; Novikova, Irina N.; Dremin, Viktor; Dunaev, Andrey V.

doi:10.1117/12.2615012

Cited by 1 publication

(2 citation statements)

References 12 publications

(16 reference statements)

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“…In addition, a preliminary numerical simulation of the radiation propagation through the tissues of the maxillary sinus was carried out. The results obtained made it possible to justify the selected wavelengths as optimal for the study [30,31]. Preliminary studies have shown that the recorded diaphanograms are strongly influenced by the value of the optical power of the LED applicator, which is due to the difference in the anatomical structure of the tissues of the maxillary sinuses in male and female (e.g., the thickness of the hypodermis, the size of the maxillary sinuses, etc.).…”

Section: Experimental System and Methodologymentioning

confidence: 98%

“…In order to assess the influence of the optical power values, numerical simulation was carried out with the establishment of different values of the optical power for male and female. The choice of optical power values was carried out in accordance with the capabilities of the previously developed LED brightness controller of the digital diaphanoscope [32]. As a result, it was found that for female patients, the optimal optical power is 35/40 mW (650 nm) and 25/65 mW (850 nm), whereas for male patients, these values amount 55/60 mW (650 nm) and 80/85 mW (850 nm), respectively.…”

Section: Experimental System and Methodologymentioning

confidence: 99%

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Digital diaphanoscopy of maxillary sinus pathologies supported by machine learning

Bryanskaya

Dremin

Shupletsov

et al. 2023

Journal of Biophotonics

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Maxillary sinus pathologies remain among the most common ENT diseases requiring timely diagnosis for successful treatment. Standard ENT inspection approaches indicate low sensitivity in detecting maxillary sinus pathologies. In this paper, we report on capabilities of digital diaphanoscopy combined with machine learning tools in the detection of such pathologies. We provide a comparative analysis of two machine learning approaches applied to digital diapahnoscopy data, namely, convolutional neural networks and linear discriminant analysis. The sensitivity and specificity values obtained for both employed approaches exceed the reported accuracy indicators for traditional screening diagnosis methods (such as nasal endoscopy or ultrasound), suggesting the prospects of their usage for screening maxillary sinuses alterations. The analysis of the obtained values showed that the linear discriminant analysis, being a simpler approach as compared to neural networks, allows one to detect the maxillary sinus pathologies with the sensitivity and specificity of 0.88 and 0.98, respectively.

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Section: Experimental System and Methodologymentioning

confidence: 98%

Section: Experimental System and Methodologymentioning

confidence: 99%