Development and clinical validation of a novel photography-based skin pigmentation evaluation system: a comparison with the calculated consensus of dermatologists

Effect of an anti-adhesion agent on vision-based assessment of cervical adhesions after thyroid surgery: randomized, placebo-controlled trial

Many patients experience cervical adhesions after thyroid surgery. To date, however, no studies have objectively measured the effects of anti-adhesion agents on cervical adhesion symptoms. This study evaluated the effects of an anti-adhesion agent on cervical adhesions after thyroid surgery, as determined using a system that measures the extent of marker movement objectively. One hundred patients were randomized in a 1:1 ratio to undergo thyroid surgery with or without the anti-adhesion agent Collabarrier. Using specially manufactured recording equipment, the position of the marker on neck skin was measured before surgery, and 2 weeks, 3 months, and 9 months after surgery. Relative change in marker distance, calculated by subtracting the marker position before surgery from the marker positions 2 weeks, 3 months, and 9 months after surgery, differed significantly in the groups of patients who underwent thyroid surgery with and without the anti-adhesion agent (P < 0.05). A novel measuring system can objectively evaluate the effectiveness of a thyroid anti-adhesion agent. The anti-adhesion agent used significantly reduced adhesions compared with the control group. The trial is registered at www.cris.nih.go.kr (KCT0005745; date of registration, 08/01/2021).

Identification of cecum time-location in a colonoscopy video by deep learning analysis of colonoscope movement

Background

Cecal intubation time is an important component for quality colonoscopy. Cecum is the turning point that determines the insertion and withdrawal phase of the colonoscope. For this reason, obtaining information related with location of the cecum in the endoscopic procedure is very useful. Also, it is necessary to detect the direction of colonoscope's movement and time-location of the cecum.

Methods

In order to analysis the direction of scope's movement, the Horn-Schunck algorithm was used to compute the pixel's motion change between consecutive frames. Horn-Schunk-algorithm applied images were trained and tested through convolutional neural network deep learning methods, and classified to the insertion, withdrawal and stop movements. Based on the scope's movement, the graph was drawn with a value of +1 for insertion, -1 for withdrawal, and 0 for stop. We regarded the turning point as a cecum candidate point when the total graph area sum in a certain section recorded the lowest.

Results

A total of 328,927 frame images were obtained from 112 patients. The overall accuracy, drawn from 5-fold cross-validation, was 95.6%. When the value of "t" was 30 s, accuracy of cecum discovery was 96.7%. In order to increase visibility, the movement of the scope was added to summary report of colonoscopy video. Insertion, withdrawal, and stop movements were mapped to each color and expressed with various scale. As the scale increased, the distinction between the insertion phase and the withdrawal phase became clearer.

Conclusion

Information obtained in this study can be utilized as metadata for proficiency assessment. Since insertion and withdrawal are technically different movements, data of scope's movement and phase can be quantified and utilized to express pattern unique to the colonoscopist and to assess proficiency. Also, we hope that the findings of this study can contribute to the informatics field of medical records so that medical charts can be transmitted graphically and effectively in the field of colonoscopy.