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Improving the Accuracy of Diagnostic Imaging using Artificial Intelligence : A Method for Assessing Necrotic Tissue in Pressure Injury
No full textBackground, Accurate assessment of pressure injuries is critical in clinical settings, especially when evaluating necrotic tissue using the DESIGN-R® scale widely adopted in Japan. This study aimed to integrate artificial intelligence (AI) into the evaluation process to enhance diagnostic consistency and accuracy. By leveraging deep learning and convolutional neural networks, we explored the potential of AI models in classifying necrotic tissue from wound images. Methods, A retrospective observational study was conducted using electronic medical records and wound photographs from patients treated at Tottori University Hospital between 2014 and 2022. Two supervised learning models were developed: a Categorical Classification Model (CCM) for multi-class prediction, and a Binary Classification Model (BCM) implementing a two-step binary classification. Necrotic tissue was categorized based on the DESIGN-R® scale into three classes: n0 (no necrosis), N3 (soft necrosis), and N6 (hard, adherent necrosis). The models’ performance was evaluated using standard classification metrics. Results, The CCM showed recall rates of 0.7824 for n0, 0.6620 for N3, and 1.0000 for N6. In contrast, the BCM achieved higher recall rates: 0.9074 for n0, 0.9884 for N3, and 1.0000 for N6. Overall metrics for CCM were: accuracy 0.8148, precision 0.8166, and F-1 score 0.8089. The BCM surpassed these with an accuracy of 0.8711, precision 0.8418, and F-1 score 0.8508. Across all performance indicators, the BCM demonstrated superior classification capability. Conclusion, The study demonstrated that AI, particularly the binary classification approach, can enhance necrotic tissue assessment in pressure injury evaluation. The BCM consistently outperformed the CCM, supporting its potential as a reliable tool to assist clinicians in objective and standardized pressure injury evaluation using the DESIGN-R® framework
The Practice of Folk Songs in Local Education at Tottori Prefectural Normal School : From the Survey of Teachers and Students
No full textＵＬＢＰ２ ハ ＮＫＧ２Ｄ オ カイシタ コウシュヨウ メンエキ オ ヨクセイ スル コト デ シュヨウ シンコウ オ ソクシン スル
No full text鳥取大学Tottori University博士（医学
プリズム ジュンノウ ニオケル シコウ カイスウ ノ コウカ ： ジュンスイ ショウノウガタ セキズイ ショウノウ ヘンセイショウ ト パーキンソンビョウ デノ ケントウ
No full text鳥取大学Tottori University博士（医学
ニホン ニオケル ジゼン ジゴ デザイン ニヨル スイミン モンダイ ノ アル ジヘイ スペクトラム ショウジ ノ ホゴシャ ヘノ オンライン スイミン キョウイク プログラム ノ パイロット スタディ
No full text鳥取大学Tottori University博士（医学
ツメ シンキンショウ ニ タイスル ルリコナゾール ５％ ツメ ガイヨウエキ ノ シンキ ノ セイタイナイ ニオケル カンサツ ： チョウ ビサイ コウゾウ ケンキュウ
No full text鳥取大学Tottori University博士（医学
Effects of Soil Physicochemical Properties on the Occurrence of Sunburn Injury in Eggplants under Semi-Forcing Cultivation
No full text鳥取大学Tottori University博士（農学
ムジゲン アンテイセイ ズ オ モチイタ フトウ ピッチ オヨビ フトウ リード フライス コウグ ノ ビビリ シンドウ ヨソク ト アンテイセイ カイセキ
No full text鳥取大学Tottori University博士（工学