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	The fourth Japan kidney, hemocatharsis AI association arts and sciences meeting, general meeting flower moon at dawn evening greetings 長沼俊秀 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 3-3, 2024.

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	EL1. The pre-back prediction of the chronic kidney disease by the AI 神田英一郎 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 21-21, 2024.

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	EL2. Utilization of process of thinking of the AI and two 岩藤和広, 吉田智史, 和泉一樹, 陣内彦博 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 22-22, 2024.

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	EL3. Design of the peptide protein using the artificial intelligence 濱田浩幸1), 友雅司2), 金成泰3,4), 山下明泰5) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 23-23, 2024.

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	EL4. Machine learning - first step ... which is usable from today 川崎路浩 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 24-24, 2024.

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	WS1-1. Judgment supporting AI collaborative investigation of the shunts state with the electrostethoscope which we developed newly 木船和弥1), 田村宏樹2), 福元広行3), 平田朋彦4), 田中保臣5) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 25-25, 2024.

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	WS1-2. Inflection of the AI in HHD 星子清貴1), 小丸明莉1), 森石みさき2), 川西秀樹3) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 26-26, 2024.

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	WS1-3. Inspection of the usefulness of the machine learning in the kidney disease diagnosis 野田竜之介 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 27-27, 2024.

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	WS1-4. Of IOT in the peritoneal dialysis, actually 小林広学 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 28-28, 2024.

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	WS2-1. It is built the predictive model of the potassium level after the dialysis by the machine learning 小久保謙一1), 長沼俊秀2) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 29-29, 2024.

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	WS2-2. Prediction of albumin leakage, the elution removal at Pre Online HDF using AI and known information 小野寺博和 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 30-30, 2024.

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	WS2-3. A prediction and problems of the α1MG extraction coefficient using the AI in HDF 森石みさき1), 星子清貴2), 西田英樹1), 高橋直子3) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 31-31, 2024.

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	WS2-4. Clinical application case of the transtime monitoring system using the AI analysis implementation original device 小林威仁1), 渥美孝郎2), 岡田敏武2), 田邊惠司2), 中元秀友1) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 32-32, 2024.

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	WS3-1. ChatGPT: Practical utilization by the dialysis physician of the AI beginner 伊東稔 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 33-33, 2024.

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	WS3-2. The present in the kidney nourishment and future Chat GPT utilization 北島幸枝 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 34-34, 2024.

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	WS3-3. How can you make use of ability of ChatGPT in municipal hospital? 鈴木一史, 青山博道 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 35-35, 2024.

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	WS3-4. Inflection method of the Chat GPT for a presentation at the meeting, research activities 矢部広樹 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 36-36, 2024.

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	O1-1. Examination of a prediction and metrics of the exercise capacity using the gait analysis AI of haemodialysis patients 森實篤司1,2) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 37-37, 2024.

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	O1-2. We estimate dose as a dialysis previous value from a change at transtime of the plasma uremia material concentration 濱田浩幸, 野里蒼天 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 37-37, 2024.

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	O1-3. Optimization of the medical publication information collection method utilizing the generation AI 小野淳一 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 38-38, 2024.

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	O1-4. Basic examination of the imaging of the blood vessel stertor 崎山亮一1), 今井太一1), 新健太郎2), 長沼俊秀3) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 38-38, 2024.

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	O1-5. Attempt of the dry weight change prediction using the machine learning method 井上貴博1,2), 花房規男1), 川口祐輝1), 平野一1), 土谷健1) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 39-39, 2024.

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	O1-6. CKD exacerbation prediction-style construction by the multiple factor analysis using the machine learning and analysis of the exacerbation factor in the each CKD stage 小川公己1), 原宏明1), 高橋康2), 小穴聖子2), 小戸司2), 相馬健人2), 小川智也1), 前嶋明人1), 長谷川元1) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 39-39, 2024.

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	O2-1. Optimization of the renal function predictive model after the living renal transplantation using CT volumetry 木島佑1), 平井敏仁1), 岩藤和広2), 西村恭紀3), 橋本弘幸3), 海上耕平4), 清水朋一4), 石田英樹4), 高木敏男1) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 40-40, 2024.

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	O2-2. It is made the predictive model of the potassium level after the haemodialysis 田部井陸斗1), 長沼俊秀2), 小久保謙一1), 新健太郎3), 酒井利奈1), 小林こず恵1), 吉田和弘1), 久保田勝1), 氏平政伸1) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 40-40, 2024.

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	O2-3. Making of the hospitalization predictive model by the depths learning utilizing electronic medical chart data 田部井陸斗1), 酒井利奈1), 福島健介2), 南里佑太3), 小久保謙一1), 小林こず恵1), 久保田勝1), 吉田和弘1), 氏平政伸1) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 41-41, 2024.

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	O2-4. Inspection of the usefulness in the medical care of the edge device 花房規男, 井上貴博, 平野一, 土谷健 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 41-41, 2024.

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	O2-5. It is experience using the translation application by an international activity in Mongolia 中西理沙1,4), 長沼俊秀2,4), 小久保謙一3,4), 花岡吾子1,4), 松井七海2,4), 武本佳昭2,4), 内田潤次2,4) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 42-42, 2024.

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	O2-6. Performance inspection of GPT-4o in the issue of polymelia choice of the kidney internal medicine 野田竜之介, 市川大介, 柴垣有吾 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 42-42, 2024.

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	O2-7. Attempt about the efficiency of the PubMed search of the large-scale language model 花房規男, 井上貴博, 土谷健, 平野一 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 43-43, 2024.

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	O3-1. Attempt to predict a re-PTA within 90 days by a kernel discriminant analysis after the PTA for AVF 飯島崇, 大熊敦子, 堀江憲吾, 岩渕仁, 浅野学, 白井哲夫, 小口健一 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 44-44, 2024.

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	O3-2. Examination of the serum albumin level predictive model of haemodialysis patients using the machine learning 早坂秀幸1), 尾崎孝則1), 伊藤英里子1), 齋藤宏乃1), 平林泰司1), 渡邉潤一1), 中島要1), 吉田昌弘1), 一和田雅義2), 和賀政伸1), 植田裕一郎1) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 44-44, 2024.

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	O3-3. Predictive system of the α1-m extraction coefficient by the self-recurrence learning 野里蒼天1), 濱田浩幸1), 田代康介1), 平川英樹1), 友雅司2), 金成泰3,4), 山下明泰5) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 45-45, 2024.

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	O3-4. Construction of the large-scale kidney echo image data set suitable for machine learning 原悠1), 藤田雅子1), 佐藤梨沙2), 宮崎健太郎2), 飯盛聡一郎1), 内藤省太郎1) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 45-45, 2024.

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	O3-5. Development of the α1-MG extraction coefficient predictive model using the machine learning 西田英樹1), 森石みさき1), 高橋直子2), 川島幸太1), 真島菜々子1), 土谷晋一郎1) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 46-46, 2024.

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	O3-6. Approach - in examination - individual model of the intravascular volume rate of change (ΔBV) predictive model at the end of the dialysis using the machine learning 大釜健広1), 川崎路浩2), 佐久間宏治1), 内田明子1), 石塚俊治1), 佐藤純彦1) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 46-46, 2024.

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	O3-7. Basic examination of the clearance prediction of the PES membrane diagram riser in the flow rate condition 崎山亮一1), 野地聖2), 今田賢心1) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 47-47, 2024.

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	MS-1. Evaluation of dialysis shunts stenosis using the wireless small device 小泉美香子1), 片桐大輔1), 大谷良介2), 峯啓真2), 高野恭行3), 高野秀樹1) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 48-48, 2024.

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	MS-2. Examination of the quick offer of the recipe that added physique, a dialysis condition, the medication contents of the patients individual, and accepted the situation 大里寿江 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 48-48, 2024.

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	MS-3. System to alert which prevents YOLO and the drug false mixture using the video 宗田直弥, 高橋歩夢, 石井妃成, 川崎路浩 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 49-49, 2024.

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	MS-4. VA management with the electrostethoscope (HVSI monitor) 木谷博之1), 昌木秀介1), 栗栖啓子2), 藤野早知栄3), 内藤隆之3), 烏田一義4), 西澤欣子5) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 49-49, 2024.

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	MS-5. Making of the cognitive functional decline predictive model of haemodialysis patients using the machine learning 高橋蓮1), 矢部広樹2), 石川英昭3), 日比野貴志1), 錫村明生4), 山田哲也5) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 50-50, 2024.

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	MS-6. Development of the diet suggestion application depending on a variety of life background of haemodialysis patients 北島幸枝 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 50-50, 2024.

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	MS-7. Practice support of the physician for the purpose of the stabilization of the test value change in patients on dialysis 稻本健司1), 手島和子1), 烏田一義2), 本丸忠生3), 加藤曜子4), 碓井公治4), 荒川哲次4), 西澤欣子5) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 51-51, 2024.

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	MS-8. Basic examination about the data expansion to predict an FV level from shunts sound 石井妃成, 川崎路浩 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 51-51, 2024.

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	MS-9. Underlying study about the relations with the acoustic feature of a state and the shunts sound of the flow of the narrow segment downstream using the para-blood vessel model 佐々木一真1), 細川柚乃2), 中根章紀3), 奥知子3), 本橋由香3), 山内忍3), 佐藤敏夫3) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 52-52, 2024.

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	MS-10. For optimization of the individualization immunosuppressive therapy of renal-transplant recipients by the AI 岩井友明1), 町田裕一1), 三好真琴2), 壁井和也1), 長沼俊秀1), 内田潤次1) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 52-52, 2024.

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	MS-11. Prediction of the Hct level using the machine learning 高橋歩夢1), 大釜健広2), 川崎路浩1) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 53-53, 2024.

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	MS-12. Examination ... ... which is simple and easy, and connects the examination that is intactness with the pre-back prediction of the appropriate dialysis using the cardioscope 遠藤陶子, 大日向舞, 澁谷高志, 内海芳淳 Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 53-53, 2024.

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	MS-13. We examine use of AI in the patients case conference of the dietitian 長谷川民子1,2) Journal of Japanese Society for Artificial Intelligence in Nephrology and Blood Purification (suppl): 54-54, 2024.

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