3 measurements spanning 4 days, net -3. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 1,768–1,771 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
10 Aug 2026, 12:22
1,768
-2
7 Aug 2026, 12:24
1,770
-1
6 Aug 2026, 15:22
1,771
first reading
Engagement
21 posts held, back to 7 July 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 2 pagesof Telegram’s post history, 20 posts per page.
ERR · 30 days
23.9%
avg views ÷ 1,768 subscribers
Avg views / post
422
14 posts measured
Reaction rate
2.03%
reactions ÷ views · ER floor
Posts in window
14
of 21 held
ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.
ER is defined industry-wide as (forwards + reactions + comments) ÷ views— note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate.
What these figures were computed from
Window
Rolling 30 days · latest post in window 7 August 2026
Posts held
21 (7 July 2026 – 7 August 2026)
Views total
5,910
Reactions total
120
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
7 Aug 2026, 17:36 UTC
Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.
Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.
Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.
What this channel posts
Video runtime
14m 59s
Average length
7m 30s
Measured directly from 2 videos with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.
Reaction mix
175 reactions across 21 posts, in 5 distinct kinds. The most used accounts for 80.6% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
141
80.6%
🔥
11
6.29%
💯
10
5.71%
👍
9
5.14%
⚡
4
2.29%
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 21 of the 21 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 175reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 21 most recent posts we hold, published 7 July 2026 to 7 August 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
🟦 اپلیکیشن TTcare Equine
🔹 اسبها به دلیل ارزش اقتصادی و ورزشی بالا نیازمند پایش دقیق وضعیت حرکتی و سلامت هستند و این اپلیکیشن با هدف تشخیص زودهنگام مشکلات اسکلتی-عضلانی و مدیریت سلامت اسبها توسعه یافته است.
▪️ثبت و تشخیص هویت هر اسب با استفاده از هوش مصنوعی
▪️تحلیل گیت (Gait Analysis) و بررسی الگوی حرکت اسب از روی ویدئو برای شناسایی تغییرات غیرطبیعی
▪️کمک به شناسایی اولیه علائم لنگش پیش از آشکار شدن علائم شدید
▪…
🟦 اپلیکیشن TTcare vet
🔹 این اپلیکیشن نسخه تخصصی برای دامپزشکان و کلینیکهاست. هدف این پلتفرم کاهش زمان معاینه، مستندسازی بهتر و کمک به تصمیمگیری بالینی است.
▪️تحلیل و بررسی تصاویر چشم، پوست، دهان و سایر ضایعات قابل مشاهده
▪️ سیستم فهرستی از تشخیصهای افتراقی را پیشنهاد میدهد تا دامپزشک سریعتر به تشخیص نهایی برسد.
▪️ثبت خودکار خلاصه پرونده بیمار (SOAP) بهصورت خودکار
▪️ایجاد گزارشی ساده و قابل فهم برای توضیح وضعی…
🟦 اپلیکیشن TTcare
🔹 این اپلیکیشن با استفاده از هوش مصنوعی تصاویر و ویدئوهای ثبتشده از پت را تحلیل کرده و در صورت مشاهده علائم غیرطبیعی کاربر را از احتمال وجود مشکل آگاه میکند.
▪️بررسی چشم و تشخیص علائمی مانند قرمزی، کدورت، ترشحات و سایر ناهنجاریهای ظاهری.
▪️بررسی و ارزیابی سلامت دهان، دندانها و لثه و شناسایی تغییرات غیرطبیعی.
▪️بررسی پوست و تحلیل ضایعات پوستی مانند التهاب، زخم و ریزش مو
▪️تحلیل نحوه راه رفتن (G…
🔹 AI FOR PET 🔹
🔹 استارتاپ کرهای AI for pet با استفاده از الگوریتمهای یادگیری عمیق امکان غربالگری و تشخیص زودهنگام برخی بیماریهای حیوانات را تنها با استفاده از دوربین گوشی هوشمند فراهم کرده است.
▪️آموزش مدل روی بیش از ۲.۵ میلیون تصویر
▪️استفاده از دادههای جمعآوریشده از ۱۲ کشور
▪️میلیونها اسکن انجامشده توسط کاربران
▪️همکاری با دانشگاهها و مراکز دامپزشکی معتبر
▪️دریافت جوایز بینالمللی مانند CES Innovation Aw…
☑️ پینوشت:
🔺 سرویس هوش مصنوعی دامپزشکی AIS RapidRead یک محصول از شرکت Antech Diagnostics است که تصاویر رادیوگرافی سگ و گربه (از قفسهی سینه، شکم و اندامها) را در کمتر از ۱۰ دقیقه تحلیل کرده و گزارش اولیهای در اختیار دامپزشک میگذارد؛ ضمن اینکه در صورت مشاهدهی موارد اورژانسی، بهطور خودکار تصویر را برای بررسی رایگان به متخصصان معتبر ارجاع میدهد تا دقت تشخیص نهایی نیز تضمین شود. ◀️ BIOVET , Antech
#ابزار
🤖 @Vet…
🟦 ارزیابی عملکرد نرمافزار هوش مصنوعی AIS RapidRead در تشخیص علائم رادیوگرافیک نارسایی قلبی (HF) در سگ و گربه و مقایسه آن با گزارش رادیولوژیستهای دامپزشکی.
🔹 در سگها این هوش مصنوعی توانست نارسایی قلبی را با حساسیت 87٪ و ویژگی 80٪ تشخیص دهد. همچنین در شناسایی کاردیومگالی (100٪)، الگوی بینابینی (98٪) و تغییرات عروقی (95٪) عملکرد بسیار بالایی داشت.
🔹 در گربهها، اگرچه در تشخیص یافتههای رادیوگرافی مانند کاردیومگالی …
🟦 طراحی شبکههای عمیق در تصویربرداری پزشکی
🔹 این کتاب یک مرجع جامع و تخصصی در حوزه طراحی شبکههای عمیق در حوزه تشخیص و تحلیل تصویربرداری پزشکی است.
🔹 این کتاب با ارائه اصول طراحی و تکنیکهای روز، برای هر مبحث یک مطالعهٔ موردی عملی به همراه شبکهٔ عصبی متناسب ارائه میدهد و در نهایت چالشهایی همچون تعمیمپذیری، حریم خصوصی و کارایی بالینی در هوش مصنوعی پزشکی را بررسی میکند.
#کتاب
🤖 @Veterinary_AI 🐎
🟦 اولین LLM اختصاصی دامپزشکی
🔹 شرکت Xiaowen Technology با معرفی VetMind، اولین LLM اختصاصی دامپزشکی را ارائه کرده است. این سیستم ابزارهای هوش مصنوعی مختلف دامپزشکی (تبدیل خودکار مکالمه بین دامپزشک و اونر و ثبت پرونده بالینی، ارزیابی تصویربرداری، پشتیبانی تصمیمگیری بالینی و...) را در یک بستر یکپارچه گرد هم میآورد و به دامپزشکان در تمامی مراحل درمان کمک میکند.
🔹 این سیستم بر پایه ۱۰۰ میلیون پرونده بالینی واقعی آمو…
🟦 پلتفرم هوش مصنوعی Connecterra AI ، سیستمی برای مدیریت گاوداریهای شیری
1⃣ Analytics
• تحلیل تولید شیر و بررسی عملکرد گله
• مقایسه شاخصها در بازههای زمانی مختلف
• ساخت داشبوردهای اختصاصی و مشاهده روندها
• شناسایی دادههای ناقص یا غیرطبیعی با استفاده از هوش مصنوعی
2⃣ AI Copilot
• ارسال خلاصه هفتگی عملکرد مزرعه بهصورت خودکار
• شناسایی تغییرات مهم و توضیح علت احتمالی آنها
• ارائه هشدارهای مهم قبل از تبدیل شدن ب…
🟦 ماشین حساب دوز داروهای دامپزشکی
🔹 این ابزار برای بررسی سریع دوز داروهای رایج در سگ و گربه طراحی شده و شامل دستهبندیهایی مثل آنتیبیوتیکها، ضدالتهابها، ضدانگلها، داروهای گوارشی، تنفسی و… است.
🔹 نحوه استفاده:
1️⃣ وارد سایت شوید.
2️⃣ وزن حیوان را وارد کنید (kg / lb).
3️⃣ در قسمت جستجوی دارو، نام داروی موردنظر را وارد کنید.
4️⃣ از دستهبندیها میتوانید داروها را راحتتر پیدا کنید (آنتی بیوتیک، ضدالتهاب، ضددرد، …
❤7👍2
Showing the 12 most recent of 21 posts we hold for @Veterinary_AI. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Citation-graph rank
Citation-graph rank — 1,107,367 of 1,345,403entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
Forward network
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
Mentions
Named by 2 registered channels — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.
Named by
Channels on the register whose posts name this channel's handle.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
Cite this entry
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 10 August 2026 — this
entry's latest reading, not the date you are reading this.
“Veterinary ~ AI” (@Veterinary_AI), 1,768 subscribers as measured 10 August 2026. Telegram Register, tgregister.com/channel/Veterinary_AI.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.