# littlEye AI — Histology AI for pathologists > Histology AI for pathologists (iPhone/iPad app by Jan Sikuta.) - Source: https://appshunter.io/ios/app/littleye-ai/id6799417580 (this page in markdown: same URL + `.md`) - Developer: [Jan Sikuta](https://appshunter.io/developer/6782549504) - Category: Medical, Education - Price: Free - Age rating: 12+ - Requires: iOS 17.0 · 13 MB - Languages: American English, Slovak - Released: 2026-08-24 - Data updated: 2026-09-26 - User reviews in markdown: https://appshunter.io/ios/app/littleye-ai/id6799417580/reviews.md ## What is littlEye AI? littlEye AI grew from a simple idea: the microscopic world gives meaning to the macroscopic one. It is built for forensic pathologists and pathologists in practice, but above all for medical students as an educational companion to histology and pathology — and for every enthusiast for whom a look through the eyepiece opens an understanding of the whole. Teach your own AI model to recognize histology slides — right on your iPhone, iPad or Mac, offline, without a single pixel leaving your device. TRAIN YOUR OWN MODELS • H&E slide dataset covering 27 organs and tissues — from brain, lung and heart to thyroid, breast and colon • Colleague slide review with an audit trail — majority vote sets the final diagnosis • On-device Core ML training: held-out test set, confusion matrix, per-class sensitivity and precision • Mark the finding with a crop; magnifications from 4× to 1000× and a stain field (H&E, PAS, trichrome, silver, elastic…) • Learning from corrections: fix a wrong result and add the image to your dataset in one tap — the next training round learns from it • Portable .le3AI models and .le3set slide datasets with metrics and audit trail — encrypted, shared via AirDrop or mail CASE WORKFLOW • Scanner slides attached to a case number, viewing with zoom • Case history, autopsy conclusion and toxicology as inputs • Tissue check: the organ-recognition model flags a possible slide mix-up • Concise summary with a "slides only" and a "with case data" conclusion — one tap to clipboard (Word) • Archiving and full backup to an external drive KNOWLEDGE BASE • Atlas of over 100 diagnoses across 27 organs with microscopic descriptions, key features and suggested conclusions • Image evaluation with a preliminary diagnosis and report draft; a field-of-view crop matches the image scale to the training • Education module: "what next after H&E" guide, special stains (Van Gieson, trichrome, reticulin, Perls…), IHC markers (CD68, fibronectin, β-APP, C5b-9…), cellular elements and pigments • Descriptions draw on referenced literature (Robbins & Cotran, Knight, Bancroft, Dabbs) — the source list is in the app • Fully bilingual SK/EN, professional and lay terminology FREE — AND WHY littlEye AI is free and fully functional. No subscription, no feature locked behind a payment, no advertising. That was a deliberate decision. The people this application was built for — medical students and junior colleagues — are the same people least able to afford it. And the point of the project is that departments exchange trained models with one another; that needs people, not revenue. Inside the app you will find a voluntary contribution. It unlocks nothing and is a condition of nothing. If the app helps you in your work, you may contribute. If not, carry on using it with a clear conscience. ABOUT THE PROJECT littlEye AI is developed in collaboration by specialists from three departments of the Faculty of Medicine, Comenius University in Bratislava: the Institute of Histology and Embryology, the Institute of Pathological Anatomy, and the Institute of Forensic Medicine and Forensic Toxicology. The content is developed by specialists from these institutes, building on the experience from the apps 4n6 Tools and Colorime3ka. WHAT THE APP CONTAINS The app is a tool — it ships with a single sample model trained on synthetic patterns. It contains no real slides and no ready-made diagnostic models. You create .le3AI models yourself by training on your own images, or exchange them with colleagues as separate files. PRIVACY The app makes no network connections. Images, cases and models remain exclusively on your device. The transfer files .le3AI and .le3set are encrypted — only littlEye AI can open them. littlEye AI is a research and decision-support tool for qualified professionals. It is not a medical device and does not provide diagnoses — the final assessment always belongs to the pathologist or forensic physician. ## Version history (last 5 releases) ### 3.4 — 2026-09-26 Quantification methodology to the standard of the scientific community — what so far needed ImageJ or QuPath is now in the app: • Automatic threshold (Otsu): the optical-density histogram of the tissue sets the threshold, not the slider — rater-independent; the value used is in the result and in the Methods paragraph. • Distribution across fields: a scan or large photograph of the section is divided into a grid of fields, each measured with the same recipe and a fixed threshold (systematic sampling). Mean ± SD, coefficient of variation, range and a map (red = low) — for desmin it decides between focal and diffuse loss. Also in the DAB + H&E image pair. • Point counting — stereological check: a grid of ~250 points with a random offset, reclassify points by tapping; the point fraction with a Wilson 95 % CI and a comparison with the pixel value. The reference method against which the automatic figure is verified. • References extended with methods and statistics (Otsu, IHC Profiler, QuPath, Macenko, Gundersen & Jensen, Wilson, Taylor & Levenson, Rimm, Shrout & Fleiss, Koo & Li, Bland & Altman) — on every recipe. The Methods paragraph, the log and the CSV include the new values. Collaboration and validation: a new sheet with a ready multi-centre validation-study proposal (copyable into an e-mail or grant application), the collaboration offer and project support — from the collaboration card and the Help menu. Manual 3.4 (SK/EN). 105 automated tests. ### 3.3 — 2026-09-23 Desmin IHC: the pair of images of the same block (DAB + H&E) now sits directly on the screen — load a scan or microphotograph of the DAB-stained section and a photo of the same block stained with H&E, and you see the CPA, the myocyte area and Index 2 at once. The app checks whether the pair is comparable (format, size, tissue share, nucleus size). Tools for publication — what a reviewer asks about first: • Image quality control: sharpness (the Crete 2007 blur metric), tissue share, clipped highlights and shadows, white point and colour cast — every image gets a badge suitable / borderline / unsuitable with the reason. • Threshold sensitivity analysis: scans the whole slider range, draws the curve, shows the plateau and states whether the value is robust to the threshold; for Index 2 both thresholds are combined into one range. • Stain vectors from your own slide (Macenko 2009) instead of the standard ones, with the deviation from the standard in degrees and anchored units. • Methods paragraph: ready Materials-and-Methods text (SK/EN) with the version, citations, optical-density thresholds, scale, controls and a settings checksum — the same checksum means the same setting. • Log: ICC(2,1) with a confidence interval and a Bland–Altman plot between and within raters; staining batches with a control slide, drift and a normalisation factor; CSV with the checksum and the batch. New manual 3.3 (SK/EN). 97 automated tests including calibration on real myocardium images. ### 3.1 — 2026-09-19 Training without a manual. A dataset readiness traffic light tells you how many images each class is missing — and where to get them; three training profiles (Quick, Standard, Thorough) instead of parameters; the model name fills itself in. After training, one sentence about quality: usable for triage, indicative, unsure about a specific class, or overfitted — with “What to add to improve it” and “Try on an image”. The app reminds you to retrain when new images arrive. Import a model trained outside the app (Core ML .mlpackage / .mlmodel) — with mandatory data provenance and an in-app test on your own images. Mac: Esc closes sheets, resizable Settings, “Open in a separate window” on right-click, training progress in the Dock. Images can also be dropped into Classify image. Fixes: validation no longer shows 0 % on small datasets; bulk image import runs in the background with a counter; two missing links in the Autolysis guide; a false thank-you on launch. New manual 3.1 (SK/EN) in the Help menu and on the website. ### 3.0 — 2026-09-12 From this version littlEye AI is free. No subscription, no feature behind a payment. The app was built for medical students and practising physicians, and the price stood in the way of exactly those people. Inside you will find a voluntary contribution that unlocks nothing. New on Mac: • Menu bar commands with shortcuts — import ⌘O, section switching ⌘1 to ⌘7 • Dropping a .le3AI or .le3set file onto the window imports it • Images can be dragged straight into an organ, on iPad too • References & sources: every diagnosis, stain and IHC marker now carries citations with links to the source ### 2.2.3 — 2026-09-08 We have clarified what the app is and the information about models. littlEye AI is a tool for training your own models. It ships with a single sample model trained on synthetic patterns — real diagnostic models are ones you train on your own slides, or exchange with colleagues as separate .le3AI files. They never have been and never will be part of the app. We no longer offer models derived from material under a non-commercial licence, and no longer list them in the app. The licence-clean colorectal cancer model (NCT-CRC dataset, CC BY 4.0) remains available free on request at forensika.eu@icloud.com. No functional changes. ## More apps by Jan Sikuta - [Breathune](https://appshunter.io/ios/app/breathune/id6782549502) - [4n6 Tools Lite](https://appshunter.io/ios/app/4n6-tools-lite/id6783425913) - [4n6 Sikuta Tools](https://appshunter.io/ios/app/4n6-sikuta-tools/id6785259219) - [Mosquiter](https://appshunter.io/ios/app/mosquiter/id6786154086) - [Lite Breathune](https://appshunter.io/ios/app/lite-breathune/id6786838686) - [Colorime3ka](https://appshunter.io/ios/app/colorime3ka/id6793206733) All apps by Jan Sikuta: https://appshunter.io/developer/6782549504 --- *Data collected daily from the US App Store and indexed by [AppsHunter](https://appshunter.io/). User reviews are verbatim App Store reviews. Ratings, prices and chart positions refresh continuously; this snapshot is from 2026-09-26.*