diff --git a/.streamlit/config.toml b/.streamlit/config.toml
new file mode 100644
index 0000000..847402d
--- /dev/null
+++ b/.streamlit/config.toml
@@ -0,0 +1,10 @@
+[server]
+# WebSocketの圧縮を無効化（接続の安定性が向上し、不意な切断が減ります）
+# 長時間操作しないと、ネットワーク機器やブラウザが「この通信はもう終わったのかな？」と勘違いして、勝手に切断してしまう
+# 圧縮をやめることで、通信が単純かつ頻繁になり、「まだ繋がってますよ！」というアピールが強くなるため、勝手に切断されにくくなる
+enableWebsocketCompression = false
+
+[browser]
+# 統計データの送信を停止（動作が少し軽くなります）
+# 研究用ツールとしては不要な通信であり、これが原因で動作が重くなったり不安定になったりするのを防ぐ
+gatherUsageStats = false
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index 4a15dc1..5330bd9 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,6 @@
 streamlit>=1.28.0
 pandas>=2.0.0
 Pillow>=10.0.0
+pytest
 openpyxl>=3.1.0
 watchdog>=3.0.0
diff --git a/start_mac.command b/start_mac.command
new file mode 100755
index 0000000..ed996b2
--- /dev/null
+++ b/start_mac.command
@@ -0,0 +1,16 @@
+# chmod +x start_mac.commandの実装が必須
+
+#!/bin/bash
+
+# カレントディレクトリをこのファイルの場所に移動
+cd "$(dirname "$0")"
+
+echo "=========================================="
+echo " IVUS Annotation Tool (Mac/Linux)"
+echo "=========================================="
+
+# 仮想環境(venv)がある場合は有効化する（エラーは無視）
+source venv/bin/activate 2>/dev/null
+
+# アプリ起動
+streamlit run app.py
\ No newline at end of file
diff --git a/start_windows.bat b/start_windows.bat
new file mode 100644
index 0000000..d75f9bc
--- /dev/null
+++ b/start_windows.bat
@@ -0,0 +1,16 @@
+@echo off
+:: カレントディレクトリをこのファイルの場所に移動
+cd /d %~dp0
+
+echo ==========================================
+echo  IVUS Annotation Tool (Windows)
+echo ==========================================
+
+:: 仮想環境(venv)がある場合は有効化する（なければ無視して進む）
+if exist venv\Scripts\activate.bat call venv\Scripts\activate.bat
+
+:: アプリ起動
+streamlit run app.py
+
+:: エラーで落ちたときにすぐ閉じないようにする
+pause
\ No newline at end of file
diff --git a/tests/test_app_logic.py b/tests/test_app_logic.py
new file mode 100644
index 0000000..9d44b65
--- /dev/null
+++ b/tests/test_app_logic.py
@@ -0,0 +1,93 @@
+# -*- coding: utf-8 -*-
+"""
+Test suite for app navigation and annotation logic.
+Tests app behavior without running Streamlit.
+"""
+
+import pytest
+from types import SimpleNamespace
+
+
+# 簡易的なMockSessionState
+# SimpleNamespaceを使うと obj.attr = value のように書けるので便利
+class MockState(SimpleNamespace):
+    pass
+
+
+def test_navigation_next_unannotated():
+    """
+    【基本動作】保存後、次の未完了症例に進むか
+    """
+    # 状態の準備
+    state = MockState(
+        current_case_idx=0,
+        case_ids=[101, 102, 103],
+        annotated_cases=set()
+    )
+
+    # 1. 現在の症例(101)を保存したと仮定
+    current_id = state.case_ids[state.current_case_idx]
+    state.annotated_cases.add(current_id)
+
+    # --- アプリのロジック（簡易再現） ---
+    next_idx = state.current_case_idx + 1
+    found = False
+    while next_idx < len(state.case_ids):
+        if state.case_ids[next_idx] not in state.annotated_cases:
+            state.current_case_idx = next_idx
+            found = True
+            break
+        next_idx += 1
+    # ----------------------------------
+
+    # 検証: idxが1(102)に進んでいること
+    assert found is True
+    assert state.current_case_idx == 1
+    assert state.case_ids[state.current_case_idx] == 102
+
+
+def test_navigation_skip_annotated():
+    """
+    【スキップ動作】完了済み症例を飛ばして、その次の未完了に進むか
+    """
+    # 状態: 101(今), 102(完了済), 103(未完了)
+    state = MockState(
+        current_case_idx=0,
+        case_ids=[101, 102, 103],
+        annotated_cases={102}
+    )
+
+    # 1. 現在の症例(101)を保存
+    current_id = state.case_ids[state.current_case_idx]
+    state.annotated_cases.add(current_id)
+
+    # --- アプリのロジック ---
+    next_idx = state.current_case_idx + 1
+    found = False
+    while next_idx < len(state.case_ids):
+        if state.case_ids[next_idx] not in state.annotated_cases:
+            state.current_case_idx = next_idx
+            found = True
+            break
+        next_idx += 1
+    # ---------------------
+
+    # 検証: idxが2(103)まで飛んでいること
+    assert found is True
+    assert state.current_case_idx == 2
+    assert state.case_ids[state.current_case_idx] == 103
+
+
+def test_completion_logic():
+    """
+    【完了判定】全症例終わったら完了フラグが立つか
+    """
+    state = MockState(
+        case_ids=[101, 102],
+        annotated_cases={101, 102}
+    )
+
+    # 完了判定ロジック
+    is_complete = len(state.annotated_cases) >= len(state.case_ids)
+
+    assert is_complete is True
diff --git a/tests/test_utils.py b/tests/test_utils.py
new file mode 100644
index 0000000..0f0a500
--- /dev/null
+++ b/tests/test_utils.py
@@ -0,0 +1,388 @@
+# -*- coding: utf-8 -*-
+"""
+Test suite for annotation saving utilities.
+Tests data persistence, CSV encoding, and append behavior.
+"""
+
+import os
+import csv
+import tempfile
+import shutil
+import pytest
+import pandas as pd
+from pathlib import Path
+
+# Import functions from utils module
+import sys
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+from utils.annotation_saver import (
+    initialize_csv,
+    save_annotation,
+    get_annotated_cases
+)
+
+
+@pytest.fixture
+def setup_teardown():
+    """
+    Create a temporary directory for each test and clean up afterward.
+
+    Yields:
+        str: Path to temporary directory
+    """
+    # Create temporary directory
+    temp_dir = tempfile.mkdtemp()
+
+    yield temp_dir
+
+    # Cleanup: Remove temporary directory after test
+    shutil.rmtree(temp_dir)
+
+
+def test_new_file_creation(setup_teardown):
+    """
+    Test 1: CSV file creation with correct headers.
+
+    Verifies that:
+    - CSV file is created when it doesn't exist
+    - Header row is correctly written
+    - First data row is correctly saved
+    """
+    temp_dir = setup_teardown
+    csv_path = os.path.join(temp_dir, "annotations_test.csv")
+
+    # Verify file doesn't exist yet
+    assert not os.path.exists(csv_path), "CSV file should not exist before saving"
+
+    # Save first annotation
+    save_annotation(
+        csv_path=csv_path,
+        case_id=134,
+        prediction="あり",
+        confidence=75,
+        reasons=["石灰化プラークが多い", "減衰プラークが多い"],
+        comment="明確な所見あり",
+        annotator="田中",
+        ground_truth=True
+    )
+
+    # Verify file was created
+    assert os.path.exists(csv_path), "CSV file should exist after saving"
+
+    # Read and verify contents
+    df = pd.read_csv(csv_path, encoding='utf-8-sig')
+
+    # Check header columns
+    expected_columns = [
+        'timestamp', 'case_id', 'prediction', 'confidence',
+        'reasons', 'comment', 'annotator', 'ground_truth'
+    ]
+    assert list(df.columns) == expected_columns, "CSV headers don't match expected"
+
+    # Verify data row
+    assert len(df) == 1, "Should have exactly 1 data row"
+    assert df.loc[0, 'case_id'] == 134, "Case ID mismatch"
+    assert df.loc[0, 'prediction'] == "あり", "Prediction mismatch"
+    assert df.loc[0, 'confidence'] == 75, "Confidence mismatch"
+    assert df.loc[0, 'reasons'] == "石灰化プラークが多い; 減衰プラークが多い", "Reasons format incorrect"
+    assert df.loc[0, 'comment'] == "明確な所見あり", "Comment mismatch"
+    assert df.loc[0, 'annotator'] == "田中", "Annotator mismatch"
+    assert df.loc[0, 'ground_truth'] == True, "Ground truth mismatch"
+    assert 'timestamp' in df.columns and pd.notna(df.loc[0, 'timestamp']), "Timestamp missing"
+
+
+def test_append_mode(setup_teardown):
+    """
+    Test 2: Append functionality - ensure data is not overwritten.
+
+    Verifies that:
+    - Second annotation is appended (not overwriting first)
+    - Both rows exist in correct order
+    - No data loss occurs
+    """
+    temp_dir = setup_teardown
+    csv_path = os.path.join(temp_dir, "annotations_append_test.csv")
+
+    # Save first annotation
+    save_annotation(
+        csv_path=csv_path,
+        case_id=134,
+        prediction="あり",
+        confidence=75,
+        reasons=["石灰化プラークが多い"],
+        comment="First annotation",
+        annotator="田中",
+        ground_truth=True
+    )
+
+    # Save second annotation
+    save_annotation(
+        csv_path=csv_path,
+        case_id=135,
+        prediction="なし",
+        confidence=50,
+        reasons=["石灰化プラークが少ない"],
+        comment="Second annotation",
+        annotator="田中",
+        ground_truth=False
+    )
+
+    # Read and verify both rows exist
+    df = pd.read_csv(csv_path, encoding='utf-8-sig')
+
+    assert len(df) == 2, "Should have exactly 2 data rows after append"
+
+    # Verify first row (should not be overwritten)
+    assert df.loc[0, 'case_id'] == 134, "First row case ID should remain"
+    assert df.loc[0, 'prediction'] == "あり", "First row prediction should remain"
+    assert df.loc[0, 'comment'] == "First annotation", "First row comment should remain"
+
+    # Verify second row
+    assert df.loc[1, 'case_id'] == 135, "Second row case ID incorrect"
+    assert df.loc[1, 'prediction'] == "なし", "Second row prediction incorrect"
+    assert df.loc[1, 'comment'] == "Second annotation", "Second row comment incorrect"
+    assert df.loc[1, 'ground_truth'] == False, "Second row ground truth incorrect"
+
+
+def test_japanese_encoding(setup_teardown):
+    """
+    Test 3: UTF-8-sig encoding with Japanese characters.
+
+    Verifies that:
+    - Japanese text is saved without corruption
+    - utf-8-sig encoding prevents BOM issues in Excel
+    - Data can be read back correctly
+    """
+    temp_dir = setup_teardown
+    csv_path = os.path.join(temp_dir, "annotations_japanese_test.csv")
+
+    # Japanese test data
+    japanese_name = "田中"
+    japanese_reasons = ["石灰化プラークが多い", "減衰プラークが少ない"]
+    japanese_comment = "非常に明確な石灰化所見があり、合併症リスクが高いと判断しました。"
+
+    # Save annotation with Japanese text
+    save_annotation(
+        csv_path=csv_path,
+        case_id=200,
+        prediction="あり",
+        confidence=100,
+        reasons=japanese_reasons,
+        comment=japanese_comment,
+        annotator=japanese_name,
+        ground_truth=None  # Test None ground truth
+    )
+
+    # Read back using utf-8-sig encoding (same as save)
+    df = pd.read_csv(csv_path, encoding='utf-8-sig')
+
+    # Verify Japanese text is intact
+    assert df.loc[0, 'annotator'] == japanese_name, f"Japanese name corrupted: expected {japanese_name}, got {df.loc[0, 'annotator']}"
+    assert df.loc[0, 'prediction'] == "あり", "Japanese prediction corrupted"
+
+    # Verify reasons (semicolon-separated Japanese text)
+    expected_reasons = "; ".join(japanese_reasons)
+    assert df.loc[0, 'reasons'] == expected_reasons, f"Japanese reasons corrupted: expected {expected_reasons}, got {df.loc[0, 'reasons']}"
+
+    # Verify comment
+    assert df.loc[0, 'comment'] == japanese_comment, "Japanese comment corrupted"
+
+    # Verify ground_truth is empty or NaN for None
+    gt_val = df.loc[0, 'ground_truth']
+    assert pd.isna(gt_val) or gt_val == "", f"Ground truth should be empty or NaN for None, got {gt_val}"
+
+    # Additional check: verify file can be opened with standard csv reader
+    with open(csv_path, 'r', encoding='utf-8-sig') as f:
+        reader = csv.DictReader(f)
+        rows = list(reader)
+        assert len(rows) == 1, "Should have 1 row"
+        assert rows[0]['annotator'] == japanese_name, "CSV reader can't read Japanese correctly"
+
+
+def test_decimal_case_ids(setup_teardown):
+    """
+    Test 4: Handling decimal case IDs (e.g., 134.1, 134.2).
+
+    Verifies that:
+    - Decimal case IDs are stored correctly
+    - get_annotated_cases returns correct set of IDs
+    """
+    temp_dir = setup_teardown
+    csv_path = os.path.join(temp_dir, "annotations_decimal_test.csv")
+
+    # Save annotations with both integer and decimal case IDs
+    save_annotation(
+        csv_path=csv_path,
+        case_id=134,
+        prediction="あり",
+        confidence=75,
+        reasons=["石灰化プラークが多い"],
+        comment="Integer case ID",
+        annotator="田中",
+        ground_truth=True
+    )
+
+    save_annotation(
+        csv_path=csv_path,
+        case_id=134.1,
+        prediction="なし",
+        confidence=50,
+        reasons=["石灰化プラークが少ない"],
+        comment="Decimal case ID 1",
+        annotator="田中",
+        ground_truth=False
+    )
+
+    save_annotation(
+        csv_path=csv_path,
+        case_id=134.2,
+        prediction="あり",
+        confidence=80,
+        reasons=["減衰プラークが多い"],
+        comment="Decimal case ID 2",
+        annotator="田中",
+        ground_truth=True
+    )
+
+    # Verify all case IDs are correctly stored
+    df = pd.read_csv(csv_path, encoding='utf-8-sig')
+    assert len(df) == 3, "Should have 3 rows"
+
+    # Check case IDs
+    case_ids = df['case_id'].tolist()
+    assert 134 in case_ids or 134.0 in case_ids, "Integer case ID 134 not found"
+    assert 134.1 in case_ids, "Decimal case ID 134.1 not found"
+    assert 134.2 in case_ids, "Decimal case ID 134.2 not found"
+
+
+def test_get_annotated_cases(setup_teardown):
+    """
+    Test 5: get_annotated_cases function.
+
+    Verifies that:
+    - Function returns correct set of annotated case IDs
+    - Returns empty set for non-existent file
+    """
+    temp_dir = setup_teardown
+    csv_path = os.path.join(temp_dir, "annotations_test.csv")
+
+    # Save annotations
+    save_annotation(
+        csv_path=csv_path,
+        case_id=134,
+        prediction="あり",
+        confidence=75,
+        reasons=["石灰化プラークが多い"],
+        comment="Case 134",
+        annotator="田中",
+        ground_truth=True
+    )
+
+    save_annotation(
+        csv_path=csv_path,
+        case_id=135,
+        prediction="なし",
+        confidence=50,
+        reasons=["石灰化プラークが少ない"],
+        comment="Case 135",
+        annotator="田中",
+        ground_truth=False
+    )
+
+    save_annotation(
+        csv_path=csv_path,
+        case_id=136,
+        prediction="あり",
+        confidence=80,
+        reasons=["減衰プラークが多い"],
+        comment="Case 136",
+        annotator="田中",
+        ground_truth=True
+    )
+
+    # Test: Get all annotated cases
+    all_cases = get_annotated_cases(csv_path)
+    assert len(all_cases) == 3, f"Should have 3 unique cases, got {len(all_cases)}"
+    assert 134 in all_cases, "Case 134 should be in annotated cases"
+    assert 135 in all_cases, "Case 135 should be in annotated cases"
+    assert 136 in all_cases, "Case 136 should be in annotated cases"
+
+    # Test: Filter by annotator
+    tanaka_cases = get_annotated_cases(csv_path, annotator="田中")
+    assert len(tanaka_cases) == 3, f"田中 should have 3 cases, got {len(tanaka_cases)}"
+
+    # Test: Non-existent file returns empty set
+    non_existent_path = os.path.join(temp_dir, "non_existent.csv")
+    empty_cases = get_annotated_cases(non_existent_path)
+    assert len(empty_cases) == 0, "Non-existent file should return empty set"
+    assert isinstance(empty_cases, set), "Should return a set type"
+
+
+def test_initialize_csv_idempotent(setup_teardown):
+    """
+    Test 6: initialize_csv is idempotent (safe to call multiple times).
+
+    Verifies that:
+    - Calling initialize_csv multiple times doesn't corrupt file
+    - Existing data is preserved
+    """
+    temp_dir = setup_teardown
+    csv_path = os.path.join(temp_dir, "annotations_idempotent_test.csv")
+
+    # First initialization
+    initialize_csv(csv_path)
+    assert os.path.exists(csv_path), "File should be created"
+
+    # Add some data
+    save_annotation(
+        csv_path=csv_path,
+        case_id=100,
+        prediction="あり",
+        confidence=75,
+        reasons=["石灰化プラークが多い"],
+        comment="Test data",
+        annotator="田中",
+        ground_truth=True
+    )
+
+    # Call initialize_csv again (should not corrupt file)
+    initialize_csv(csv_path)
+
+    # Verify data is still intact
+    df = pd.read_csv(csv_path, encoding='utf-8-sig')
+    assert len(df) == 1, "Data should be preserved after re-initialization"
+    assert df.loc[0, 'case_id'] == 100, "Case ID should be preserved"
+    assert df.loc[0, 'annotator'] == "田中", "Annotator should be preserved"
+
+
+def test_empty_reasons_list(setup_teardown):
+    """
+    Test 7: Handling empty reasons list.
+
+    Verifies that:
+    - Empty reasons list is handled gracefully
+    - Results in empty string in CSV
+    """
+    temp_dir = setup_teardown
+    csv_path = os.path.join(temp_dir, "annotations_empty_reasons.csv")
+
+    # Save annotation with empty reasons
+    save_annotation(
+        csv_path=csv_path,
+        case_id=200,
+        prediction="なし",
+        confidence=50,
+        reasons=[],  # Empty list
+        comment="No specific reasons",
+        annotator="田中",
+        ground_truth=False
+    )
+
+    # Read and verify
+    df = pd.read_csv(csv_path, encoding='utf-8-sig')
+    assert len(df) == 1, "Should have 1 row"
+
+    # Empty reasons should result in empty string
+    reasons_value = df.loc[0, 'reasons']
+    assert reasons_value == "" or pd.isna(reasons_value), f"Empty reasons should be empty string, got: {reasons_value}"